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DIBASHI Azuka Anthony PG/Ph.D/10/54604

CAPACITY PLANNING AND PERFORMANCE IN THE

NIGERIAN BREWING INDUSTRY IN SOUTHEASTERN

NIGERIA

FACULTY OF BUSINESS ADMINISTRATION

DEPARTMENT OF MANAGEMENT

Ebere Omeje Digitally Signed by

Name

DN

O= University of Nigeri

OU = Innovation Centre


CAPACITY PLANNING AND PERFORMANCE IN THE

NIGERIAN BREWING INDUSTRY IN SOUTHEASTERN

NIGERIA

OF BUSINESS ADMINISTRATION

DEPARTMENT OF MANAGEMENT

Digitally Signed by: Content manager’s

Name

DN : CN = Webmaster’s name

O= University of Nigeria, Nsukka

OU = Innovation Centre

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CAPACITY PLANNING AND PERFORMANCE IN THE NIGERIAN

BREWING INDUSTRY IN SOUTHEASTERN NIGERIA


DEPARTMENT OF MANAGEMENT, FACULTY OF BUSINESS ADMINISTRATION,

UNIVERSITY OF NIGERIA, ENUGU CAMPUS

ENUGU

JULY, 2014

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CAPACITY PLANNING AND PERFORMANCE IN THE NIGERIAN BREWING INDUSTRY IN SOUTHEASTERN NIGERIA


SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR

THE AWARD OF DOCTOR OF PHILOSOPHY (Ph.D) IN MANAGEM ENT

DEPARTMENT OF MANAGEMENT,

FACULTY OF BUSINESS ADMINISTRATION,


ENUGU.

SUPERVISOR: PROF. U.J.F. EWURUM

JULY, 2014

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DECLARATION

I, DIBASHI Azuka Anthony, hereby declare that I have satisfactorily completed the requirement

for Ph.D. thesis. That the work embodied in this thesis is original and has not been submitted in

part or full for any other Diploma or Degree of this University or any other University.

…………………….……. DIBASHI Azuka Anthony

(PG/Ph.D/10/54604)

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APPROVAL

We the undersigned certified that this thesis is adequate in scope and quality for the award of

Ph.D in Management.

___________________ _____________ PROF. U.J.F. EWURUM DATE Supervisor ____________________ _______________ DR. V.A. ONODUGO DATE Head of Department

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DEDICATION

This study is dedicated to the Almighty God, whose inspiration guided the work.

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ACKNOWLEDGEMENTS

I remain forever grateful to the Almighty God for leading me to the successful completion of this

work. I wish to express my gratitude to Prof. U.J.F. Ewurum my supervisor for his guidance,

unrelenting advice, encouragement, constructive criticism, and valuable suggestions, without

which this work would have been an uphill and more demanding task. His combined wealth of

experience, foresightedness, magnanimity, generosity, academic prowess and moral probity,

which he always displayed, merit commendation. I also appreciate the Head of Department

(Department of Management) Dr. V.A. Onodugo for his patience, suggestions and fatherly

qualities.

I must also extend my heartfelt and warm gratitude to our dignified, amiable, and pragmatic

Dean of the Faculty, Prof G. Ugwuonah for her exemplary and charismatic leadership. I am also

grateful to Dr. E.K. Agbaeze, Dr. O.C. Ugbam, Dr. (Mrs.) Ann Ogbo (LSM), Rev. Fr. Dr. Tony

Igwe, Dr. Ben Chukwu and a host of other academic staff of the Department who has been

exceptionally wonderful; To Mrs. N. Ofodile and Mrs. Ijeomanta. I am highly indepted, may

God Almighty highly bless and reward you all.

I also remain loyal to Senator Dr. (Chief) Ify Okowa, senator representing Delta North senatorial

zone at the National Assembly Abuja for all his support, advice and encouragement. I am also

grateful to my wife Mrs. Josephine Dibashi (LSM) and my sons Azuka, Anene, Chude, and

Joseph Dibashi for their love and understanding and being there for me.

I am also grateful to Miss Augusta Adaghegbe, my driver Osakwe .O. John, Mrs. Peace Ottah

and Mrs Ngozi Ofodile for making this work a success. I am grateful to the Management and

Staff of Guinness Nigeria Plc, Benin City, Nigerian Breweries Plc, Ninth Mile Enugu,

Continental Breweries Plc, Awoomama and Premier Breweries Plc, Onitsha for their support and

Cooperation during the field work.

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TABLE OF CONTENTS

Pages

Declaration………………………………………………………………………………… iii

Approval……………………………………………………..…….………………… iv

Dedication………………………………………………………………………………… v

Acknowledgements…………………………………………..…………………………. vi

Table of Contents………………………………………………..…………….………… vii

List of Tables………………………………………………………….….…………….. x

List of Figures…………………………………………………………………………… xi

Abstract…………………………………………………………………………………. xii

CHAPTER ONE:

1.1 Background of the Study………………………………………………………… 1

1.2 Statement of the Problem…………………………………………………… 5

1.3 Objectives of the Study……………………………………………………….. 5

1.4 Research Questions……………………………………………………………. 6

1.5 Research Hypotheses…………………………..……………………………….. 6

1.6 Significance of the Study………………………………………………………. 7

1.7 Scope of the Study…………………………………………………………….. 7

1.8 Limitations of the Study……………………………………………………… 8

1.9 Profiles of the Brewing Firms studied……………………….………...……. 8

1.10 Operational Definition of Terms………………………………………………….. 13

References……………………………………………………………………… 15

CHAPTER TWO: LITERATURE REVIEW

2.1 Literature……………………………………………………………………… 16

2.2 Conceptual Framework……………………………………………………….. 17

2.3 Theoretical Framework……………………………………………………….. 37

2.4 Empirical Review……………………………………………………………… 67

2.5 Capacity Management and Planning………………………………………….. 69

2.6 Brewing………………………………………………………………………… 74

2.7 Summary of the Review of the Related Literature………………………………. 90

References…………………………………………………………………………. 93

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CHAPTER THREE: METHODOLOGY

3.1 Research Methods………………………………………………………………… 105

3.2 Research Design……………………………………………………………………105

3.3 Sources of Data Collection……………………………………………………. 105

3.4 Population of the Study………………………………………………………. 106

3.5 The Sample and Sampling Technique…………………………………………… 106

3.6 Description of Research Instruments……………………………………………… 107

3.7 Data Analysis Technique(s) ……………………………………………………… 107

3.8 Validity of Instrument…………………………………………………………… 109

3.9 Reliability of the Research Instrument……………………………………………. 109

References……………………………………………………………………… 110

CHAPTER FOUR: DATA PRESENTATION AND ANALYSIS

4.1 Data presentation and analysis……………………………………………. 111

4.2 Data Presentation………………………………………………………………... 111

4.3 Data Analysis………………………………………………………………....... 114

4.4 Analysis of the Relationship of the Contingency Theory and the Five Objectives 126

4.5 Discussion of Findings……………………………………………………….. 139

4.6 Discussion related to the Contingency Theory and Multi Period Capacity Problem 150

References………………………………………………………………........... 161

CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSION,

RECOMMENDATIONS, CONTRIBUTION TO KNOWLEDGE AND

SUGGESTIONS FOR FUTURE RESEARCH

5.1 Summary of Major Findings………………………………………………….. 167

5.2 Conclusion………………………………………………………………......... 167

5.3 Recommendations………………………………………………………………. 169

5.4 Contribution to Knowledge…………………………………………………….. 169

5.5 Suggestions for Future Research……………………………………………… 170

References………………………………………………………………............. 171

Bibliography………………………………………………………………....... 172

Appendix I(Questionnaire) ……………………….…………………………… 186

Appendix II(Oral Interview Schedule) ………………..………………………… 191

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Appendix III (Dichotomous Oral Interview Schedule on the Contingency Theory of

Capacity Planning)……………………………………………………………….. 192

Appendix IV (Dichotomous Oral Interview Schedule for Implementing the Capacity

Multi-Period Problem…………………………………………………………… 193

Appendix V (Calculation of Cronbach’s Alpha Co-efficient of Reliability)……. 194

Appendix VI (Results related to the Personal Data of the Respondents)………… 195

Appendix VII (Multi-Period Capacity Planning Problem)……………………….. 199

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LIST OF TABLES

PAGE

Table 4.1: The Presentation of the Response rate of the questionnaires administered……. 111

Table 4.2: The Summary of the Personal Data of the 740 Respondents…………………. 112

Table 4.3: The Presentation of the Responses on their Statuses and their experiential years 113

Table 4.4: The Analysis of the Responses related to the Five Objectives………………… 114

Table 4.5: The Analysis of the 12 steps towards developing a Capacity Plan ………..….. 116

Table 4.6: The Analysis of the Responses Opposite in Meaning to the Objectives……… 117

Table 4.7: The Analysis of the other Responses related to the First Four Objectives……. 118

Table 4.8: The Computation Details of the First Hypothesis………………………..…….. 122

Table 4.9: The Computation Details of the Second Hypothesis…………………………. 123

Table 4.10: The Computation Details of the Third Hypothesis…………………………… 124

Table 4.11: The Computation Details of the Fourth Hypothesis………………………….. 125

Table 4.12: The Computation Details of the Fifth Hypothesis…………………………….. 126

Table 4.13: The Analysis of the Responses to the Dichotomous Oral Interview Questions….. 127

Table 4.14: The Analysis of the Responses on the Relationship between the Multi-Period

Capacity Problem and the Five Objectives……………………………………. 129

Table 4.15: The Analysis of the Data on how Indigenous Capacity Building Theory

relates to the Five Objectives……………………………………………….. 131

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LIST OF FIGURE

PAGE

Figure 2.1: Systems Theory of Performance……………………………………………… 59

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ABSTRACT

The study investigated the influence of capacity planning on the performance of brewery firms in South Eastern States of Nigeria. The specific objectives of the study were to determine the extent to which capacity planning enhanced the level of performance in the brewing industry in South Eastern Nigeria. The study examined the nature of the relationship between capacity requirement planning and materials requirements planning, to ascertain the extent to which capacity planning sustains organizations competitive advantage, thereby determining the relationship between capacity planning and capacity building, determining the steps toward developing a capacity planning that affects the profitability in the brewing firms in the area studied. The research design adopted in the study was a combination of the survey, oral interview and model modifications. Hypothesis 1,3 and 5 were tested using Z test of population proportions and 2 and 4 using Spearman’s Rank Correlations revealed that capacity planning to a large extent enhanced the performance in the brewing industry in Southeastern Nigeria, that there was a significant capacity planning to a large extent (p< 0.05) enhanced the performance in the brewery industry in the area studied. Capacity requirement had positive relationship (p< 0.05)with materials requirements planning. Capacity planning to large extent (p< 0.05) sustained the organizations competitive position. There was significant positive relationship (p< 0.05) between capacity planning and capacity building. The steps of the capacity plan positively (p< 0.05) affected profitability. Positive relationship between capacity requirements planning and materials requirements planning, that capacity planning to a large extent sustained the organizational competitive advantage, that there is a positive relationship between capacity planning and capacity building, that the 12 steps of the capacity plans were developed that positively affected the profitability in the brewing industry in the area studied. In conclusion, the finding that capacity planning enhanced the performance in the brewing industry in Southeastern Nigeria implied that it made the brewing companies studied to achieve their organizational goals and objectives. The finding that there was a significant positive relationship between capacity requirements planning and materials requirements planning implied that there was a positive correlation between them. This means that materials requirements planning which was a method of coordinating the detailed production plans could lead to an enhancement of capacity requirements planning which meant taking future decisions on the items needed for the production capability of the brewing facility.

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CAPACITY PLANNING AND PERFORMANCE IN THE NIGERIAN

BREWING INDUSTRY IN SOUTHEASTERN NIGERIA


DEPARTMENT OF MANAGEMENT, FACULTY OF BUSINESS ADMINISTRATION,


ENUGU

JULY, 2014

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CHAPTER ONE

1.1 BACKGROUND TO THE STUDY

Capacity Planning has enhanced the performance of the brewing industry in Nigeria right from

1946 when Nigerian Breweries Limited set up the First Brewery in Nigeria (Nigerian Breweries

PLC, 2011). The direction has been to increase the number of breweries. Guinness Nigeria Plc

set up a Brewery in Lagos in 1963. The magnitude is now five breweries located at Ikeja, Ogba,

Benin, Jos and Aba (Guinness Nigeria Plc; 2011). Capacity has continued to be the production

capability of a facility in terms of the inputs, throughput and outputs.

In 1989, the Federal Government policy of using local inputs such as sorghum and corn instead

of malled barley negatively affected a lot of the breweries. Both Nigerian Breweries Plc and

Guinness Nigeria Plc depended on the assistance of the Parent Companies. The Brewing Industry

in Nigeria have relied on capacity planning for meeting the increased demand for beer, stout and

malt products through Demand Forecasting and Capacity Requirements Planning (Guinness

Nigeria Plc, 2011; Nigerian Breweries Plc, 2011).

Capacity building has followed capacity planning in the creation of the enabling environment

with appropriate policy and legal frameworks, institutional development including community

development (of women in particular). Human Resource Development and strengthening of

managerial systems, adding that, UNDP recognizes that capacity building is a long-term,

continuing process, in which all stakeholders participate (ministries, local authorities, non-

governmental organizations and water user groups, professional associations, academics and

other (citation: UNDP). Capacity building is very necessary for capacity planning. Planning is

deciding in advance what is to be done, when, where, how and whom it is to be done. In that it

bridges the gap from where we are to where we want to go in any business building and

performance. It is continuous, periodic managerial activities and reduces uncertainty. Capacity

is the production capability of a facility and it is measured in terms of inputs, throughput and

outputs. Manufacturing is that aspect of industry in which products, waste products and services

are produced (UNDP, 2012).

By 1992, capacity building became a central concept in Agenda 21 and in other United Nations

Conference on Environmental and Development (UNCED) Agreements. By 1998, the UN

General Assembly had commissioned and received evaluations of the impact of the UN system’s

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support for capacity building. These evaluations were carried out by the UN Department of

Economic and Social Affairs as part of the United Nations GATT Agreement’s (UNGA’)

triennial policy review during which it looks at all UN system development activities (UN

Publications Section). Since then, the issue of capacity building has become a major priority

within the global conventions, the Global Environmental Facility (GEF) and the International

Communities.

In the year 2000, UNDP through its Strategic Partnership with the GEF Secretariat, launched the

Capacity Development Initiative (CDI), a consultative process involving extensive outreach and

dialogue to identify countries’ priorities issues in capacity development needs, and based on

these findings, to develop a strategy and action plan that addresses identified needs to meet the

challenges of global environmental action.

In 2002, the World Summit in Sustainable Development (WSSD) and the Second GEF Assembly

reaffirmed the priority of building the capacity of development countries. The WSSD

recommended that GEF resources be used to provide financial resources to developing countries

to meet their capacity needs for training, technical knowhow and strengthening national

institutions.

Capacity Building is, however, not limited to international aid work. More recently, the term is

being used by governments to transform community and industry approaches to social and

environmental problems.

According to Skinner (1985), there are five periods of industrial history that stand out in

the development of manufacturing management:

1780 – 1950 Manufacturing leaders as technology capitalists.

1850 – 1890 Manufacturing leaders as architects of mass production.

1890 – 1920 Manufacturing management as movers in the organization.

1920 – 1960 Manufacturing management refines its skills in controlling and stabilizing.

1960 – 1980 Shaking the foundations of industrial management.

During the early years of the indusial revolution, production began to shift from low volume

activity to larger-scale operations. Although the scale of these early operations was large, the

machinery was not particularly complex and production operations were rigid. The management

of these operations remained essentially in the hands of top management with the aid of

overseers. Working conditions during this period were often abysmal.

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The major thrust of the Industrial Revolution took place in the (second 40-year) period from

1850 – 1890. During this period, the concepts of mass production and the assembly line were

born. Since coal could be efficiently transported, plants could be located in a larger variety of

locations. The plant foreman had enormous power and influence during this period.

According to Skinner(1985), the job of production manager actually came into being in the

period 1890 – 1920. Manufacturing processes became too complex to be handled by top

management personnel only. With this complexity came the need for scientific management

techniques. Frederick Taylor (often called the father of industrial engineering) is generally

credited with being the originator of the concept of scientific management. Most of the scientific

management techniques introduced around the turn of the century involved merely breaking a

task down into its various components. These techniques are probably less scientific than just

orderly. With the new levels of complexity, the single plant foreman could no longer coordinate

the demands of producing a varied product line and changing production schedules.

The enormous worldwide depression that took place n the 1930s notwithstanding, in many ways

the period 1920 – 1960 can be considered a golden age for the development of industry in the

United States. By 1960, the United States was the preeminent economic power in the world.

With the growth of the labour movement, working conditions had improved enormously. True

scientific methods started finding their way into the factory. Mathematical models for learning,

inventory control, quality control, production scheduling, and project management gained

acceptance by the user community. Top management often came through the ranks of production

professionals during this period.

Since 1960, many American companies have relinquished their domination of certain markets.

Products that were traditionally produced in the Untied States are now imported from Germany,

Japan, and the Far East. Many products are produced more cheaply and with higher quality

overseas. Furthermore, management-employee relations are often better in foreign companies.

Quality circles, introduced in Japan, allowed employees to input opinions about product

development and production procedures. Far more sophisticated scientific production methods

have been adopted in Japan than in other countries. For example, there are many more robots and

modern flexible manufacturing systems in Japan than in the United States (Skinner, 1985).

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Over the past five years, a broad conceptual framework has emerged. This approach is

increasingly being adopted by the development cooperation community. It involves a System

Perspective that addresses various levels of environmental management capacities (i.e. capacities

of institutions, individuals, overall countries and regions) (Vallejo, 2006). This approach lays

greater emphasis on the Capacity Development Process itself, on local ownership of its process

and on equal partnership in its support (Lafontaine, 200).Capacity Building involves human

resource development, the development of organizations and promoting the emergence of an

overall policy environment, conducive to the generation of appropriate responses to emerging

needs (UNDP/UNDOALOS, 1994).

The concept of capacity building includes the following issues.

Human resource development, the process of equipping individuals with the understanding,

skills and access to information, knowledge and training that enables them to perform

effectively.Organizational development, the elaboration of management structures, processes and

procedures, not only within organizations but also the management of relationships between the

different organizations and sectors (public, private and community).Institutional and legal

framework development, making legal and regulatory changes to enable organizations,

institutions and agencies at all levels and in all sectors to enhance their capacities.The levels of

capacity building are that:The Individual: refers to the process of changing attitudes and

behaviours-imparting knowledge and developing skills while maximizing the benefits of

participation, knowledge exchange and ownership.The Institution: focuses on the overall

organizational performance and functioning capabilities, as well as the ability of an organization

to adapt to change.

The System: emphasizes the overall policy framework in which individuals and organizations

operate and interact with the external environment (Lafontaine, 2000).

1.2 STATEMENT OF THE PROBLEM

There is difficulty in determining the extent to which capacity planning enhanced performance in

the brewing firms in Southeastern Nigeria from the inception of brewing industry in Nigeria in

1946 to date. Capacity has consistently and continuously determined operational capabilities of

decision, focasting changes in demand attitudes, skill and aids proximity to market future time

services needed and work load leading to various shortage or lack of stock in production process

which involves complex measures in terms of input, through put and output. This problem leads

to other challenges in ascertaining the relationship between capacity requirementsplanning and

material requirements planning and the extent to which capacity planning sustains organizations’

competitive advantage.

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Determining the extent of the relationship between capacity planning and capacity building and

the steps towards developing a capacity plan to improve the profitability in the brewing sector

have also produced several challenges and these lead to lack of gateways. It is these challenges

that are to be addressed in this study.

The capacity planning problem of a brewing firm would make it have less output in the form of

Lager beer, Stout and malt than is demanded by the present and potential customers. One of the

numerous ways of solving this problem is to build a new brewery firm. This is a long term

decision that will raise new issues of plant location, plant layout, selection and design of the

product, selection of equipments and processes, production design of items processed, and job

design. If these issues are not properly handled, performance will be negatively affected. This is

why the topic on capacity planning and performance in the Nigerian brewing industry in the

Southeastern States of Nigeria is apt.

1.3 OBJECTIVES OF THE STUDY

The thrust of the study is the effect of capacity planning on performance in the Nigerian brewing

industry in Southeastern Nigeria.

The specific objectives of the study are as follows:

1) To evaluate capacity planningand performance in thebrewing industry in Southeastern

Nigeria.

2) To assess capacity requirements planning and materials requirements planning.

3) To ascertain the extent to which capacity planning sustains organisations’ competitive

advantage.

4) To evaluate the relationship between capacity planning and capacity building.

5) To assess the steps toward developing a capacity plan and the profitability in the brewing

firms in the area studied.

1.4 RESEARCH QUESTIONS

This research is designed to provide answers to the following questions:

i. To what extent does capacity planning enhances performance in the brewing industry in

South Eastern Nigeria?

ii. What is the nature of the relationship between capacity requirements planning and

material requirements planning?

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iii. What is the extent to which capacity planning sustains organisations’ competitive

advantage?

iv. What is the extent of the relationship between capacity planning and capacity building?

v. How do we assess the steps that could be used to develop capacity planning that would

affect profitability in the brewing industry in the area to be studied?

1.5 RESEARCH HYPOTHESES

Five research hypotheses have been formulated to guide the study. They are as follows:

(i): Capacity planning to a large extent does not enhances performance in the brewing

industry in South Eastern Nigeria.

(ii): There is no significant relationship between capacity requirements planning and material

requirements planning.

(iii): Capacity planning to a large extent does not sustain organizations’ competitive

advantage.

(iv): There is no positive significant relationship between capacity planning and capacity

building.

(v): The steps towards developing capacity plan that would not affect profitability in the

brewing industry in South Eastern Nigeria are of the same order of magnitude.

1.6 SIGNIFICANCE OF THE STUDY

This study will be of immence significance to the Shareholders, Board members, Manager and

Stakeholders of brewing firms. It will also benefit officers of government, the public at large and

future researchers in the following ways: Shareholders, Members, Managers, Stakeholder,

Officers of Government,The public and Researchers.

1.7 SCOPE AND DELIMITATION OF THE STUDY

The focus of the study is to determine the extent to which capacity planning enhances the

performance in the brewing industry in the South Eastern Nigeria. The brewing companies were

chosen across the major zones in south eastern Nigeria in terms of subject matter, methodology,

spatial and data that best fits the study.

The geographical scope is South Eastern Nigeria, and the time scope of the study is 2 years from

December 2010 to December 2012. The brewing firms studied are Nigeria Breweries Plc, Ninth

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Mile Enugu, Guinness Nigeria Plc, Aba (former Dubic Breweries Plc), Premier Breweries Plc

Onitsha and Continental Breweries Plc Awoomama.

1.8 LIMITATIONS OF THE STUDY

Attitude of Respondents: Privacy of information and attitude of respondents were alo being

constraints. Some of the respondents were reluctant at releasing the required information as a

result of prejudiced opinion conceived by the study.

The Survey Research Design: It had the limitation that some respondents were not willing to

give answers to the probes. This limitation is minimized by persuading the respondents to give

answers.

The oral interview: Ithad the limitation that the interviewing situation may change from one

situation to another especially if more than one field data collector is used. This limitation is

minimized by the Researcher doing most of the field work.

The Questionnaire Research instrument: It had the limitation that its structured nature

compelled some of the respondents to give answers that they do not fully endorse. This limitation

was minimized by also asking some open-ended questions in an oral interview schedule.

The oral interviewschedule: It had the limitation that the open-ended questions asked were

difficult to analyse. This limitation was minimized by also using relative frequencies as the

numbers given over the total number of research instruments returned.

1.9 PROFILES OF THE BREWING FIRMS STUDIED

Historical Development of Nigerian Breweries

Nigerian Breweries Plc (NBPLC) is the country’s pioneer factory. Incorporated in 1946, it

commenced production in 1949. It started as a joint venture between the United African

Company (UAC) International, UK and Heineken of Holland, Thus, at inception, it was 100 per

cent foreign owned. By the early 1950s, when it began operating fully, some indigenous traders

already involved with its products were invited to become shareholders. Under the indigenization

policy of the early 1970s the foreign shareholders were forced to sell a significant proportion of

their holdings. Today, the company is 60 per cent Nigerian owned and 40 per cent foreign

owned. The 60 per cent Nigerian stake is held by company employees and members of the

public, while the 40 per cent foreign ownership is split almost equally between CWA Holdings

Limited (for Unilever) and Heineken Brouwerijen BV (Nigerian Breweries Plc, 2011).

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The foreign partners now perform the role of technical advisers, with Unilever advising on

commercial aspects such as accounting, purchasing, marketing and personnel, while Heineken

does the same for technology. Organizationally, the company has four divisions: technical,

finance, marketing and personnel, each of which is headed by an executive director (Nigerian

Breweries Plc, 2011).

Performance and Development

At its inception in 1949, NBPLC had only Start Lager (Nigeria’s first) on the market, over the

years it has broadened its product range. Except for the period 1984-86, when sales volume

suffered an annual average decline of about 18 per cent, turnover growth in the company has

generally been accompanied by growth in profit and production volume. Thus, when normal

growth was restored in 1987, the 51 per cent and 83 per cent increases in turnover and operating

profit, respectively, for 1987 – 88 were accompanied by about 35 per cent volume growth.

Similarly, the turnover of about N1.7 billion recorded in 1991 was partially the result of 8 per

cent growth in sales volume. However, from all indications, product pricing has been the major

factor in the impressive growth in operating profits (Nigerian Breweries Plc, 2011).

The Table 1.1 presents indicators of the growth turned in the company. Apart from sales and

profit, both net total assets and the numbers of employees have enjoyed respectable growth.

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Table 1:1 Performance Indicators for NBPLC

1971 1975 1981 1985 1991

Turnover (Nm) 40.2 75.7 241.1 179.1 1,708.6

Pretax operating profit

(Nm)

6.1 12.4 38.5 41.6 422.5

Net assets (Nm) 11.9 29.1 103.6 161.9 1,248.5

Employees 1,270.0 2,243.0 n.a 3,998.0 4,297.0

Source: Nigerian Breweries Plc (2014). Annual Report and Statement of Account. Lagos:

Nigerian Breweries Limited.

The deteriorating results recorded by the company in 1984-86 reflected the foreign exchange

rationing policy of the period, which was necessitated by the severe balance of payments crisis of

the post-oil-boom era. The import licence allocation of the company could hardly satisfy one

third of its foreign exchange requirements. The government’s mandatory backward integration

policy in the mid-1980s saw the company establishing a 5,000 – hectare far, estimated to be

worth N30 million, in Niger State. The farm is highly mechanized and produces mainly maize,

rice and sorghum, with Soya beans and cowpeas as rotational crops. The main crops are used as

input replacements for barley malt. The changeover in input mix was assisted by the company’s

N2 million R&D facility, which was commissioned in June 1987 and plant conversion costing

about N100 million (NBL,PLC, 2011).

The company works with highly structured plans, with annual budgets of intentions translated

into explicit targets. The decision board sits towards the end of the year to deliberate on the

report of each divisional head. Annual budget estimates are made in the middle of the year while

decisions on annual plans are left fill the end of the year (NBL, PLC 2011).

The company has experienced remarkable changes in its technical capability. In 1949 it used to

take between 28 and 30 days to produce a bottle of beer but with technological improvement it

now takes about two weeks. The change in input content in the late 1980s also involved changes

in processing technology (NBL, PLC, 2011).

Different measures of productivity are used for the technical division and other divisions. In the

technical section, productivity is measured in terms of the efficiency of plant operation an also in

terms of capacity utilization. In other divisions, it is in terms of the accomplishment of assigned

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responsibility. The company is viewed as a leader in the national industry and in Africa it enjoys

a high rating, in term of both productivity and product quality (NBL, PLC, 2011).

NBPLC concentrates on the production of its beer and related products, leaving ancillary

services such as bottles, crown corks, labels, cartons and crates to be supplied by other local

manufacturers. In fact, Nigerian law precludes a brewer from producing such ancillary services.

Only the companies in the soft drinks industry appear to sponsor firms to produce such services.

Backward integration into farming was a special concession granted to the breweries in 1984

following the stringent foreign exchange control measures introduced in that year. It also uses

outside transport companies for 60 per cent of total distribution (NBL,PLC, 2011).

The company; cooperates with other producers in the industry in lending materials that are

urgently required were applicable and most needed. Under the umbrella of MAN, it cooperates

with competitors to discuss issues affecting the industry, e.g. adverse government policy. There

is no collusion with competition in marketing and no cooperation in technical services, probably

because most of the local brewers have foreign technical partners (NBL, PLC, 2011).

The prosperity of the company has been preserved by its efficient costing system, which seeks to

protect profit margins in a high-inflation setting by adjusting prices in response to changing costs

of production. Input costs rose to about 105 per cent in the period 1982 to June 1992 and selling

prices have risen to almost the same extent (NBL,PLC, 2011).

Historical Development of Guinness Nigeria Plc

In 1759, Mr. Arthur Guinness built his factory on a site whose area was four acres. The site was

not far from the western entrance to the city of Dublin in Ireland. Even though the gate is no

more, the factory which bears the name of the founder has increased in size to upwards of 66

acres and is one of the biggest breweries in the world (Guinness Nigeria Plc, 2011).

In 1936, the high demand for Guinness necessitated the establishment of a second factory. This

was located at Park Royal near London. In 1963, the third Guinness factory was opened at Ikeja,

Lagos, Nigeria. Quite unlike the situation in both Dublin and Park Royal, Guinness in Nigeria is

bottled in the factory and the Ikeja factory has the largest bottling hall of all Guinness breweries

all over the world (Guinness Nigeria Plc, 2011).

25

The world wide popularity of Guinness has made it possible to establish breweries in the

following countries:

1. Malaysia;

2. Cameroon;

3. Ghana; and

4. Jamaica (Guinness Nigeria Plc, 2011).

Guinness is also brewed under Guinness supervision in the following countries:

i. Kenya;

ii. Sierra Leone;

iii. Australia;

iv. Trinidad;

v. Canada;

vi. Mauritius;

vii. New Zealand;

viii. Seychelles;

ix. Liberia;

x. Thailand;

xi. Indonesia; and

xii. Venezuela (Guinness Nigeria Plc, 2011)

In 1959, Guinness went into the production of a larger brand of beer called Harp in Ireland and

soon expanded this market into many other countries including U.K. where it sells very widely.

Larger beer is now produced in Ireland, U.K., Malaysia, Cameroon and in the Benin Factory in

Nigeria (Guinness Nigeria Plc, 2011).

Historical Development of Premier Breweries Plc

Premier Breweries Plc was incorporated on 23rd January, 1976 with head office in Onitsha,

Anambra State. It was listed on the Nigerian Stock Exchange in 1988. Proshare reports that

Premier Breweries Plc declares 2008, 2009 and 2010 Audited Results with N42.225m million

loss in 2010. The Stock Exchange weekly reports that Premier Breweries Plc’s audited result for

the year ended 31st March 2008 shows Nil Turnover same as in 2007. Loss after tax stood at

N23.005 million compared with N224.21 million in 2007. Consequently, if previous year’s

losses are taken into account, the retained loss carried forward stood at N440.3 million compared

to N417.3 million in 2007 (Premier Breweries Plc, 2011).

26

Historical Development of Continental Breweries Plc

The company was established at Awoomama in 1980 in Imo State and very well located on the

Onitsha-Owerri Road. It is very close to such big towns like Oguta, Orlu, Owerri and Onitsha

and this gives it the advantage of proximity to a lot of traders travelling from Owerri to Onitsha.

Owerri is the State Capital of Imo State, Onitsha is the biggest commercial center in Southern

Nigeria. The location advantage of the factory is instrument to their product “33” lager beer to be

very popular and the patronage of the customers has led to the factory being a very big factory

with very modern factory facilities such as matching, cementing, match rating and bottling

facilities (Continental Breweries Plc, 2011).

1.10 OPERATIONAL DEFINITION OF TERMS

The key terms used in this study are defined as follows:

Capacity Planning is defined as the forecasting and decision making to determine the service

capability of the brewing firms and is the process of determining the production capacity

needed by an organization to meet changing demand for its product (North Caroline State

University). Capacity planning, is also the maximum amount of work an organization is

capable of completing in a given period due constraints such as quality problems, quantity,

delays, materials handling etc. Capacity planning is also used in business computing as

synonym for capacity management.

Performance is the extent to which the brewing firm achieves her organizational objectives. It

looked at operational, financial, and managerial and share angles. A performance

management system thus consists of the processes used to identify, encourage, encourage,

evaluate, improve, and reward employee performance. Armstrong & Baron (1998), define

performance as a strategic and integrated approach to delivering sustained successes to

organizations by improving the performance of the individual contributors. Since

organizations exist to achieve goals, the degree of success that individual employees have in

reaching their individual goals, therefore, becomes a critical state in the capacity planning

process.

Material requirements planning is a method for coordinating detailed production plans, It is a

multi-stage process which beings with a master schedule and works backward to determine

when and how much components will be needed. It gives the time for placing orders and

when the order is required considering the lead time.

27

Capacity requirements planning is a method that utilizes the time faced material plan

information produced by a material requirement system and includes the consideration of all

actual lot sizes as well as lead times for both open shop orders (schedule receipts) and orders

planned for future release (planned orders).

Profitability is that aspect of performance that is a measure of the difference between total

revenue and total cost, and if the difference is positive, it is said that there is profit, if

negative, it is said to be a loss.

(i) Steps are procedures for doing capacity planning.

(ii) Competitive advantage is the distinctive competence which makes a particular firm to

stand out among its competitors.

(iii) Capacity building is the creation of enabling environment with appropriate policy and

legal frameworks, institutional development, including community participation (of

women in particular), human resources development and strengthening of managerial

systems.

28

REFERENCES

Chase, R.B. and Aquilano, N.J. (2005), Production and Operations Management Homewood,

Illinois: Richard D. Irwin, Incorporated.

Continental Breweries Plc (2011). “Annual Report and Statement of Accounts”

www.continentalbreweiresplc.orgdownloaded 10th November, by 2 pm.

Guinness Nigeria Plc (2011). “Annual Report and Statement of Accounts”

www.guinnessbreweriesannualreport.orgdownloaded 10th November, by 4 pm.

Lafontaine, A. (2000). “Assessment of Capacity Development Efforts of Other Development

Cooperation Agencies.” Capacity Development Initiative, GEF-UNDP Strategic

Partnership, 1-20

Nigerian Breweries Plc (2011). “Annual Report and Statements of Accounts”

www.nigerianbreweriesplcannualreport.org downloaded by 10th November by 5 pm.

Premier Breweries Plc (2011). “Annual Report and Statement of Accounts”

www.bremierbreweriesannualreport.orgdownloaded 10th November, by 3 pm.

Skinner, W. (2004), “The Focus Factory”. Harvard Business Review, May – June. 113-121.

UNDP (2012).United Nations Development Programme. www.undp.org. downloaded 24

September by 3pm, 1-10

UNDP/UNDOALOS, (1994), “Reports on the Consultative Meeting on Training in Integrated

Management of Coastal and Marine Areas for Sustainable Development,” Sassari,

Sardinia, Italy, 21-23 June, 1993. United Nations Development Programme and Division

for Ocean Affairs, United Nations, New York, 1-20

Vallejo, S.M. (2006), Are we meeting the challenges for capacity building for managing ocean

and coasts? Balboa, Panama, November, 13-14.

29

CHAPTER TWO

LITERATURE REVIEW

Capacity Planning is the process of determining the production capacity needed by an

organization to meet changing demands for its products (North Caroline State University). In the

context of capacity planning, ‘design capacity’ is the maximum amount of work that an

organization is capable of completing in a given period, ‘effective capacity’ is the maximum

amount of work that an organization is capable of completing in a given period due to constraints

such as quality problems, delays, material handling, etc. Capacity planning is also used in

business computing as a synonym for Capacity Management.

Planning is necessary in all complex organizations. In the absence of planning, different work

units may pursue the possibly conflicting objectives of their own (Sheu and Wacker, 2001).

However, not all organizations are complex and thus heavy planning efforts are not always

necessary. In simple settings, where specialization, action variety, and task interdependence are

low, coordination can be achieved through rules and heuristics (Cyert and March, 1963).

Capacity planning in the literature has been applied to the manufacturing industry. The Research

Gap here is to determine the effect of capacity planning on the performance of the Brewing

Industry in South Eastern Nigeria. In manufacturing management, the planning-focused methods

have been developed around the concept of material requirements planning (MRP, Orlicky,

1975), while the methods that emphasize rule-based control and simplicity are founded on the

just-in-time (JIT) methodology (Ohno, 1988).

Performance factors include: efficiency, effectiveness, productivity, profitability, solvency,

leverage, activity and morale (Nwachukwu, 2004). Dictionary’s definition of efficiency as fitness

or power to accomplish or success in accomplishing the purpose intended, adequate power,

effectiveness, efficacy. Later on, it is pointed out that efficiency acquired a second meaning – the

ratio between input and output, between effort and results, expenditure and income, cost and the

resulting pleasure, this second meaning became current in Business and Economics, only since

the beginning of the 20th Century. Still later on, influenced by the scientific management

movement, efficiency was defined as the ratio of actual performance to the standard performance

(Bell, 2006).

The performance of the brewing industry was constrained by high cost of production which was

attributable mainly to substantial depreciation of the naira exchange rate. The resultant sharp rise

in cost of importation of raw materials, machinery and spare parts resulted in corresponding

30

sharp rise in the overall cost of production. Other factors that contributed to high cost of

production during the year were escalation in interest rates and sharp increases in tariffs on

public utilities, especially electricity. The sharp increase in production costs was translated into

higher product prices which tended to dampen demand for local manufactures resulting in high

inventory accumulation. Another factor that reduced the domestic demand for locally produced

goods was massive importation and smuggling of a wide range of foreign goods into the country

(CBN, 2002).

2.2 CONCEPTUAL FRAMEWORK

2.2.1 Capacity Planning


organization to meet changing demands for its products (North Caroline State University, 2006).

In the context of capacity planning, ‘design capacity’ is the maximum amount of work that an



such as quality problems, delays, material handling, etc. Capacity planning is also used in

business computing as a synonym for Capacity Management (Gunther, 2007).

Guinness Nigeria Plc (2011), defines capacity planning as the extent to which decisions are taken

and forecasting is made on the production capability of the facility for brewing stout and lager

beer and producing malt with the use of raw materials: malted barley, hops, yeast, and water.

Brewing in Guinness was done for the first time in 1759 by Mr. Arthur Guinness at St. James’s

gate in Dublin Ireland. Brewing had been done earlier outside Guinness.

A discrepancy between the capacity of an organization and the demands of its customers results

in inefficiency, either in under-utilized resources or unfulfilled customers. The goal of capacity

planning is to minimize this discrepancy. Demand for an organization’s capacity varies based on

changes in production output, such as increasing or decreasing the production quantity of an

existing product, or producing new products. Better utilization of existing capacity can be

accomplished through improvements in overall equipment effectiveness (OEE). Capacity can be

increased through introducing new techniques, equipment and materials, increasing the number

of workers or machines, increasing the number of shifts, or acquiring additional production

facilities (Gunther, 2007).

31

Capacity is calculated:

( ) ( ) ( ) ( )efficiency nutilizatioshifts ofnumber or workers machines ofnumber ×××

The broad classes of capacity planning are lead strategy, lag strategy, and match strategy.

• Lead strategy: is adding capacity in anticipation of an increase in demand. Lead strategy

is an aggressive strategy with the goal of luring customers away from the company’s

competitors. The possible disadvantage to this strategy is that it often results in excess

inventory, which is costly and often wasteful (Olhanger, 2003)

• Lag strategy: refers to adding capacity only after the organization is running at full

capacity or beyond due to increase in demand (North Olhanger, 2003). This is a more

conservative strategy. It decreases the risk of waste, but it may result in the loss of

possible customers.

• Match strategy: is adding capacity in small amounts in response to changing demand in

the market. This is a more moderate strategy.

Capacity planning is long-term decision that establishes a firms’ overall level of resources. It

extends over time horizon long enough to obtain resources. Capacity decisions affect the

production lead time, customer responsiveness, operating cost and company ability to compete.

Inadequate capacity planning can lead to the loss of the customer and business. Excess capacity

can drain the company’s resources and prevent investments into more lucrative ventures. The

question of when capacity should be increased and by how much is the critical decisions (Hill,

2006).

From a scheduling perspective, it is very easy to determine how much capacity (or time) will be

required to manufacture a quantity of parts. Simply multiply the Standard Cycle Time by the

Number of Parts and divide by the part or process.

If production is scheduled to produce 500 pieces of product A on a machine having a cycle time

of 30 seconds and the OEE for the process is 85%, then the time to produce the parts would be

calculated as follows:

(500 parts x 30 Seconds)/85% = 17647.1 seconds. The OEE index makes it easy to determine

whether we have ample capacity to run the required production. In this example, 4.2 hours at

standard versus 4.9 hours based on the OEE index (Lazowska, 1984).

32

Repeating this process for all the parts that run through a given machine, it is possible to

determine the total capacity required to run production. Considering new work for a piece of

equipment or machinery, knowing how much capacity is available to run the work will

eventually become part of the overall process. Typically, an annual forecast is used to determine

how many hours per year are required. It is also possible that seasonal influences exist within

your machine requirements, so perhaps a quarterly or even monthly capacity report is required.

The steps for capacity planning include:

1. Determine Service Level Requirements

The first step in the capacity planning process is to categorize the work done by systems

and to quantify users’ expectations for how that work gets done.

2. Analyze Current Capacity

Next, the current capacity of the system must be analyzed to determine how it is meeting

the needs of the users.

3. Planning for the Future

Finally, using forecasts of future business activity, future system requirements are

determined. Implementing the required changes in system configuration will ensure that

sufficient capacity will be available to maintain service levels, even as circumstances

change in the future (Blackstone, 2009).

Determining service levels is important in capacity planning. The overall process of establishing

service level requirements first demands an understanding of workloads.

Workloads from a capacity planning perspective, is a computer system processes workloads

(which supply the demand) and delivers services to users (Mckay and Wiers, 2004)

During the first step in the capacity planning process, these workloads must be defined and a

definition of satisfactory service must be created. A workload is a logical classification of work

performance on a computer system. For capacity planning purposes, it is useful to associate a

unit of work with a workload. This is a measurable quantity of work done, as opposed to the

amount of system resources required to accomplish that work.

To understand the difference, consider measuring the work done at a fast food restaurant. When

deciding on the unit of work, you might consider counting the number of customers served, the

weight of the food served, the number of sandwiches served, or the money taken in for the food

served. This is an opposed to the resources used to accomplish the work, i.e., the amount of

33

French fries, raw hamburgers or pickle slices used to produce the food served to customers

(Berry, Schmitt and Vollmann, 2002).

The next step is to establish a service level agreement. A service level agreement is an agreement

between the service provider and service consumer that defines acceptable service. The service

level agreements are often defined from the user’s perspective, typically in terms of response

time or throughput. Using workloads often aids in the process of developing service level

agreements, because workloads can be used to measure system performance in ways that makes

sense to clients/ushers (Berry et al, 2002).

Udochi (1999) worked on the adoption of Total Quality Management (TQM) in enhancing

capacity planning in Guinness Nigeria Plc had steps that were different from that of Blackstone

(2009). The steps of Udochi were as follows:

1. Determining the current capacity needs.

2. Determining the future capacity needs.

3. If step one is less than step two, then the company needs to take a decision on contracting out

capacity, outsourcing or use of shifts.

4. If step one is more than step two, then the company can just continue.

5. When the decisions are taken, there is need for implementation and control after involving

top management.Udochi found that total quality management was very useful by

encouraging continuous quality in enhancing capacity planning in Guinness Nigeria Plc.

Arisa (2007) worked on investment practices to enhance capacity planning in an industry: a case

study of Guinness Nigeria Plc. He observed that in 1963, the high demand for Guinness that

necessitated the establishment of a third Guinness Brewing at Ikeja was because there was the

investment practice of raising money through shares and bonds so as to go into the capacity

planning of building a third Guinness Brewing at Ikeja, Lagos. Quite unlike the situation in both

Dublin and Park Royal, Guinness in Nigeria is bottled in Ikeja Brewery and capacity is built by

having the largest bottling hall of all Guinness Breweries. After the marshing process of the raw

materials and the fermentation process the liquid that is produced is called wort which has to be

marturated. It is after the marturation of beer that it can bottled. Arisa (2007) found that the

investment practice which enabled having sufficient share capital and bond capital was necessary

for enhancing capacity planning in Guinness Nigeria Plc.

34

In the case of appointment scheduling application, service level requirement might be

established, regarding the number of requests that should be processed within a given period of

time, or it might be required that each request be processed within a certain time limit. These

possibilities are analogous to a fast food restaurant requiring that a certain number of customers

should be serviced per hour during the lunch rush, or that each customer should have to wait no

longer than three minutes to have his or her order filled (Berry et al, 2002).

Ideally, service level requirements are ultimately determined by business requirements.

Frequently, however, they are based on past experience. It is better to set service level

requirements to ensure that the business objective will be accomplished, but not surprisingly

people frequently resort to setting service level requirements like provide a response time at least

as good as is currently experienced, even after the business is ramp up.

There are several steps that should be performed during the analysis of capacity measurement

data.

a. First is comparing the measurements of any items referenced in service level agreements

with their objectives. This provides the basic indication of whether the system has

adequate capacity.

b. The next step is checking the usage of the various resources of the system (CPU,

memory, and I/O devices). This analysis identifies highly used resources that may

provide problematic now or in the future.

c. Looking at the resource utilization for each workload. Ascertain which workloads are the

major users of each resource. This helps narrow the attention to only the workloads that

are making the greatest demands on system resources.

d. Determining where each workload is spending its time by analyzing the components of

response time, allowing the determining of which system resources are responsible for

the greatest portion of the response time for each workload (Blackstone, 2009).

It is important to take a look at each resource within systems to see if any of them are saturated.

If a resource that is running is found at 100% utilization, then any workloads using that resource

are likely to have poor response time. If the goal is throughput rather than response time,

utilization is still very important. If it has two disk controllers, for example, and one is 50%

utilized and the other is swamped, then it has an opportunity to improve throughput by spreading

the work more evenly between the controllers.

35

The resources that are responsible for the greatest share of the response time are indicators for

where it should concentrate efforts to optimize performance. Using TeamQuest Model, it can be

determined the components of response time on a workload by workload basis, and it can predict

what the components will be after a ramp-up in business or a change in system configuration.

Components of response time analysis shows the average resource or component usage time for

a unit of work. It shows the contribution of each component to the total time required to

complete a unit of work

The steps to a capacity plan are:

a. First, forecasting what the organization will require of the IT systems in the future.

b. Using TeamQuest Model to determine the optimal system configuration for meeting

service levels on into the future.

Systems may be satisfying service levels now, but will they be able to do that while at the same

time meeting future organizational needs? Besides service level requirements, the other key input

into the capacity planning process is a forecast or plan for the organization’s future. Capacity

planning is really just a process for determining the optimal way to satisfy business requirements

such as forecasted increases in the amount of work to be done, while at the same time, meeting

service level requirements (Karmarker, 2009).

Future processing requirements can come from a variety of sources. Input from management

which include:

• Expected growth in the business.

• Requirements for implementing new applications.

• Planned acquisitions or divestitures.

• IT budget limitations.

• Requests for consolidation of IT resources.

Additionally, future processing requirements may be identified from trends in historical

measurements of incoming work such as orders or transactions (Karmarker, 2009).

After system capacity requirements for the future are identified, a capacity plan should be

developed to prepare for it. The first step in doing this is creating a model of the current

configuration. From this starting point, the model can be modified to reflect the future capacity

36

requirements. If the results of the model indicate that the current configuration does not provide

sufficient capacity for the future requirements, then the model can be used to evaluate

configuration alternatives to find the optimal way to provide sufficient capacity.

In summary, these basic steps towards developing a capacity plan have been shown as follow:

1. Determining service level requirements.

a. Defining workloads

b. Determining the unit of work

c. Identifying service levels for each workload

2. Analyzing current system capacity

a. Measuring service levels and compare to objectives

b. Measuring overall resource usage

c. Measuring resource usage by workload

d. Identifying components of response time

3. Planning for the future

a. Determining future processing requirements

b. Planning future system configuration.

By following these steps, it ensures that the organization will be prepared for the future, ensuring

that service level requirements will be met using an optimal configuration, and also have the

information necessary to purchase only what is needed, avoiding over-provisioning while at the

same time assuring adequate service (Karmarker, 2009).

2.2.2 Importance of Capacity Planning

Capacity planning is important because it makes the manufacturing organization to determine the

production capability of the facility. This will enable the organization to have the appropriate

through put. By having the appropriate throughput, the production process will be properly

ascertained. It will consist of the appropriate machinery, methods and maintenance (Vollmann,

Berry, Whybark and Jacobs, 2005).

Capacity planning also makes the organization to have the appropriate outputs. In a brewery, the

output will be in hectolitries of beer produced per month. The appropriate will enable the

organization to plan her sales strategies. From the sales, the total revenue will be determined as

sales revenue is price times quantity produced (Bandey, 2008). This is why capacity has a direct

37

positive relationship with the competitive advantage. This is because a company with adequate

capacity planning will know her output and sales in advance and use the knowledge to be ahead

of its competitors. It will be possible to have the correct price strategies.

Capacity planning makes the organization to know its inputs. Inputs are of the form of raw

materials, mean (human resource), money (capital) time, knowledge, energy, information and

infrastructure. It is these inputs that are processed through the production process to get the

outputs in the form of goods and services (Krajewshi, and Bitzman, 2000). The raw materials for

producing beer are malted barley, yeast, hops, additives, concentrated stout for blending and

water. Water is the largest by volume as beer is 95% water.

Capacity planning entails a knowledge of the current capacity and the average utilization rate.

The average utilization rate is the average output rate divided by capacity. It enables the

organization to determine if capacity is too much or too little. If it is too much, the company will

need to outsource, reduce capacity or sub contract some capacity. If it is too little the company

will need to run shift, or build another plant (Krajewishi and Bitzman, 2000).

2.2.3 Concept of Capacity Planning

Egujie (2001) wrote that capacity planning is the process of taking future decision today on the

inputs, throughput and outputs to meet the production requirements in a manufacturing

organization such as brewery. He pointed out that there is a hierarchy for the system linkages for

the capacity planning modules and one of the items in the hierarchy is resource planning. It is

linked directly to the sales and operations planning modules. It is the most highly aggregated and

longest range planning decision. The master production schedule is the primary information

source for rough-cut capacity planning. The rough-cut capacity planning stage is the next stage

lower than the resource planning stage. For breweries using materials requirements planning to

prepare detailed materials plans, a much more detailed capacity planning is possible with the

capacity requirements planning (CRP) is the third stage lower than rough-cut capacity planning

stage. The next stage after the capacity requirements planning is finite loading stage. Finite

loading in some ways is better seen as shop scheduling process and its therefore part of

production activity control and it is also a capacity planning procedure. The last but not the least

is the input/output stage. The inputs in Nigeria brewery include raw materials, men or human

resource which also includes women, money which is packaged through investment practices,

time which is a non-renewable resource, energy which is power x time and it is measured in

Joules when the power is in watts and time is in seconds. It also includes knowledge which is

38

specialized information gained through education, training and development and it makes the

workers in the breweries studied to reason a logical manner. It also includes information which is

processed data and it is needed for decision making, it also includes infrastructure which is the

totality of such public facilities like road network, power supply, air ways, communication

system, rail-way network, etc. Unfortunately, the breweries spent so much on electricity because

of the poor outage of electricity by the Power Holding Company of Nigeria (PHCN) and this

makes for the increase in a bottle of beer. Egujie (2001) found that quality control was a very

useful technique to improve capacity planning in a company striving for excellence such as

Bendel Brewery.





low, coordination can be achieved through rules and heuristics. In manufacturing management,

the planning-focused methods have been developed around the concept of material requirements

planning, while the methods that emphasize rule-based control and simplicity are founded on the

just-in-time (JIT) methodology (Ohno, 1988).

A classic way to pursue simplification in brewing is to isolate operations from external

uncertainties. The extent of the isolation depends greatly on the order penetration point (Olhager,

2003:319): the earlier the order-specific requirements are taken into account, the higher is the

exposure to the environment. That is why planning methods are most important in the MTO

manufacturing and the JIT methods are at their best in the make-to-stock environments

(Karmarkar, 1989; Vollman, 2005).

Usually both approaches co-exist in assemble-to-order systems and other intermediate settings.

The postponement of the order penetration point enables the use of JIT methods in the upstream

operations of customized manufacturing (Olhager and Rudberg, 2002). However, the inherent

complexity of producing according to individual orders cannot be eliminated by forcing JIT

methods upon the MTO parts of the processes (Hopp and Spearman, 2004). Hence, the time-

phased planning has remained as a vital part of manufacturing management despite the important

contributions of JIT. Recent literature has describes several techniques for integrating the

benefits of the two paradigms. The techniques are known by many names (e.g., CONWIP,

39

POLCA, COBACABANA, etc) and they differ in details but time-phased planning methods for

the creation of production schedules (Spearman et al, 1990; Suri, 1998; Land, 2009).

Contemporary methods of time-phased production planning are based on the Manufacturing

Resource Planning (MRP) framework. It was originally developed to complement MRP with

capabilities to check material plans’ feasibility against capacity constraints. Later, more

advanced applications of MRPII have been developed so that the feasibility checks could be

extended to other factors such as delivery schedules and financial constraints (Yusuf and Little,

1998). However, the practical implementations of such solutions have remained rare (McKay

and Wiers, 2004). In fact, it has been observed that even the capacity planning features of MRPII

are far less utilized than what could be expected on the bases of the academic literature (Halsall

et al, 1994); Kemppainen, 2007). As the material-planning parts of MRPII are well-established

(Vollmann, 2005), the observation implies that companies’ production planning practices can be

measured through the methods that they use in capacity planning.

Recent developments in enterprises software deliver a promise of easily applicable capacity

planning tools. While the conventional ERP systems are well-suited for the simpler capacity

checks, the so-called advanced planning and scheduling (APS) systems promote the more

sophisticated methods (Kreipl and Pinedo, 2004; Stadtler and Kilger, 2005). However,

companies’ diligence in applying their enterprise systems’ features is known to vary

considerably (e.g. Bendoly and Cotteleer, 2008). Thus, variance may be found also in the

utilization of the capacity planning features. That variance enables testing whether complex

organizations that do not put efforts in planning suffer from the lack of coordination (e.g.,

Zwikael and Sadeh, 2007). Consequently, the following hypothesis is presented as the

underlying assumption of this study:

Advantages of Sophisticated Capacity Planning Methods

It is reasonable to assume that not only the efforts in capacity planning but also the ways of

planning matter. The practical relevance of the framework is high because dominant ERP

software providers have structured their production planning modules in the same fashion (SAP,

2009). In addition, most textbooks either refer to it directly or provide illustrations that closely

resemble it (Hill, 2005 and Stevenson, 2004).

The backbone of the capacity planning process is in the material planning activities, that is:

master production scheduling (MPS), MRP, and the input/output (I/O) control (Vollmann et al,

40

2000). The optional activities are on the side of capacity planning. They are numbered in the

order of sophistication. It shows that the amount of required data records increases as the

methods get more sophisticated. The increase is cumulative because the records do not fully

substitute each other. Brief descriptions of each method are given in the following:

Non-systematic capacity planning represents inexplicit consideration of capacity constraints.

At the level of master schedules, it means that planners use their personal experience to evaluate

the feasibility of plans (Proud, 2007). In MRP, the inexplicit capacity considerations are realized

through the lead time parameters of bills of materials. The processing lead times represent the

averages, while the variances around the averages are taken into account with safety lead times

(Vollmann et al, 2000). In the I/O, priority scheduling rules can be used to level capacity

utilization without formal planning activities (Green and Appel, 1981; Kemppainen, 2007).

Rough-cut capacity planning (RCCP) is the simplest systematic method. It can be done with

several techniques but they all share the common characteristic of aggregation. Materials are

aggregated to end products or product groups and capacities to production lines or resource

groups (Proud, 2007). RCCP simplifies planning by ignoring sub-assembly inventories,

operations’ sequences, setups, and batch sizes but still provides the planners with a systematic

means to supervise how the resource utilization accumulates during the MPS activity (Vollmann

et al, 2000). That is an advantage when master schedules are updated frequently, MPS items are

numerous, or different MPS items load the same resources. In such situations, then non-

systematic methods are prone to human errors and easily result in overloaded schedules.

Capacity requirements planning (CRP) provides a more detailed technique for checking

material plans’ feasibility. The CRP calculations are done not only for the end products but also

for the subassemblies. In addition, the routing data enable calculating loads at individual

resources and considering the effects of operations’ sequences, setups, and batch sizes. Thus,

CRP corrects for the simplifications of RCCP and helps generating more reliable schedules.

Iterating the plans to achieve feasibility in terms of resources’ capacity limits is done manually

by human planners (Burcher, 1992; McKay and Wiers, 2004).

The next step from CRP is to automate the iterations of the plans. It can be done with finite

loading methods that are usually featured in APS systems (McKay and Wiers, 2004). The

process of using them is typically the following: first, material plans are downloaded from an

ERP system. Then, the algorithms of the finite loading software are used to find a solution,

where capacity constraints are satisfied with the fewest breaches of due dates. Finally, the

41

revised plans are uploaded back to the ERP system, where they are executed. The obvious

benefit of automating the capacity leveling is that it reduces the room for human errors.

In addition to capacity leveling, the finite loading algorithms can be used to solve more

complicated scheduling problems. The finite loading tools with optimization may be used, for

example, to maximize throughput or to minimize setups or downtimes (e.g., Davis and Mabert,

2000). Such techniques require the most planning parameters and their outputs are highly

dependent on the accuracy of the parameters. Yet, the data maintenance efforts and the

investments in the software may well be justified in some manufacturing environments, for

example in capital intensive production systems (Kreipl and Pinedo, 2004).

The planning methods are by no means mutually exclusive. Instead, several methods can be used

simultaneously for different purposes. For example, plant managers can use RCCP to evaluate

sales plans, master schedulers may use CRP to supervise their processes, and production

planners can do the finite loading of critical resources. A concept that brings clarity to this

plurality is bottom-up re-planning (Fransoo and Wiers, 2008; Vollmann et al., 2000). It means

that master schedules are updated on the bases of the lower-level planning activities. In a closed-

loop planning system, the master schedules are based on the finite loading of critical resources

(Kenat and Sridharan, 1998). In an intermediate solution, the master schedules are revised on the

bases of CRP. Consequently, the main method of planning can be identified. It is the method that

determines the output to which the manufacturing function commits itself.

In handling the advantages of capacity planning in the Nigerian Brewery studied (Osaguona,

2006) wrote that the first advantage of capacity planning in the Nigeria breweries studied is that

it entailed the use of the correct quality of the input materials. If the correct inputs are not

allowed for, it will not yield the correct output. The brewing department is saved from the

problem of having to a lot of blending with good quality beer before arriving at a good quality

product. Blending entails going back a lot of times to the labouratory to run physical, chemical

and micro-biological to get the appropriate quality of the final lager beer, stout, malt products.

Another advantage of capacity planning is that it entails the appropriate throughput. By

throughput is meant the production process. The components of the process include machines,

methods, and maintenance. The brewery studied had the appropriate machines that are imported

from Dublin, Ireland. Others are placed through Oversea Buying Limited (OBL) London. Some

of the machines include marshing machine, fermenter, marturating machine and bottle pasturizer.

42

The methods include marshing, decoction, fermentation, marturation techniques, bottling and

even distribution to the customers. Maintenance is to ensure the reliability of the brewing system.

Maintenance is by a combination of planed and corrective maintenance and overhauls. Every

year, the brewery is closed for some few months, and the maintenance crew is invited from the

manufacturers of the equipments. They come with some of the knockdown parts and all the

critical parts of the equipments are changed and the equipments function as though they are new.

Osaguona (2006) found that quality control was an enabler of capacity planning in the brewing

companies studied.

As all of the advanced planning methods aim to reduce errors in planning, it can be proposed that

they should have a positive effect on operational performance. Some studies have already

implied evidence of such an effect (Sheu and Wacker, 2001). Yet, they have not included finite

loading techniques, which is a major shortcoming because substantial effort has been put into

their development (Kouvelis et al., 2005). The development of progressive algorithms and

software would be well justified if there was evidence on the relationship between the accuracy

of planning and performance. Hence, the following hypothesis is formulated:

Fit between Capacity Planning Methods and Process Types

Another perspective to different planning methods’ effectiveness is to assume that methods’

suitability would depend on the context of their usage. Preliminary support for such an argument

can be found in the surveys of Jonsson and Mattsson (2003). This shows that practitioners’

satisfaction with different planning techniques depends on the type of their production processes:

the managers of job shops are content with RCCP, the most satisfied users of CRP work in batch

process plants, and the finite loading methods are most popular in production lines. The

observations are aligned with the systematic review of and the review of Sousa and Voss, which

both indicate that the process type is a typical contingency factor for the effectiveness of various

operations management practices. In the context of planning, the influence of the process type

can be explained with two classic contingency-theoretical constructs: the repetitiveness and the

complexity of the tasks that constitute the processes.

1) RCCP fits with the job shops because in low-volume and high-variety environments, the

data records of the more detailed methods are difficult to maintain. Moreover, the more

detailed resource-specific plans are not necessary because the complexity of the system is

43

limited with general-purpose machinery and widely skilled workforce (Blackstone and

Cox, 2005; Hill, 2007).

2) CRP fits with the batch processes because the more repetitive operations make the

maintenance of the data records worthwhile. Furthermore, information about the

resource-specific workloads is necessary because the resources are more specialized, and

different products utilize them differently (Jonsson and Mattsson, 2003).

3) Finite loading methods fit with batch processes, whose complexity is reduced with

bottleneck control (Vollmann et al, 2000). Finite loading works in a batch process if a

stationary bottleneck can be identified and all other resources are subordinated to its

schedule. Otherwise, each finite loading of one resource can make another resource a

new bottleneck, and consequently the iteration of the plans may become endless.

4) In production lines, the complexity is low because all resources are subordinated to the

flow of the line. Thus, the capacity of the entire line can be planned as a single resource.

Detailed planning is desirable because untimely changeovers can be costly in larger

assembly lines (Hayes and Wheelwright, 1979) or cause congestion in smaller

manufacturing cells. In addition, the repetitiveness of operations makes it easier to

maintain the parameters of the most sophisticated methods.

The relationship between the process types and planning methods can also be explained with the

interdependence between the resources of the processes. The alternative types of

interdependence are pooled, sequential, and reciprocal. The pooled and the sequential processes

are the simplest to coordinate but they have very different implications for planning (Barki and

Pinsonneault, 2005). The processes with pooled resources are inherently flexible, and that is a

capability that should not be constrained with too stringent planning. A job shop is an archetype

of pooled interdependence (Galbraith, 1973). Meanwhile, the sequential processes are suited for

efficiency, which is a capability that can be fostered with detailed planning. In manufacturing

environments, sequential relationships exist in production lines and around the bottlenecks of

batch processes.

The most difficult processes to coordinate are those where resources are reciprocally

interdependent. That is because all actions by any resource may affect multiple other resources

(Galbraith, 1973). Some specificity in planning is necessary to prevent undesirable cascade

effects but getting into the details is difficult because the possible interactions are. Therefore, a

44

moderately sophisticated planning method such as CRP is the most suitable option for the

reciprocal processes of batch shops (Reeves and Turner, 1972).

2.2.4 The Concept of Performance


leverage, activity and morale (Nwachukwu, 2004).

Dictionary’s definition of efficiency as fitness or power to accomplish or success in

accomplishing the purpose intended, adequate power, effectiveness, efficacy. Later on, it is

pointed out that efficiency acquired a second meaning – the ratio between input and output,

between effort and results, expenditure and income, cost and the resulting pleasure, this second

meaning became current in Business and Economics, only since the beginning of the 20th

Century. Still later on, influenced by the scientific management movement, efficiency was

defined as the ratio of actual performance to the standard performance (Bell, 2006).

While efficiency is concerned with measuring the ability of inputs to produce outputs, or

relationship between performance and standard efficiency is concerned wit the failure of inputs

to achieve desired outputs, the gap between actual performance and expected, and between

results and efforts (Abernathy and Townsend, 2005).

Apart from the efficiency another closely related performance variables is effectiveness. To be

literally means to have effects, when we say that something is effective we mean that it has

effects that we desired that we recognize as international in the design of the thing in question.

When we say that a television set is effective we mean that it provides clear picture and

reasonable reproduction of sounds. Such an example serves in this simple case in which the

system under study has felt outcomes and the relevant observers are decided on what is intended

to design and use. When the system under study has few outcomes and the relevant observers are

decided on what is intended in design and user. When the system is more complex like in the

case of a public enterprise, operationalisation becomes difficult. However, one public enterprise

is more effective than another if:

(a) It has more chances of survival than the other;

(b) It meets its essential function or throughput than the other;

(c) It contributes more to the suprasystem than the other; and

45

(d) It more than maximizes its benefits like profit subject to some constraints like taxes and

other obligations than the other. Apart from efficiency and effectiveness another important

performance variable is productivity (Nwachukwu, 2004).

Productivity has been defined as the measure of how well resources are brought together in

organization and utilized for accomplishing a set of results. It is reaching the highest level of

performance productivity in a public expenditure or resource. To operationalise productivity in a

public enterprise the ratio of total output to total input in very handy. Total input is the naira

value of all the factors of production for that year which include land, labour and capital. The

limitation of this method of operationalising productivity is that entrepreneurship or management

which is the factor of production is difficult to quantify in monetary terms. Another limitation is

that of public enterprises that render a service, it becomes difficult to quantify the output in

monetary terms since the outputs are not tangible (Buffa and Sarin, 2007).

This measure of productivity has the advantage that it aggregates the effectiveness of the use of

the factors of production of the public enterprise to produce goods and services. It draws

attention to the fact that a good integration of resources physical and human will yield higher

output of the public enterprises shown by the result of total output/total input being greater than 1

(Cohen and Zysman, 2007).

Higher productivity of the employees of a public enterprise has the following good effects:

iii. Higher incomes and profits;

iv. Higher earning;

v. Increased supplies of both consumer and capital goods at lows costs and lower prices;

vi. Ultimate shorter hours of work and improvements in working at living conditions;

and

vii. Strengthening the general economic foundation of workers.

Another performance variable apart from productivity is profitability or the ability of the

enterprise to make profit. Profit is the income or the difference between sales revenue and total

cost. The profitability of enterprise is summarized in the valuation of that enterprise. Indeed the

basic objective of measurement of profitability is to provide a valuation, the enterprise which

will be a critical assessment of the worth of the investment. In effect, the value of an enterprise

may be stated as being the present value of its future stream (Bell, 2006).

46

The profitability of a privatized or commercialized public enterprise can be operationalised by

using profitability ratios. Profitability ratios are classified into two categories; ratios which

express income as a percentage of sales, and, and ratios which express income as a yield

associated with the employment of resources. For the purpose of the analysis of profitability,

income is generally expressed as earnings before interest and tax (EBIT). The profitability ratios

of income as a percentage of sales include the following:

i. gross profit ratio which is the ratio of gross margin or profit to sales which is used to

check stability of market conditions;

ii. net income ratio of earnings before interest and taxes to sales. The profitability ratio of on

resources employed include the following;

iii. return on capital employed which is the ratio of the earnings before interest and taxes

over net asset value is found by adding the value of assets to that current assets and

subtracting the total assets which is the ratio of earnings before interest and taxes over

fixed plus current assets;

iv. return on total assets which is the ratio of earnings before interest and taxes plus current

assets; and

v. return on gross assts which is the ratio of earnings before interest and taxes plus

depreciation for the period over assets at costs plus current assets.

Another performance variable apart from profitability is solvency. Solvency is the ability of an

enterprise to meet its immediate financial obligations and thus avoid the possibility of

insolvency. To operationalise the solvency of the public enterprise two ratios are in common use

as follows:

i. current ratio which is the ratio of current assets to current liabilities is a measure of how

current assets could be converted into cash to meet current liabilities and if its value is

less than one it would indicate that the firm might have a potential problem in meeting

creditor’s claim;

ii. acid test ratio which is the ratio of current assets less inventory over current liabilities

which recognizes he problem of the current ratio that inventory is not easily converted to

cash (Nwachukwu, 2004).

47

Another performance variable apart from solvency is leverage which is a measure of how far the

total capital of the enterprise is borne by low term debt. In operationalising the leverage of

privatized public two ratios come in hand as follows:

i. gearing of leverage ratios which is long term debt as a fraction of share capital;

ii. gearing or leverage ratio which is longer term as a fraction of share capital.

Another performance variable apart from leverage is activity.

Activity is defined as the use made of resource by the enterprise. To operationalise activity of a

public enterprise the following ratios are useful as follows:

(i) Inventory turnover or the ratio of sales over average inventory which is the rate at

which an enterprise converts inventory into sales;

(ii) Average debt collection period which is given by debtors divided by credit sales times

365 which gives the average number of days for payment;

(iii) Ratio of sales to total assets value which is the ratio of sales to fixed assets plus current

assets and indicates the ability of the assets to generate income.

Apart from activity another performance variable is morale. It is truly as a member of an

integrated group with high morale that a worker in public enterprise can make his maximum

contribution the enterprise. Morale is the state of mind which makes men do great things

(Nwachukwu, 2006). The movement of morale includes the following:

i) Polarization;

ii) Autonomy;

iii) Flexibility;

iv) Potency;

v) Participation.

Polarization is the degree to which the group is oriented towards goals that is clear to the

members and share by them. Autonomy is the degree to which a group determines it’s own

activities and takes it’s or decision. Flexibility is the degree to which the group’s activities are

mark as informal rather than formal procedures. Potency is the degree to which the individual

needs are satisfy by membership in the group it. Participation is the degree to which members of

the groups at themselves to the assigned duties (Nwachukwu, 2004).

48

2.2.5 Concept of Manufacturing

The index of manufacturing production increased by 2.6 per cent to 182.7 (1985 =100) in 1992

compared with 9.3 per cent in 1991. The increase was reflected in all the quarterly indices which

were generally higher than those of the previous year, except the second quarter where the index

declined slightly by 0.2 per cent. The share of the subsection in the Gross Domestic Product

(GDP) rose from 8.4 per cent in 1991 to 8.6 percent in 1992 (CBN, 2002).

The performance of the brewing sub-sector was constrained by high cost of production which

was attributable mainly to substantial depreciation of the naira exchange rate. The resultant sharp

rise in cost of importation of raw materials, machinery and spare parts resulted in corresponding







(CBN, 2002).

The observed developments were largely corroborated by the results of a country-wide survey

conducted by the Central Bank of Nigeria. The survey covered 684 manufacturing

establishments in 29 industrial groups and achieved a response rate of 56.6 per cent. The results

showed that overall manufacturing capacity utilization rate raised from 38.7 per cent in 1991 to

41.8 per cent. Eight of the twenty-seven industrial sub-groups that responded.

2.3 THEORETICAL FRAMEWORK

This study was guided bymany relevant theories and models.

2.3.1 Contingency, or Situational Management

There has been a fairly widespread tendency for certain scholars and writers in organization

theory to misunderstand the approach to management by those who emphasize the study of

management and its fundamentals (Bell, 2006). They see principles and theory as a search for the

one best way of doing things. For example, two scholars said:

49

In the past few years, there has been evidence of a new trend in the study of organizational

phenomena. Underlying this new approach is the idea that the internal functioning of

organizations must be consistent with the demands of organization task, technology, or external

environment, and the needs of its members if the organization is to be effective. Rather than

searching for the panacea of the one best way to organize under all conditions, investigators have

more and more tended to examine the functioning of organizations in relation to the needs of

their particular members and the external pressures facing them. Basically, this approach seems

to be leading to the development of a “contingency” theory of organization with the appropriate

internal states and processes of the organization contingent upon external requirements and

member needs (Bell, 2006).

In the same tone, another writer on management, one among many, appears to be concerned that

basic management theory and science attempt to prescribe a one best way of doing things and do

not take the situation into account. In an interesting book, this writer states:

Above all, the situationalist holds that there is no one best way to manage. Taylor may have been

right when he said there is one best way to perform a repetitive physical task, but that is not true

of planning, organizing, leading, controlling, or decision making. Different organizations with

different tasks and different competitive

2.3.2 Aggregate Planning Models

Aggregate planning, which might also be called macro production planning, addresses the

problem of deciding how many employees the firm should retain and, for a manufacturing firm,

the quality and the mix of products to be produced. Macro planning is not limited to

brewingfirms. Service organizations must determine employee staffing needs as well. For

example, airlines must plan staffing levels for flight attendants and pilots, and hospitals must

plan staffing levels for nurses. Macro planning strategies are a fundamental part of the firm’s

overall business strategy. Some firms operate on the philosophy that costs can be controlled only

by making frequent changes in the size and/or composition of the workforce. The aerospace

industry in California in the 1970s adopted this strategy. As government contracts shifted from

one producer to another, so did the technical workforce. Other firms have a reputation for

retaining employees, even in bad times. Until recently, IBM and AT&T were two well-know

examples (Fisher et al, 2002).

50

Whether a firm provides a service or produces a product, macro planning begins with forecast of

demand. How responsive the firm can be to anticipate changes in the demand depends on several

factors. These factors include the general strategy the firm may have regarding retaining workers

and its commitments to existing employees. Demand forecasts are generally wrong because there

is almost always a random component of the demand that cannot be predicted exactly in

advance. This assumption is made to simplify the analysis and allow us to focus on the

systematic or predictable changes in the demand pattern, rather than on the unsystematic or

random changes (Bell et al, 2003).

2.3.3 Contingency theory of capacity planning

In manufacturing organization, many important decisions are made in service activities. Capacity

planners decide when and with what resources organizations produce their outputs. The methods

that are used to create the plans are crucial to organizational performance (Kanet and Sridharan,

1998; Davis and Mabert, 2000; Zwikael and Sadeh, 2007). Poor methods yield plans that are

either too weak and result in excessive lead times or too tight and result in failures to keep

promised delivery dates. Consequently, it is not surprising that planning methods have

represented a major research area in the operations management literature. Different planning

techniques have been studied especially in analytical and simulation-based research (Kouvelis et

al, 2005). That stream of research has produced various sophisticated algorithms that enable the

leveling and optimization of capacity plans (e.g. Davis and Mabert, 2000; Yang, 2002; Deblaere

et al, 2007).

Meanwhile, however, empirical researchers have repeatedly observed that most practitioners use

considerably less sophisticated planning methods than what is discussed in the academic

literature (MacKenzie and House, 1978; McKay, 2002). Moreover, empirical evidence indicates

that those practitioners using advanced planning methods are on average less satisfied with their

plans than those who use simpler and less accurate methods (Jonsson and Mattson, 2003). This

section aims to use process complexity as a contingency factor that explains why the practices of

capacity planning often differ from the academic model of capacity planning.

The analysis of this section employs the logic of strong inference and the contingency theory of

organization to explain the determinants of different planning methods’ effectiveness. The

strong-inference logic refers to a research design, where theory building is based on tests of

competing hypotheses (Platt, 1964). The contingency-theoretical perspectives to process

51

complexity are used to propose that sometimes the most sophisticated planning methods may be

less effective than the simpler techniques. The contingency hypothesis is tested against a

hypothesis about the universal superiority of the most advanced planning methods. The statistical

results from the survey dataset are complemented by the interview dataset that sheds light on the

reasons why practitioners end up using certain planning methods.

2.3.4 Capacity Planning Supply Chain Model

Two major optimization problems in supply chain management are long term capacity planning

(static problem), and short term inventory control optimization (a dynamic problem). In capacity

planning, the entire structure of the supply chain – locations and sizes of factories, warehouses,

roads, etc is decided (within constraints). In inventory optimization, we take the structure of the

supply chain as fixed, and decide possibly in real-time who to order from, the order quantities,

etc. The challenge is to perform these optimizations under uncertainty (Berry et al, 2002).

A supply chain is a network of suppliers, production facilities, warehouses and end markets.

Capacity planning decisions involve decisions concerning the design and configuration of this

network. The decisions are made on two levels: strategic and tactical. Strategic decisions include

decisions such as where and how many facilities should be built and what their capacity should

be. Tactical decisions include where to procure the raw-materials from and in what quantity and

how to distribute finished products. These decisions are long range decisions and a static model

for the supply chain that takes into account aggregated demands, supplies, capacities and costs

over a long period of time (such as a year) will work (Berry et al, 2002)

From a theoretical viewpoint, the classical multi-commodity flow model (Buffaand Sann: 2007)

is the natural formulation for capacity planning. However, in practice, a number of non-convex

constraints like cost/price breakpoints and binary 0/1 facility location decisions change the

problem from a standard LP to an non-convex LP problem, and heuristics are necessary for

obtaining the solution even with state-of-the-art programs like CPLEX. Theoretical results on the

quality of capacity planning results do exist, and refer primarily to efficient usage of resources

relative to minimum bounds. For example, the total installed capacity can be compared with

respect to the actual usage (utilization), total cost with respect to the minimum possible to meet a

certain demand, etc.

52

The Supply Chain Model: Details

In a simple generic example, to design a supply chain network, location and capacity allocation

decisions were made. It had a fixed set of suppliers and a fixed set of market locations. There

was need to identify optimal factory and warehouse locations from a number of potential

locations. The supply chain was modeled as a graph where the nodes are the facilities and edges

are the links connecting those facilities. The model will work for linear, piece-wise linear as well

as non-linear cost functions (Ahmed, King, Parija: 2003).

In general the supply chain nodes can have complex structure. Two major classes were

distinguished: AND and OR modes, and their behaviour (Ahmed et al, 2003).

OR Nodes: At the OR nodes, the general flow equation holds. Here, the sum of inflow is equal

to the sum of outflow and there is no transformation of the inputs. The output is simply all the

inputs put together. A warehouse node is usually an OR node. For example a coal warehouse

might receive inputs from 5 different suppliers. The input is coal and the output is also coal and

even if fewer than 5 suppliers are supplying at some time, then also output from the warehouse

can be produced.

AND nodes: At the AND nodes, the total output is equal to the minimum input. A factory is

usually an AND node. It takes in a number of inputs and combines them to form some output.

For example a factory producing toothpaste might take calcium and fluoride as inputs. Output

from the factory can only be produced when both the inputs are being supplied to the factory.

Even if the amount of one input is very large, the output produced will depend on the quantity of

other input which is being supplied in smaller amounts. The flow equation for node C, if C is an

ANDnode will be as follows:

φCD = min (φAC,φBC)

The total cost of the supply chain is divided into 4 parts

1. Fixed capital expenses for the nodes: the cost of building the factory or warehouse

2. Fixed capital expenses for the edges: the cost of building the roads

3. Operational expenses for nodes

4. Transportation expenses for the edges (Ahmed et al, 2003)

The following notations are used in the model:

S = Number of supplier nodes

M = Number of market nodes

P = Number of products

53

X = Number of intermediate stages

Nx = Number of potential facility locations in stage x

E = Number of edges

PijC (Q) = Cost function for node j in stage i of the supply chain

PijC (Q) = Cost function for edge k of the supply chain

PijQ = Quantity of product p processed by node j in stage i

PijQ = Quantity of product p transported over edge k

Qij−max = Maximum capacity of node j in stage i

Qk −max = Maximum capacity of edge k

plmφ = Flow of product p between node l and node m

Fij = Fixed capital cost of building node j in stage i of the supply chain

Fk = Fixed capital cost of building edge k in the supply chain

uj = Indicator variable for entity j in the supply chain, i.e., uj = 1 if entity j is located at site j, 0

otherwise (Ahmed et al, 2003)

The goal is to identify the locations for nodes in the intermediate stages as well as quantities of

material that is to be transported between all the nodes that minimize the total fixed and variable

costs.

This minimax program is in general not a linear or integer linear optimization (weak duality can

be used to get a bound, but strong duality may not hold due to the nonconvex cost, profit

functions having breakpoints). The absolute best case (best decision, best demands and supplies)

and worst case (worst decision, worst demands and supplies) can be found using LP/ILP

techniques. It is stressed that even this information is very useful, in a complex supply chain

framework. However, note the following. The key idea in the approach was that linear

constraints were used to represent uncertainty. Sums, differences, and weighted sums of

demands, supplies, inventory variables, etc, indexed by commodity, time and location can all be

intermixed to create various types of constraints on future behaviour. Integrality constraints on

one or more uncertain variables can be imposed, but do result in computational complexities

(Ahmed, et al, 2003)

54

Given this, there are the following advantages of the approach:

(i) The formulation is quite intuitive and economically meaningful, in the supply chain

context. Many kinds of future uncertainty can be specified.

(ii) Bounds can be quickly given on any candidate solution using LP/ILP, since the

equations are then linear/quasi-linear in the demands/supplies/other params, which are

linearly constrained (or using Quadratic programming with quadratic constraints). The

best case, best decision and worst case, worst decision are clearly global bounds, solved

directly by LP/ILP.

(iii) The candidate solution is arbitrary, and can incorporate general constraints (e.g., set-

theoretic) not easily incorporated in a mathematical programming framework (formally

specifying them could make the problem intractable).

(iv) Multiple candidate solutions can be obtained in one of several ways, and the one having

the lowest worst case cost selected. These solutions can be obtained by:

(a) Randomly sampling the solution space: A feasible solution in the supply chain context

can be obtained by solving the deterministic problem for a specific instance with a

random sample of demand and other parameters. The computational complexity is that

of the deterministic problem only. A number of solutions can be sampled, and the one

having the lowest worstcase cost selected. While the convergence of this process to the

Min-max solution is still an open problem, note that our contribution is the complete

framework, and the tightest bound is not necessarily required in an uncertain setting.

(b) Successively improving the worst case bound.

1. A candidate solution is found (initially by sampling, say), and its worst case performance

is determined at a specific value of the uncertain parameters (demand, supply, …).

2. The best solution for that worst case parameter set is determined by solving a

deterministic problem. This is treated as a new candidate solution, and step 1 is repeated.

3. The process stops when new solutions do not decrease the worst case bound significantly,

or when an iteration limit has been reached.

In passing it is noted that the availability of multiple candidate solutions can be used to

determine bounds for the a-posteriori version of this optimization. How much is the worst case

cost, if an optimal decision is made after the uncertain parameters are realized? This is very

simply incorporated in our cost function CO, by using at each value of the uncertain parameters,

55

a new cost function which is the minimum of all these solutions. This retains the LP/ILP

structure of the problem of determining best/worst case bounds given candidate solutions.

C (Demands Supplies,….) =

min(C1 (Demands Supplies,…) C2 (Demands, Supplies,…)…)

These same comments apply for the inventory optimization problem also. Contrasting this with

the probabilistic approach, even if an optimal sets of decisions (candidate solution) is given, at

the minimum, the pdf's governing the uncertain parameters will in general have to be propagated

through an AND-OR tree, which can be computationally intensive.

2.3.5 Capacity Planning Model

In the previous section, the single period capacity planning problem was studied. In this section,

how to extend the single period model to a multi-period setting will be discussed. In practice, a

contract will have duration. In the existing literature that studies capacity contracts, there are two

different ways to model the duration of a contract. If the contracts require a long term

commitment, after the firm signs the contract to acquire capacity from its supplier, the firms

reserve or buy the same amount of capacity in each period until the end of the planning horizon.

On the other hand, if the contracts are short term, the firm can reserve different amounts of

capacity for different periods. For example, Ahmed et al, (2003), consider long term contracts

while Yazlali and Erhun use one-period short term contract.

In the context of the design of a new supply chain, the firm does not own the capacity itself but

reserves capacity from its suppliers. The contract does not need to be for either the short term

such as one period or the long term such as to the end of the planning horizon. The firm and its

suppliers can reach agreement on a duration that is beneficial to both parties. For instance, a

supplier might want to offer a contract with median duration and better price to encourage the

firm to commit. For the firm, signing a long term contract might be too risky; on the other hand

short term contracts might be too expensive. In this section, how the firm should plan its capacity

when it has the flexibility to choose the durations of the contracts will be examined(Ahmed et al,

2003)

2.3.6 Capacity Mathematical Model

In the single period problem, each contract can be specified with three terms: per-period unit

price of the fixed-price capacity, per-period unit price to reserve the option capacity, and per-

56

period unit exercise price of the option capacity. In a multi-period setting, another specification

will be added, which is the contract duration. For example, a supplier quotes a three-month

contract with fixed-price $50, option reservation price $5, and option exercise price $50 to the

manufacturer. The manufacturer decides to reserve 100 units of fixed-price capacity and 20 units

of option capacity under this contract. It must pay the price of 100 units fixed-price capacity ($50

$100 = $5000) and 20 units option capacity ($5 $20 = $100) in each of the three consecutive

months starting with the first month of the contract. The manufacturer then has 100 units of

fixed-price capacity and 20 units of option capacity for each of the three consecutive months.

The prices of the contract can depend on the duration. To encourage a longer commitment, the

prices might decrease as the duration of the contract increases. In these situations, the multi-

period capacity planning problem involves another type of tradeoff between the flexibility (or

duration) of the contract and its price. Contracts with shorter duration have more flexibility while

contracts with longer duration offer lower prices (Ahmed et al, 2003).

Let T be the length of the planning horizon. Resource k offers contracts with durations in the set

Tk = {Tk,1, …, Tk,i, …}. To simplify the notation, we assume that for any resource all contracts

have different durations. This assumption can be relaxed and all the results still follow.

The first condition says a contract starts after the previous contract finishes. Condition 2

specifies that the manufacturer does not reserve capacity beyond the planning horizon. We call a

sequence feasible if it satisfies these two conditions. One implicit assumption here is that for

each period, we have only one contract active for each resource. In addition to deciding the

sequence of the contracts for each resource, the manufacturer needs to decide the corresponding

sizes: {ck,1, …, ck,i, …} and {gk,1, …, gk,i, …}. It is noted that it permit zero capacity contracts at

zero cost, which allows the firm to not use a resource for any subset of periods. The first contract

will cover the first two periods. Since the first two periods are cover by the same contract, the

fixed-price and total capacity reserved for each of these two periods are the same, which are c2

and g2. Similarly, a contract with duration 1 period is used to covered period 3 and a contract

with duration 3 periods is used to cover the rest of the horizon (Ahmed et al, 2003).

It was assumed that unfilled demands are lost and unused capacity cannot be saved for future

usage. It was also assumed that the manufacturer will not use any unused capacity to build and

store inventory. Even though we do not allow inventory, the multi-period capacity planning

problem is not separable since the firm can use a contract to cover multiple periods.

57

It was assumed that the manufacturer needs to decide the sequence and sizes of the contracts for

each resource at the beginning of the planning horizon. To this extent, we also assume that it has

a demand forecast for each period at the beginning of the first period. In practice, capacity

decisions usually need to be made with a much longer lead time than the planning horizon. In

these situations, our two-stage decision process matches with the reality. Moreover, as was

discussed in the introduction, since the manufacturer doesn’t own the capacity, it is important for

it to secure the price and supply of the capacity by signing contracts at an early stage. However,

this is a restrictive assumption and it would be interesting to study the capacity planning problem

in a dynamic setting.

A strategy in multi-period problem contains two types of decisions: the sequence of contracts to

be used and the amount of capacity to acquire after choosing the sequence of contracts. There are

an exponential number of combinations of contracts that the manufacturer can choose from. To

evaluate one strategy, the firm needs to solve a large scale stochastic linear program, to find the

optimal contract sizes. Therefore, the multi-period problem is much more complex than the

single period problem (Ahmed et al, 2003)

In the following sections, we will develop an efficient heuristic algorithm that can find a good

capacity plan for the multi-period problem under assumption 1. The same heuristic algorithm

will also provide a good upper bound to verify the effectiveness of the capacity plan (Fisher et al,

2002).

2.3.7 Solving the General Multi-Period Problem

The main difference from the single period case is that the multi-period capacity planning

problem needs to decide the sequence of the contracts. The amount of capacity that needs to be

reserved depends on the contract sequence that the firm has chosen. If the sequence for each

process is fixed, finding the optimal contract sizes is a stochastic linear programming problem

that is very similar to the single period capacity planning problem (Fisher et al, 2002)

The difficulty of solving the multi-period problem lies in the fact that there are a large number of

combinations of contract sequences that the firm can choose from. The algorithm that we

proposed for the single period problem is effective, but it still requires a considerable amount of

computational power. Therefore, in this section an efficient heuristic algorithm for the general

58

multi-period capacity planning problem will be developed under the assumption that each

process only has one dedicated resource.

The idea is to separate the decision of choosing the contract sequence from finding the optimal

contract sizes. The algorithm consists of the following steps:

1. Use the decomposition method proposed to separate the original multi-period capacity

planning problem into independent sub-problems, with one multi-period problem for

each process (Bell et al, 2003)

2. Solve each multi-period sub-problem to find a feasible contract sequence for each

process. This provides an initial feasible solution.

3. Fix the contract sequence for each process and then find the optimal contract sizes. This

provides an improvement to the initial solution.

Formalize the algorithm, and distribute the revenue of each product into each process based on

the prices of the contracts for the process. The same method will be use to separate the multi-

period problem. However, in the multi-period problem, each process has multiple sets of prices,

with one for each contract duration. Therefore, for each process, the average prices will be used

over all the contract durations in the decomposition method.

2.3.8 More Theories or Models of Capacity

Aggregate planning, which might also be called macro capacity planning, addresses the problem

of deciding how many employees the firm should retain and, for a manufacturing firm, the

quality and the mix of products to be produced. Macro capacity planning is not limited to

manufacturing firms. Service organizations must determine employee staffing needs as well. For

example, airlines must plan staffing levels for flight attendants and pilots, and hospitals must

plan staffing levels for nurses. Macro capacity planning strategies are a fundamental part of the

firm’s overall business strategy. Some firms operate on the philosophy that costs can be

controlled only by making frequent changes in the size and/or composition of the workforce. The

aerospace industry in California in the 1970s adopted this strategy. As government contracts

shifted from one producer to another, so did the technical workforce. Other firms have a

reputation for retaining employees, even in bad times. Until recently, IBM and AT&T were two

well-know examples (Fisher et al, 2002).

59

Whether a firm provides a service or produces a product, macro capacity planning begins with

forecast of demand. How responsive the firm can be to anticipate changes in the demand depends

on several factors. These factors include the general strategy the firm may have regarding

retaining workers and its commitments to existing employees. Demand forecasts are generally

wrong because there is almost always a random component of the demand that cannot be

predicted exactly in advance. This assumption is made to simplify the analysis and allow us to

focus on the systematic or predictable changes in the demand pattern, rather than on the

unsystematic or random changes (Bell et al 2003).

Traditionally, most manufacturing firms have chosen to retain primary service in house. Some

components might be purchased from outside suppliers, but the primary product is traditionally

produced by the firm. Henry Ford was one of the first American producers to design a

completely vertically integrated business. Ford even owned a stand of rubber trees so it would

not have to purchase rubber for tires. That philosophy is undergoing a dramatic change, however.

In dynamic environments, firms are finding that they can be more flexible if the production is out

sourced; that is, if it is done on a subcontract basis. One example is Sun Microsystems, a

California-based producer of computer workstations. Sun, a market leader, adopted the strategy

of focusing on product innovation and design rather than on production. They have developed

close ties to contract producers such as San Jose-based Solectron Corporation, winner of the

Baldrige Award for Quality. Subcontracting is its primary producing function has allowed Sun to

be more flexible and to focus on innovation in a rapidly changing market (Hadley, 2002).

Capacity planning involves competing objectives. One objective is to react quickly to anticipated

changes in demand, which would require making frequent and potentially large changes in the

size of the labour force. Such a strategy has been called a chase strategy. This may be cost

effective, but could be a poor long-run business strategy. Workers who are laid off may not be

available when business turns around for this reason, the firm may wish to adopt the objective of

retaining a stable work-force. However, this strategy often results in large buildups of inventory

during periods of low demand. Service firms may incur substantial debt to meet payrolls in slow

periods. A third objective is to develop a production pan for the firm that maximizes profit over

the planning horizon subject to constraints on capacity. When profit maximization is the primary

objectives, explicit costs of making changes must be factored into the decision process (Hiller

and Lieberman, 2000).

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Capacity planning methodology is designed to translate demand forecasts into a blueprint for

planning staffing and production levels for the firm over a predetermined planning horizon.

Capacity planning methodology is not limited to top-level planning. Although generally

considered to be a macro planning tool for determining over all workforce and production levels,

large companies may find capacity planning useful at the plant level as well. Production capacity

planning may be viewed as a hierarchical process in which purchasing, planning, production, and

staffing decisions must be made at several levels in firm. Capacity planning methods may be

applied at almost any level, although the concept is one of managing groups of items rather than

single items. This section reviews several techniques for determining capacity plans. Some of

these are heuristic (i.e. approximate) and some are optimal. We hope to convey to the reader an

understanding of the issues involved in aggregate planning, knowledge of the basic tools

available for providing solutions, and an appreciation of the difficulties associated with

implementing aggregate plans in the real world (Buffa and Sarin, 2007).

The capacity planning approach is predicted on the existence of a capacity planning unit of

production. When the types of items produced are similar, a capacity planning production unit

can correspond to an average item, but if many different types of items are produced, it would be

more appropriate to consider capacity planning units in terms of weight (tons of steel), volume

(gallons of gasoline), amount of work required (worker-years of programming time), or dollar

value (value of inventory in dollars). What the appropriate capacity planning scheme should be is

not always obvious. It depends on the context of the particular planning problem and the level of

capacity planning required (Fisher et al, 2002).

The Aggregate Capacity Planning Problem

The goal of aggregate capacity planning is to determine aggregate production quality and the

levels of resources required to achieve these production goals. In particular translates to finding

the number of workers that should be employed and the number aggregate units to be produced

in each of the planning periods 1, 2…, T. The effective of aggregate capacity planning is to

balance the advantages of producing to meet divisions as closely as possible against the

disruptions caused by changing the levels of proportion and/or the workforce levels (Hadley,

2002). The primary issues related to the aggregate planning problem include:

1. Smoothing: smoothing refers to cost that result from changing production and workforce

levels form one period to the next. Two of the key components of smoothing costs are the

costs that result form hiring and firing workers. Aggregate capacity planning methodology

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requires the specification of these costs, which may be difficult to estimate. Firing workers

could have far reaching consequences and costs that may be difficult to evaluate. Firms that

hire and fire frequently develop a poor public image. This could adversely affect sales and

discourage potential employees from joining the company. Furthermore, workers that are laid

off might not simply wait around for business to pick up. Firing workers can have a

detrimental effect on the future size of the labor force if those workers obtain employment in

other industries. Finally, most companies are simply not at liberty to hire and fire at will.

Labor agreements restrict the freedom of management to freely alter workforce levels.

However, it is still valuable for management to be aware of the cost trade-offs associated

with varying workforce levels and the attendant savings in inventory costs (Hadley, 2002).

2. Bottleneck problems:the term bottleneck is use to refer to the inability of the system to

respond to sudden changes in demand as a result of capacity restrictions. For example, a

bottleneck could arise when the forecast for demand in one month is unusually high, and the

plant does not have sufficient capacity to meet that demand. A breakdown of a vital piece of

equipment also could result in a bottleneck (Hiller and Lieberman, 2000).

3. Planning horizon: the number of periods for which the demand is to be forecasted, and

hence the number of periods for which workforce and inventory levels are to be determined,

must be specified in advance. The choice of the value of the forecast horizon, T, can be

significant in determining the usefulness of the aggregate plan, if T is too small, then current

production levels might not be adequate for meeting the demand beyond the horizon length.

Ifit is too large, it is likely that the forecast far into the future will prove inaccurate. If future

demands turn out to be very different from the forecasts, then current decisions indicated by

the aggregate plan could be in correct. Another issue involving the planning horizon is the

end-of-horizon effect. For example, the aggregate plan might recommend that the inventory

at the end of the horizon be drawn to zero in order to minimize holding costs. This could be a

poor strategy, especially if demand increases at that time. (However, this particular problem

can be avoided by adding a constraint specifying minimum ending inventory levels) (Bell et

al, 2003).

In practice, rolling schedules are almost always used. This means that at the time of next

decision, a new forecast of demand is appended to the former forecasts and old forecasts might

be revised to reflect new information. The new aggregate plan may recommend different

production and workforce levels for the current period than were recommended one period ago.

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When only the decisions for the current planning period need to be implemented immediately,

the schedule should be viewed as dynamic rather than static (Fisher et al, 2002).

Although rolling schedules are common, it is possible that because of production lead times, the

schedule must be frozen for a certain number of planning periods. This means that decisions over

some collection of future periods cannot be altered. The most direct means of dealing with frozen

horizons is simply to label as period 1 the first period in which decisions are not frozen (Hadley,

2002).

4. Treatment of demand: as noted above, aggregate planning methodology requires the

assumption that demand is known with certainty. This is simultaneously a weakness and a

strength if the approach. It is a weakness because it ignores the possibility (and, in fact,

likelihood) of forecast errors. It is virtually a certainty that demand forecasts are wrong.

Aggregate planning does not provide any buffer against unanticipated forecast errors. However,

most inventory models that allow for random demand require that the average demand be

constant over time. Aggregate planning allows the manager to focus to the systematic changes

that are generally not present in models that assume random demand. By assuming deterministic

demand, the effects of seasonal fluctuations and business cycles can be incorporated into the

planning functions (Hiller and Lieberman, 2000).

Costs in Aggregate Capacity Planning

As with most of the optimization problems considered in production management, the goal of the

analysis is to choose the aggregate plan that minimizes cost. It is important to identify and

measure those specific costs that are affected by the planning decision (Abernathy and Wayne,

2004).

1. Smoothing Cost. Smoothing costs are those cost that accrue as a result of changing the

production levels from one period to the next. In the aggregate planning context, the most

salient smoothing cost is the cost of changing the size of the workforce. Increasing the size of

the workforce requires time and expenses to advertise positions, interview prospective

employees, and train new hires. Decreasing the size of the workforce means that workers must

be laid off. Severance pay is thus one cost of decreasing a decline I worker morale that they

result and (b) the potential for decreasing the size of the labor pool in the future, as workers who

are laid off acquire jobs with other firms or in other industries (Becker, 2006).

2. Holding cost: holding costs are the costs that accrue as a result of having capital tied up

in inventory. If the firm can decrease its inventory, the money saved could be invested

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elsewhere with a return that will vary with the industry and with the specific company. (A more

complete discussion of holding costs is deferred to the next chapter.) Hold costs are almost

always assumed to be linear in the number of units being held at a particular point in time. We

will assume for the purposes of he aggregate planning analysis that the holding cost is expressed

in terms of dollars per unit held per planning period. It will be also assumed that holding costs

are charged against the inventory remaining on hand at the end of the planning period. This

assumption is made for convenience only. Holding costs could be charged against starting

inventory or average inventory as well (Hiller and Lieberman, 2000).

3. Shortage costs. Holding costs are charged against the aggregate inventory as long as it is

positive. In some situations it may be necessary to incur shortages, which are represented by a

negative level of inventory. Shortages can occur when forecasted demand exceeds the capacity

of the production facility or when demands are higher than anticipated. For the purposes of

aggregate planning, it is generally assumed that excess demand is backlogged and filled in a

future period. In a highly competitive situation, however, it is possible that excess demand is

lost and the customer goes elsewhere. This case, which is known as lost sales, is more

appropriate in the management of single items and it’s more common in retail than in a

manufacturing context.As with holding costs, shortage costs are generally assumed to be linear.

Convex functions also can accurately describe shortage costs, but linear functions seem to be

the most common (Hadley, 2002).

4. Regular time costs. These costs involve the cost of producing one unit out-put during

regular working hours. Included in this category are the actual payroll costs of regular

employees working on regular time, the direct and indirect costs of materials, and other

manufacturing expenses. When all production is carried out on regular payroll costs become a

“sunk cost,” because the number of units produced must equal the number of units demanded

over any planning horizon of sufficient length. If there is no overtime or worker idle time,

regular payroll costs do not have to be included in the evaluation of different strategies (Hadley,

2002).

5. Overtime and subcontracting costs. Overtime and subcontracting costs are the costs of

production of units not produced on regular time. overtime refers to production by regular-time

employees beyond the normal work day, and subcontracting refers to the production of items by

an outside supplier. Again, it is generally assumed that both of these costs are linear (Fisher et

al, 2002).

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6. Idle time costs. The complete formulation of the aggregate planning problem also

includes a cost for underutilization of the workforce, or idle time. In most contexts, the idle time

cost is zero, as the direct costs of idle time would be taken into account in labor costs and lower

production levels. However, idle time could have other consequences for the firm. For example,

if the aggregate units are input to another process, idle time on the line could result in higher

costs the subsequent process. In such cases, one would explicitly include a positive idle cost

(Fisher et al, 2002).

Capacity Planning and Utilization

We focus primarily on techniques for determining the capacity requirements implied by a

production plan, master production schedule, or detailed material plans. One managerial problem

is to match the capacity with the plans: either to provide sufficient capacity to execute plans, or

to adjust plans to match capacity constraints. A second managerial problem with regard to

capacity is to consciously consider the market place implications of faster throughput times for

making products, at the expense of reduces capacity utilization. For example, JIT production

results in very fast throughout times for manufacturing products, but typically some capacities

are underutilized. Similarly, by scheduling the highest priority jobs through all work center –

taking explicit account of available capacity – it is possible to complete these jobs in much

shorter times than under more conventional MPC approaches. But this gain in speed for high

priority jobs comes at the expense of lower priority jobs throughout times and some

underutilization of capacity (Berry, Schmitt and Vollman, 2004).

This section is organized around five topics:

i) The role of capacity planning in MPC systems: how does it fit and how is capacity

managed in various manufacturing environments?

ii) Capacity planning and control techniques: How can capacity requirements be estimated

and capacity utilization controlled?

iii) Scheduling capacity and materials simultaneously: How can finite scheduling techniques

be applied, and what are the costs/benefits of these techniques?

iv) Example applications: How are techniques for capacity planning applied and what are

some best practices? (Karmaker, 2009).

Some of the techniques developed in this chapter are closely analogous to approaches for

demand management and operations planning. Finite loading techniques and the theory of

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constrains are described here as forms of capacity planning to produce detailed schedules

(Charkravarty and Jain (2000).

2.3.9 Capacity Planning Theory

There is a classic dilemma in maintenance work. If the maintenance people are busy the place is

not earning money. If they are not busy they are usually first on the redundancy list. Scheduling

of maintenance work exists against a background of unusual breakdowns, which have to be

accommodated in a hurry. The only 100% reliable way of managing this situation is to have

spare capacity either through sub-contracting or through re-deploying maintenance personnel to

other duties when not busy. This is very difficult unless routine scheduled maintenance

predominates. Another problem is the lack of outline scheduling information (standard methods

and times) for non-routine operations. A typical problem of this type of work measurement is the

establishment of loose standards, which if used to drive incentive schemes gives rise to serious

problems. As an aside: incentive schemes are no substitute for good supervision. However rule

of thumb time estimates and Rough Cut Capacity Planning is possible. Skills are the usual

resources that need to be scheduled, not plant. If Total Productive Maintenance is being utilized

scheduling becomes simpler because a higher proportion of the work is scheduled rather than

breakdown dominated (SM Thacker Associates, 2012).

Capacity Control

This again is a classic dilemma. Do we do the urgent first or the very urgent? Frequently a job

will be shelved to accommodate a more urgent one. This process can degenerate into very

cluttered workshops and high work-in-process stock holding. Running a strict good

housekeeping regime of operations control can alleviate this. The only satisfactory way to avoid

building unwanted work-in-process is to sue a simple form of input/output control i.e. do not

issue another job until the last is out of the way. One way which we have used to control work in

process is to restrict the number of work or kitting trolleys to one per individual so that they can

only be working on one job at a time. The trolley is used as a Kanban to request the next job

from stores, when the previous job has been started. Also it is common to hold some sub-

assemblies in work-in-process. Unless these require significant lead-time to assemble it is hard to

justify holding sub-assemblies and this situation often leads to cannibalizing one job to make a

more urgent job. Our advice is do not do it unless you really have to, and draw a Commonality

Tree to assess the need (SM Thacker Associates, 2012). The use of loading boards is common in

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this environment. More recently electronic loading boards with pick and place facilities are being

used. Skill management may be very important to maintain.

Other Important Aspect

Tools management is essential with Shadow Boards used to ensure tools can be located when

needed and in safety critical situations such as aircraft assembly it ensures that they are not lost.

The use of housekeeping techniques such as 5S’s is appropriate. Diagnostic skills and possibly

tools are required. These may be required to support remote diagnostic. Considerable effort may

be required to establish this infrastructure (SM Thacker Associates, 2012).

2.3.10 Theories of Performance

Performance occupies a key interface between organization behaviour, strategy and international

management. In organization behaviour the position of performance in the structural contingency

theories and research studies was marginal.

Organization behaviour is at the leading edge in developing a more substantial understanding of

performance. The structural contingency theory requires extensive revision. There are two major

areas of revision. First, to account for the hidden impacts on performance of the national context

of the firm. The hidden aspects include the roles of actor endowments (for example, raw

materials), the institutions and the market characteristics (for example, size, homogeneity and

speed of saturation). These hidden aspects impact on the performance of firms by creating a zone

of manoeuvre. Firms have to be aware of the zone, yet can enroll elements in the context which

reshape the zone. Second, it is important to be aware of the differences in approach between the

practices of auditing performance within firms from the concepts and theories used in

organization behaviour. Within firms of all kinds – public and private, commercial and custodial

– there are extensive arrays of performance data covering very diverse aspects. The financial

dimensions of the array are highly influential in constituting the recipe knowledge about strategic

directions. The influence of accountancy on the everyday understanding of performance is

significant, but should be closely scrutinized. The aim is to develop a theory which links

organizational learning to the selective usage of performance measures, in particular, to explain

the role of intangible assets but undertaking these revisions is a major challenge (Sorge, 1991).

67

Key Interface

There are six reasons why performance is a key interface. First, there has been a significant shift

in the definition of best practice organization from mass standardization into mass customization

(Pennings, 1975). The practices of large US firms were regarded as the only relevant exemplars

worth emulating. The international success of firms from Japan, the Pacific Rim and Germany

demonstrates that definitions of best practice should include features which are found in those

nations. Second, political elites and corporate leaders give increasing attention to the use of

market and quasi-market mechanisms. This stimulates the auditing of performance for the parent

organizations and its sub-units. The latter are placed in quasi-market situations. This reflects the

shift from a producer-anchored capitalism to one in which consumerism is central. Third,

information technology greatly facilitates the collection of data about performance. Computer

modeling and the application of multi-dimensional frameworks display the complex way in

which various processes contribute to performance. Fourth, the professional associations

connected to accounting and to information services gain high fees and rents from developing

and diffusing measures of performance for a wide range of organizations. Fifth, firms wish to

develop their own recipe knowledge about the dynamics of performance. Finally, external

stakeholders monitor selected dimensions of performance. Ecological interests monitor the level

of pollutants and the use of scarce resources (Pennings, 1975).

Structural Contingency Theory

Organization behaviour is influenced by the forms of law-like knowledge constituted in

economics. Industrial economics concentrated upon the strategic relationship of the firm to its

economic context, especially in the choice of sector in the positioning of the firm within the

sector (Porter, 1990). Economics treated the organization structure, key processes and the

transactions with the context as a black box. Opening the black box began in the late 1950s

(Pennings, 1975; Burns and Stalker, 1994). Organization behaviour was founded on the claim

that its structural contingency theories of organization design provided a highly effective

approach to achieving high performance (Nystrom and Sharbuck, 1986).

In the late 1950s there were two contending theories to explain the differences between

successful and unsuccessful performance as assessed by survival and profitability. One theory

emphasized universal solutions to be applied everywhere. The autonomous work group was

widely promoted for every kind of organization and social movement (for example, kibbutz).

The alternative theory was derived from systems thinking. The theory of equifinality shows that

68

high performance is achieved by different routes. Some firms might decentralize decisions while

also increasing the degree to which procedures were formalized. Other firms could transfer

control of professional specialists (Pennings, 1975).

Organizational behaviour established a third approach by selectively adapting systems thinking.

The contingency and congruence fit perspective specified the conditions under which given

structural solutions led to varying performance. Burns and Stalker (1994) connected the viability

of any organization to its ability to match the degree of variety in its environment with internal

mechanisms for encoding the variety and activating solutions from its repertoire. Their theory of

performance is evolutionary. Firms that chose strategies and structures without congruence to

their environment with underperform.

After the 1970s the connection between organization behaviour and strategy was established.

The linear, four-step and quasi-rational approach is an outside-in approach. From the

organization behaviour perspective the environment is analysed to ascertain the degree of

complexity and ambiguity and their stability or otherwise in the future. This information guides

the design of organization structure. The design of organization is also shaped by strategic

decisions, because those will influence features such as the economies scale and scope. In the

strategy version (version 3) the environment is analysed by searching for those sectors which are

expected to provide the most favourable sources of profits in the future. The pharmaceutical

sector was desirable in the 1980s, but less so in the 1990s. Within any sector there are wide

variations in profitability. The aim is to select a position. Key choices are whether to be a firm

which provides low-cost commodities or provides items which are differentiated from one

another. Currently the strategic approach is being challenged by organization studies (Pennings,

1975).

Six problems arise with the assumptions underpinning Figure and its usage of structural

contingency approach to performance. First, the approach assumes that firms can move from

sector to sector without friction, in a manner similar to the economists’ theory of frictionless

adjustments of the markets. In practice most firms can only alter their repertoire rather slowly:

television producers cannot easily move into tourism. Second, the approach assumes that

knowledge about the best positioning to achieve high performance will be acceptable to the

political groupings. Yet, firms are milieus of political bargaining and are set in contexts where

external stakeholders may exert influence). For example, the credibility of a firm to its financial

69

community is crucial. Third, performance is treated as end-variable with only small feedback

inputs into the state of the firm. Performance is and should be a source of scrutiny, sense-making

and the foundation for developing new strategies. Past performance is the foundation for

developing new strategies. For the four sequential steps are too rigid and do not allow for

iterations, abortions and variations in the decision process. Fifth, the treatment of the external

context is too narrow (Nohria and Eccles, 1992) and too close to the immediate context of the

firm. There is neglect of impact of symbolic forces and of the hidden influence of the national

context (Sorge, 1991). Sixth, the measurement of performance in research studies intended to

support the structural contingency perspective is mainly based upon financial data (for example,

profits) measured as a cross-sectional slice describing the past. Reviews typically conclude that

research studies relied upon inconsistent operational definitions and simplistic measurements

(Pennings, 1975).

The limits of the structural contingency theory of performance are handled in two major

revisions. First, to include the national context. Second, to develop a processual, learning theory

of performance.

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Figure 2.1: System Theory of Performance

Source: Pennings, J.M. (1975), “The relevance of the structural-contingency model for

organizational effectiveness’, Administrative Science Quarterly 20 (3)

Analysis of

environment

Strategy

formulation

Organization

design and

implementation

Desire

performance

Levels of

uncertainty,

complexity and

variability

Design of

organization

Implementation

of design

(Assumed) Desire

performance

Economic data for

national and world

Structural patterns

New technologies

Profit streams

Choice of sectors to

enter and to

position in sector

Firm as a portfolio

(Assumed that

the firm adjusts

the organisation)

Desire

performance

1

2

3

Key 1. Four steps in model

2. Version of model

found in organization

studies

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Hidden Influence Theory

Variations in the performance of firms are explained by features within the national theories. The

national context provides an envelope of opportunities and constraints. It is unlikely that Henry

Ford could have achieved such large sales for the Ford T between 1908 and 1926 if he had

started in the UK or France. It is also unlikely that the successful performance of the Italian firm

Benetton could have been achieved after 1970 if they had started up in the UK because of the

existing structure of the market for clothing. To survive and grow all firms require an envelope

of opportunity within which there are the necessary resources and markets for the outputs. These

envelopes tend to influence clusters of firms at the level of the sector. There are two theories

which reveal the hidden contextual factors in the nation of origin: the theory of societal

institutions and elective affinities (Sorge, 1991); the six-factor theory of competitive advantage

(Porter, 1990).

International comparisons reveal that some sectors do better in certain societies. For example, the

German automobile sector has performed better than the British automobile sector. How

important are the institutions within a nation through which knowledge is brought into play

within firms? The theory of elective affinities (Sorge, 1991) seeks to demonstrate that the

German success and the British failure is explained by the interaction between the institutions of

knowledge management and the strategic directions chosen by a nation’s firms. In the German

case there is a positive elective affinity because the management of knowledge creates highly

competent mechanical engineers, a connected hierarchy of skill within the firm and the strategic

choice of producing cars that are distinctive rather than produced as commodities. The theory of

elective affinities redefines the role of the structural contingency theory. The theory of elective

affinities is not deterministic in the relationship between institutions and firms, leaders

sometimes choose strategic directions which have the worst affinity with the societal institutions.

The theory of the competitive advantage of nations (Porter, 1990) locates the societal institutions

of knowledge management in the context of six interacting factors: the endowed factors, the

degree of rivalry between firms, the role of government, chance, the role of support sectors (for

example, design, and marketing) and the influence of the national market. The role of the

endowed factors deserves attention because their absence or presence exerts an influence.

Endowed factors include: raw materials, soil, rivers and climatic features. The USA has a

historical abundance of endowed factors for agriculture, but a historical shortage of labour

(compare Japan). In the USA there were many kinds of hard and soft wood which could be used

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for many purposes: making firs, building houses, fences, making equipment. One of the early

developments in the USA was the fast running saw-milling machines which wasted vast

quantities of wood and substituted technology inputs for labour inputs. These early developments

to technology became cumulative and defined a trajectory or path of development (Sorge, 1991)

The influence of the national market is worthy of close examination. National tastes differ

significantly. The Japanese prefer household consumer goods which are small, multi-functional,

portable, possess fine surfaces, are packaged delightfully and are accompanied by extensive

information. The Japanese market for consumer goods tends to be homogenous, very conscious

about newness and quickly saturated. To survive, the Japanese firms have created many variants

of products (for example, Sony Walkman) through short design cycles (Sorge, 1991)

Organizational performance is significantly shaped by the national context. Domestic firms

experience endowed factors, using their domestic market as a major context for learning, and

tend to become shaped to its characteristics. This process is referred to as ‘entertainment’ and its

influence affects the ability of firms to cross the borders from their nation of origin. There are

zones within which firms can choose to manoeuvre, and to some degree firms can reconstitute

features which might be unfavourable in the longer run. The ability of firms to manoeuvre is

influenced by how they learn from their performance.

2.3.11 Theories of Manufacturing

Manufacturing and national economies

Wealth and manufacturing theory

Wealth may be categorized into two types; namely: natural wealth and man-made wealth.

Natural wealth is derived from crude materials, that is, materials occurring in the natural state

such as mineral deposits in the earth’s crust and agricultural products. Natural wealth especially

that based on mineral deposits is delectable. Also, wealth obtained from agricultural products

without man-made inputs is unsustainable in modern times. Natural wealth is fate-dependent and

its location can hardly be influenced by man. On the other hand, man-made wealth is one derived

from refined or manufactured products, in which man exercises enormous control. This type of

wealth is usually sustainable. In this case, the wealth usually is created by manufacturing

(Ibhadode, 1993).

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Manufacturing is concerned with the production of goods, where a good is a tangible entity,

Vollmann et al, 2000). Manufacturing is undoubtedly one of the most important sectors of

national economies, as it creates wealth. The tendency is most societies are to create more

wealth. And the more volume of manufacturing activities, the more plentiful the wealth produced

(Vollmann et al, 2000).

Manufacturing is a productive system that may be defined as a process for converting resource

inputs into goods and waste products. The inputs to the system are energy, materials, equipment,

labour, information and other capital-related inputs (infrastructure). The inputs are converted to

outputs by the process technology, which is the particular method used to transform the various

inputs into outputs. Changing the process technology alters the way one input is used in relation

to the other and it may also change the outputs produced.

The outputs produced include useful products (goods), and waste products. Waste production is

inevitable as a consequence of the generalized results of the second law of thermodynamics. It is

characteristic of processes including human life that they take in suitable raw materials and

convert them into products of value. In doing so, they must produce waste materials (Ibhadode,

2006). This is inevitable even under clean technology.

The process technology in a manufacturing system is usually effected by means of machines and

equipment along with the processing methodology. The body of knowledge concerned with the

optimal combination of machinery, materials, and methods needed to achieve economical and

trouble-free production is referred to as manufacturing engineering (or manufacturing technology

when used loosely and interchangeably). It combines field experience and special engineering

research with concepts of fundamental and applied sciences to solve basic and specific

manufacturing problems. Manufacturing technology techniques are of immense importance to

modern industries where inconceivable machines are produced from elementary materials such

as blanks using basic specific manufacturing processes. In conjunction with engineering

management techniques, it ensures the most efficient use of materials and labour. It curtails

wastes and ensures the use of the right processes for each operation in the production of a

product or component. It also ensures the most effective method of handling and assembly of

components to form specific products (Ibhadode, 2006).

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Importance of Manufacturing to National Economies

Engineering manufacture is, undoubtedly, one of the most important sectors of industry. It

provides machines of different purposes to the economy. The economic and industrial growth of

a nation is largely dependent on the development of engineering industry. Food, clothing, shelter

and all the benefits of civilization determine how well a people live. How well a people live

depends on how much it produces is determined by its level of manufacturing activities

(Ibhadode, 2006).

The global economy has become knowledge and technology-driven. While innovation and rapid

technological changes are the reasons for unprecedented prosperity and growth in industrialized

countries, many developing countries and countries with economies in transition are risking

marginalization by being trapped in the technology-divide and investment gap. Research and

development (R&D) and innovation-intensive products are increasingly driving world trade.

According to a UNIDO report in 1998, high – and medium – technology products accounted for

63.6%. 67.8% and 53.8% of manufactured exports of world developed economics and

developing economies respectively. Regrettably they accounted for only 12.7% of manufactured

exports from Sub-Sahara African countries. This poses serious industrial and economic

development challenges to the Sub-Saharan region (World Bank, 2008).

To prove further that manufacturing drives the economy, despite the endowment of large

reserves of oil in the major oil producing countries, their GDP, per capita income, per capita

value added in manufacturing and longevity are 3%, 14%, 7% and 28% respectively of those for

the G7 countries. The contribution of manufacturing to the GDPs of the major oil producing

countries is at about the same level as for the world’s 20 poorest countries (World Bank, 2008).

The Manufacturing Sector in Nigeria

The Nigerian economy is in a precarious state. The manufacturing sector seems to be hardest hit.

While the mean contribution of manufacturing to GDP in the world’s 20 poorest countries was

9% in 2003, that of Nigeria was only 4%. Further, whereas the world’s 20 poorest countries had

a mean per capital value added in manufacturing in 2003 of $22, Nigeria had only $16. The

picture is even worse when Nigeria is compared with the 5 oil-producing nations.

The Manufacturers Association of Nigeria (MAN) has given the current status of the

manufacturing sector in Nigeria and is summarizes as follows:

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i. The contribution of manufacturing to the Gross Domestic Product (GDP) has been on

persistent decline over the years from 8.2% in 1990 to 4.7% in 2003.

ii. At independence in 1960, the contribution of the manufacturing sector to GDP was

3.8%. Notwithstanding the growing contribution and dominance of the oil sector

since the 1970s, manufacturing recorded impressive performance and contributed on

the average was consistently above the 70% mark. The situation has changed

dramatically. Industrial capacity utilization dropped to a paltry 48.8% in 2003.

Currently, 30% of manufacturing companies have been closed down, 60% are ailing

and only 10% are operating at sustainable level. Sectorially, the companies operating

at sustainable level are food, beverages and tobacco; leather, pharmaceuticals and

Household products. (Soaps, detergents, toothpastes, cleaning materials). Companies

in the ailing category include Textile firms, Vehicle assemblers, cable manufacturers,

paint manufacturers, steels and petrochemicals (Ibhadode, 2006).

Companies in the ‘closed down’ category cut across all industrial products but most affected are

products such as chalk, candle, dry cells and automotive batteries, shoes polish, matches, etc.

MAN gives the following as constraints to the manufacturing sector:

i. Sector is highly importing dependent.

ii. Hampered by policy inconsistencies.

iii. Besieged with multiple taxation.

iv. Burdened with weak infrastructural base and ineffective public utilities.

v. Tormented by acute funding problems, weak capital base as well as high cost of fund.

vi. Inundated with fake, counterfeit and substandard imported products.

vii. Burdened with poor sales partly as a result of low purchasing power of the citizenry.

viii. Bugged down with delay in clearing consignments due to existence of multiple

inspection agencies at the ports (World Bank, 2008).

This state of affairs is lamented! Engr. Charles Ugwuh, President of MAN, has said that the

manufacturing sector is ‘threatened with collapse due largely to deficiencies in infrastructure,

lack of appropriate funding and other policy inconsistencies and frustrated implementation of

otherwise well intentioned strategies” he added that “the sector can make enormous contribution

to the growth of Nigeria if the necessary drivers and vital investment in energy, petrochemicals

and human capacity can be made to uplift the level of value – addition’ Furthermore, the

Engineer advised that: “the federal Government must muster the courage to make the vital

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investments (in partnership with the private sector) that add value to our natural resources of

crude oil, gas and solid minerals in a way that multiplies the national wealth so all Nigerians can

share the prosperity within our reach. He summed up with the epigram:

“Poverty has no place in Nigeria”.

Re-Manufacturing

1. Materials Planning

This breaks down into 3 parts:

1) Managing the supply of units awaiting salvage

i) Sufficient stock must be maintained to support underlying demand for

reconditioned units.

2) Managing the stripped component stock to keep balanced sets of parts for rebuild

(Vollmann et al, 2000)

i) Using new items instead of salvaged items is costly. So in order to

maintain components it may be necessary to strip further units. Yields

must be used as an input to calculate material requirements. Ultimately

imbalances are bound to occur. In this case an occasional purge may be

required to restore the balance, by either throwing away surpluses or

buying new components depending on the economics of doing so

(Vollmann et al, 2000).

3) Managing the rebuild

i) Because there is a greater volume, medium to large batch rules apply with

many similarities to original production / assembly operations. Forecasting

is easier and there is more repetition. Systems may be appropriate where

demand can be forecast with some certainty. We have encountered

situations were a negative bill of material was constructed to

accommodated yields expected from salvaged units which were then

offset against the requirements for remanufacture. This method was later

abandoned in favour of change of manufacturing strategy where salvaged

units were stripped as soon as possible to determined availability of good

components.

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4) Generally Re-order Point techniques are most appropriate for forecasting demand,

with blanket orders / schedules for repetitious component requirements (SM

Thacker Associates, 2012).

2. Materials Control

Call offs are more likely to be via Kanban control because of increased repetition. It is

vital to monitor yields in this situation to ensure that the correct numbers of un-salvaged

stocks are sufficient to satisfy demand. Re-manufacturing creates a special problem for

lot traceability. If a part has been recycled and it fails, what is the cause of the failure? Is

it the original manufacture or the recycling process?

3. Capacity Planning

Because more time standards on work content are available (however informally)

estimating jobs is easier. Because processes are more predictable Routes (Routings) can

be established to use in shop loading. Because there is repetition, demand is also

smoother. The combined effect of these factors makes capacity planning easier. The use

of level scheduling is recommended (SM Thacher Assocaites, 2012).

4. Capacity Control

Because demand is smoother and more repetition is present, skills management is less

important and in fact more deskilling or automation may be possible. Switching effort to

stripping rather than rebuilding can accommodate troughs and conversely reducing

stripping to satisfy immediate demands can accommodate peaks SM Thacher Assocaites,

2012).

5. Other Important Aspects

Sometimes the organization may be slightly schizophrenic, flipping from job shop to

volume producer. At this point it is worth considering some method of segmentation

along resource utilization lines.

2.4 EMPIRICAL REVIEW

Empirical Review of the effect of Capacity Planning on Performance

Bell et al (2002) worked on how to improve the distribution of industrial gases with online

computerized Routing. He observed that distribution was very important in materials

management. Other important materials management activities included, purchasing, production

and inventory control, storage and warehousing and distribution. Distribution had to do with

transportation of raw materials, parts, sub-assemblies, semi-finished goods and finish goods from

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the point of production until they get to the final consumers. He found that the starting point in

distribution is to know the capacity or the production capability of the goods that are being

transported. Capacity planning became very important in distribution if corporate objectives had

to be achieved so capacity planning as a distribution function had a positive effect on

performance in many goods industries.

Relationship between capacity requirements planning and materials requirements

planning, using work in progress

Karmarker (2009) worked on capacity loading and Release Planning with work in progress and

lead times. The main objective of his study was to determine the nature of the relationship

between capacity requirements planning materials require planning using work in progress.

Materials requirements planning is a method of coordinating detailed production plans and it is a

multi-stage process which begins with a master’s schedule and works backwards to determine

when and how much components will be needed. It gives the time for placing orders and when

the other aids is required considering the limited time.

Capacity requirements planning give the capacity of the materials that are required both at the

ordering and receipt stages. It utilizes the time-faced materials plan information produced by a

material requirements plan system. This includes consideration of all actual lot sizes as well as

lead times for both open-shop orders (scheduled receipts) and others plan for future release

(planed orders). Karmerker found that there was a positive relationship between capacity

requirements planning and materials requirements planning in the manufacturing industry in Los

Angeles, United States of America.

The extent to which capacity planning sustains organization’s competitive advantage

Whelan (2012) did a study on who to initiate capacity planning and management process for a

rapid deployment unit of a security services company. Support Services Group Limited was

founded in 2000 to provide security and risk management services for companies within the

United Kingdom. The enterprise has encountered rapid development and growth which lead to

establishment to Rapid Deployment Service Product. The product was created to answer sudden

and volatile demand by correlating supply.

The capacity for the supply has not been planned or managed coherently creating a call for a

research on how such as process could be initiated. The purpose of the thesis is to determine the

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extent to which capacity planning sustains the organization’s competitive advantage. The

research was carried out as case study. The database of the company provided the information

which was then analyzed using a mix of both qualitative and quantitative methods. It was found

that to a large extent capacity planning sustains the organization’s distinctive competence which

is the competitive advantage which the company has over its competitors.

The relationship between capacity planning and capacity building

S.M Thacher Associates (2012) did a study on the extent of the relationship between capacity

planning and capacity building. Manufacturing Planning and Control (MPC) is often seen as

encompassing two major activities: planning/control of materials and planning/control of

capacities. The two need to be coordinated for maximum benefits, on the basis of managerial

perceptions of what is required in the marketplace. Capacity planning techniques have as their

primary objectives the estimation of capacity requirements, sufficiently far enough into the

future to be able to meet those requirements. A second objective is execution: the capacity plans

need to be executed flawlessly, with unpleasant surprises avoided. Insufficient capacity quickly

leads to deteriorating delivery performance, escalating work-in-progress inventories, and

frustrated manufacturing personnel. On the other hand, excess capacity might be a needless

expense that can be reduced. Even firms with advanced MPC systems have found times when

their inability to provide adequate work center capacities has been a significant problem. On the

other hand, there are firms that continually manage to increase output from what seems to be a

fixed set of capacities.

In the case of capacity building, the intention is to provide an enabling environment to enable

capacity planning to be properly handled. This will involve the factors and variables that will

enable the managers to be able to determine the present and future production capability of the

facility. In their study, S.M. Associates found out that to a large extent, there was a positive

relationship between capacity planning and capacity building in a case study of a manufacturing

firm in Los Angeles.

Steps towards developing capacity plan to improve the profitability in the manufacturing

sector

Farwell (2012) did a study to determine the steps of capacity planning to improve the

profitability in a manufacturing industry in a study Ohio in the United States. The steps included

determining the present capacity needs of the company, comparing the present and future

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capacity needs. If the present capacity needs are more than the future needs, then the company

could do so contracting out or outsourcing of some of its jobs. But if the future capacity needs

are more than the present capacity needs, then the company could run shifts, increase capacity or

build a new plant.

The last step is to implement its decision and Farwell (2012) found that there was a positive

relationship that the steps towards developing capacity plan if properly implemented could

improve the profitability in the manufacturing industry in areas studied.

2.5 CAPACITY MANAGEMENT AND PLANNING

The most successful events and actions in the world are primarily the outcomes of coherent and

linear planning. In order to excel, one must point out the steps towards success with awareness.

The actualization of competences, resources and capabilities will widen the viewpoint so it

comes clear where the development leads to and which tools are to be possessed and/or used at

which point of the way.

Capacity planning is the perspective of businesses to map out their capabilities. Therefore,

capacity planning is the one of key performance elements of a functional business. When

executed through careful and considerate calculations, capacity planning can be the sole

ingredient which would make the profits of an enterprise boom. However, companies which do

not focus enough on management of strategic capacity planning will encounter serious

difficulties or even disastrous problems especially during a growth or starting period.

Strategic Capacity Planning

The word capacity normally defined in Business dictionary as “specific ability of an entity

(person or organization) or resource, measured in quantity and level of quality, over an extended

period.” (Business Dictionary.com, 2011)In other words, capacity refers to the skill to hold,

receive, store, or accommodate. In general business logic, it’s often viewed as the amount of

output that a system is capable of achieving over specific period of time. (Jacobs and Chase,

2008)

Capacity also refers to the limitation which the operating element is able to process; the amount

of services executed or tangible products produced. The vital elements and considerations

needed to be taken into account before-hand are what type of capacity – whether it’s equipment,

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space or human skills – are needed, how much of it is required and the timeframe of when those

factors are to be accessible. (Beamer, 2010)

Professors F. Robert Jacobs and Richard B. Chase (2008) define strategic capacity planning as an

approach tactic which is the idea of determining the total levels of capacity for resources

mentioned above. The common feature of these resources is the scarce and the finite nature,

which is normally described as “capital-intensive” but also the availability in terms of – often

placed secondary in importance – time reflecting the reasons why planning is necessary. (Jacobs

and Chase, 2008; Hope and Muhlemann, 1997)

Purpose of Capacity Planning

The main point for an organization to perform plan capacity usage in advance is to match its

supply competence and capability levels with the predicted demand by the customer. Capacity

plan is formed to support the company’s main competitive strategy and it has to be inline and

correlate with it. The accuracy of the capacity plan is in sync with the company’s ability to

actualize their capabilities enabling them to have precise respond to the needs of the customer.

Should the situation be so that the demand is too excessive, through a detailed plan it is easy to

seek out the required steps which are to be done in order to satisfy the demand. Insufficient or

otherwise inadequate capacity may turn out to be costly for the company as unpleased customers

are lost and such a market attracts competition faster.

Capacity measurement definition

Capacity as a term is in directly aimed at the rates of output of the operations in question. The

output is normally indicated through a rate which presents the amount of deliverables completed

in a period of time. For a small pub, a fairly demonstrative measurement could be for instance

drinks sold in a day. The actual output rate gives a mere indication of the daily result. In order to

assess the rate further to determine the actual effectiveness, two capacity efficiency performance

indicators are to be used. Those indicators alongside the formula are presented in the equation

below:

capacityDesign

output Actual n Utilizatio

capacity Effective

output Actual Efficiency

=

=

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The efficiency ratio expresses the give day output of the pub in correlation to the best possible

daily rate. The effective capacity is a measure which the process was designed for, but which can

be realistically expected as a result; while taking into consideration miscellaneous factors which

keep the process reaching its peak due to their inevitability. Examples of such in the given

surrounding could be maintenance, personnel breaks, etc. Design capacity is the best possible

level for an operation, process or a facility to deliver. In brewing, the design capacity reflects the

volume of output with a minimized cost of an average unit. Ideally, the design capacity – also

known as best operating level – would be in direct link to productivity but as various

uncontrollable reasons prevent operations to reach maximum capacity, it is vital to use both

indicators as results lead to different interpretation. Both of the ratios are normally expressed in

percentages and give an idea of improvement needs. Efficiency ratio shows how effective we are

in terms of productivity whereas utilization ratio indicates the need for improvement within the

process itself. Evidently, where there are bigger the gaps – the resulting number being a lot less

than 100% - between the figures, lie a greater opportunity for an improvement. Low percentage

value in efficiency indicates insufficient variable resources such as employees who required

more training or orienting and a low value in utilization indicates the problems within the actual

process for instance the bar tap needing maintenance frequently. (Jacobs and Chase, 2008;

Beamer, 2010)

Capacity decisions

By their nature, capacity decisions are generally strategic involving investments and therefore

commitment in resources such as equipment, buildings and manpower. In light of this factor,

capacity decisions affect greatly into a myriad of organizational functionality. These decisions

have an enormous impact on the ability to meet the future demands for the goods an organization

is offering. Costs are widely influenced by capacity decision as operating costs are larger when

there are investments in resources. Additionally, the initial cost of the product is determined by

the unit cost which is normally a direct derivation from the costs of the capacity used. Other

areas which are affected are the ease of management; better capacity, easier to manage, and

competitiveness of the company. Coming to the 21st century, globalization has added its share

into the capacity decision mix by highlighting the importance as the markets and competitors are

operating in a global scale and increasing the complexity. All these reasons emphasize the need

to plan these crucial choices in advance. Capacity decisions can be divided into three categories.

Long-term, medium-term and short-term. (Hope and Muhlemann, 1997; Beamer, 2010)

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Long-term capacity decisions

Long-term capacity decisions are made in a timeframe of greater than a year. Including top

management participation, long-term plans concern productive resources which take a longer

time to acquire and/or dispose. These resources include for instance buildings and facilities.

Because of the gravity of these choices, they are to be done in the knowledge of the external

factors which could affect the decision-making. Such components are markets, major

competitors and PEST (political, economic, social and technical) environment. Long-term

capacity decisions are made to either increase or reduce capacity.

Medium-term capacity decisions

Whereas long-term capacity decisions can be considered as the macro viewpoint of capacity

planning, medium-term takes care of the micro view. The timeframe here is from 6 to 18 months.

However, depending on the organization medium-term and short-term capacity decisions do not

have a clear separation but are carried out while linked to each other. Therefore, medium-term

range can include timescale as specific as weeks. The focus point here is on “softer” resources

which include human resources and minor equipment purchasing. Decisions made within this

concept are executed in order to match the supply with demand. To do this, the company can

either adjust the supply of resources or the demand. (Jacobs and Chase, 2008; Hope and

Muhlemann, 1997)

Adjusting the demand is highly complex activity. Sudden changes in demand are somewhat

impossible to foresee. Organizations can try to manipulate demand through marketing.

Aggressive commercials entice people in to consuming more. Other attempts to affect the

demand could be two-for-one offers at restaurants during a specific day of the week, which try to

lure in customers when business is usually slow. The practice is known as yield management;

predicting and altering demand to maximize revenue. Yield management is applied in situation

which required determining the best possible price and timing of a capacity to the most suitable

customer segment. Offering a family holiday week at a resort during school summer vacation at

a price affordable by most families with children to get peak levels in sales is a fine example of

yield management (Hope and Muhlemann 1997; Jacobs & Chase, 2008).

Possibilities to adjust capacity to meet the demand are deeply associated with the flexibility of

the resources. Flexibility refers to the ability of the organization’s capacity to adapt to changes;

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multi-skilled employees, overtime, having alternatives to material resources or speeding up the

use of physical resources (libraries limiting the use of computer/internet per customer).

Concentration on one aspect of resources being flexible might not be the best choice to act by.

Most brewing companies perform a mix of human, physical and material resources in terms of

flexibility.

Short-term capacity decision

As mentioned before, short-term capacity decisions are strongly linked to medium-term

decisions. The timescale is less than one month. The decision might even be made for daily or

weekly scheduling. Short-term capacity decision could be presented as fine-tuning of medium-

term decisions. The capacity, determined and acquired through medium-term plans, is made to

match the demand by eliminating variance between planned and actual output. The flexibility of

the resources are put to test here as sudden changes in demand require rapidly executed moves

through such means as employee overtime and alternative production routines (Jacobs and

Chase, 2008; Hope and Muhlemann, 1997)

2.6 BREWING

Brewing is the production of beer through steeping a starch source (commonly cereal grains) in

water and then fermenting with yeast. It is done in a brewery by a brewer, and the brewing

industry is part of most western economies. Brewing has taken place since around the 6th

millennium BC, and archaeological evidence suggests that this technique was used in most

emerging civilizations including ancient Egypt (Arnold, 2005).

The basic ingredients of beer are water; a starch source, such as malted barley, which is able to

be fermented (converted into alcohol); a brewer’s yeast to produce the fermentation; and a

flavouring, such as hops. A secondary starch source (an adjunct) may be used, such as maize

(corn), rice or sugar. Less widely used starch sources include millet, sorghum and cassava root in

Africa, potato in Brazil, and agave in Mexico, among others (Jacson, 2008). The amount of each

starch source in a beer recipe is collectively called the grain bill.

There are several steps in the brewing process, which include malting, milling, mashing,

lautering, boiling, fermenting, conditioning, filtering, and packaging. There are three main

fermentation methods, warm, cool and wild or spontaneous. Fermentation may take place in

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open or closed vessels. There may be a secondary fermentation that can take place in the

brewery, in the case, or in the bottle.

Brewing specifically includes the process of steeping, such as with making tea, sake, and soy

sauce. Technically, wine, cider and mead are not brewed but rather vinified as there is no

steeping process involving solids.

Malted barley before roasting

The basic ingredients of beer are water; a starch source, such as malted barley, able to be

fermented (converted into alcohol); a brewer’s yeast to produce the fermentation; a flavouring,

such as hoops, (Alabev.com, 2008) to offset the sweetness of the malt (Nachel, 2012). A mixture

of starch sources may be used, with a secondary starch source, such as maize (corn), rice, or

sugar, often being termed an adjunct, especially when used as a lower-cost substitute for malted

barley (Ted Goldammer, 2008). Less widely used starch sources include millet, sorghum, and

cassava root in Africa, potato in Brazil, and agave in Mexico, among others (Jackson, 2008). The

amount of each starch source in a beer recipe is collectively called the grain bill.

Water

Beer is composed mostly of water. Regiosn have water with different mineral components; as a

result, different regions were originally better suited to making certain types of beer, thus giving

them a regional character. For example, Dublin has hard water well suited to making stout, such

as Guinness; while Pilsen has soft water well suited to making pale lager, such as Pilsner

Urquell. The waters of Burton in England contain gypsum, which benefits making pale ale to

such a degree that brewers of pale ales will add gypsum to the local water in a process known as

Burtonisation (Jackson, 2008).

Starch Source

Main articles: Malt and Mash ingredients

The starch source in a beer provides the fermentable material and is a key determinant of the

strength and flavor of the beer. The most common starch source used in beer is malted grain.

Grain is malted by soaking it in water, allowing it to begin germination, and then drying the

partially germinated grain in a kiln. Malting grain produces enzymes that will allow conversion

from starches in the grain into fermentable sugars during the mash process (Wikisource, 2008).

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Different roasting times and temperatures are used to produce different colours of malt from the

same grain. Darker malts will produce darker beers.

Nearly all beer includes barley malt has the majority of the starch. This is because of its fibrous

husk, which is important not only in the sparging stage of brewing (in which water is washed

over the mashed barley grains to form the wort) but also as a rich source of amylase, a digestive

enzyme that facilitates conversion of starch into sugars. Other malted and unmalted grains

(including wheat, rice, oats, and rye, and less frequently, corn and sorghum) may be used. In

recent years, a few brewers have produced gluten-free beer made with sorghum with no barley

malt for people that cannot digest gluten-containing grains like wheat, barley, and rye

(Smagalski, 2006).

Hops

Hops are the female flower clusters or seed cones of the hop vine Humulus lupulus, which are

used as a flavouring and preservative agent in nearly all beer made today. Hops had been used

for medicinal and food flavouring purposes since Roman times; by the 7th century in Carolingian

monasteries in what is now Germany, beer was being made with hops, (Unger, 2007) though it is

not until the thirteenth century that widespared cultivation of hops for use in beer is recorded

(Cornell, 2003) Before the thirteenth century, beer was flavoured with plants such as yarrow,

wild rosemary, and bog myrtle, and other ingredients such as juniper berries, aniseed and ginger,

which would be combined into a mixture known as gruit and used as hops are now used;

between the thirteenth and the sixteenth century, during which hops took over as the dominant

flavouring, beer flavoured with guit was known as ale, while beer flavoured with hops was

known as beer (Nornsey, 2003). Some beers today, such as Fraoch by the Scottish Heather Ales

Company and Cervoise Lancelot by the French Brasserie-Lancelot company, use plants other

than hops for flavouring.

Hops contain several characteristics that brewers desire in beer: they contribute a bitterness that

balances the sweetness of the malt; they provide floral, citrus, and herbal aromas and flavours;

they have an antibiotic effect that favours the activity of brewer’s yeast over less desirable

microorganisms; and they aid in “head retention”, the length of time that a foamy head will last.

The acidity of hops is a preservative (Lewis, 2002). Flavouring beer is the sole major

commercial use of hops.

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Yeast

Yeast is the microorganism that is responsible for fermentation in beer. Yeast metabolises the

sugars extracted from grains, which produces alcohol and carbon dioxide, and thereby turns wort

into beer. In addition to fermenting the beer, yeast influences the character and flavor. The

dominant types of yeast used to make beer are Saccharomyces cerevisiae, known as ale yeast,

and Saccharomyces uvarum, known as lager yeast; Brettanomyces ferments lambics, and

Torulaspora delbrueckii ferments Bavarian weissbier. Before the role of yeast in fermentation

was understood, fermentation involved wild or airborne yeasts, and a few styles such as lambics

still use this method today. Emil Christian Hansen, a Danish biochemist employed by the

Carlsberg Laboratory, developed pure yeast cultures which were introduced into the Cartsberg

brewery in 1883, and pure yeast strains are now the main fermenting source used worldwide

(Burgess, 1964).

Clarifying agent

Some brewers add one or more clarifying agents to beer, which typically precipitate (Collect as a

solid) out of the beer along with protein solids and are found only in trace amounts in the

finished product. This process makes the beer appear bright and clean, rather than the cloudy

appearance of ethnic and older styles of beer such as wheat beers (Lewis and Young, 2002).

Examples of clarifying agents include isinglass, obtained from swimbladders of fish; Irish moss,

a seaweed; kappa carrageenan, from the seaweed Kappaphycus cottonii; Polyclar (artificial); and

gelatin. If a beer is marked “suitable for Vegans”, it was generally clarified either with seaweed

or with artificial agents, although the “fast Cask” method invented by Marston’s in 2009 may

provide another method (Hui, 2006).

Brewing process

There are several steps in the brewing process, which may include malting, mashing, lautering,

boiling, fermenting, conditioning, filtering, and packaging (Roger, 2010).

Malting is the process where barley grain is made ready for brewing. Malting is broken down

into three steps in order to help to release the starches in the barley. First, during steeping, the

grain is added to a vat with water and allowed to soak for approximately 40 hours. During

germination, the grain is spread out on the floor of the germination room for around 5 days. The

final part of malting is kilning. Here, the malt goes through a very high temperature drying in a

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kiln. The temperature change is gradual so as not to disturb or damage the enzymes in the grain.

When kilning is complete, the grains are now termed malt, and they will be milled or crushed to

break apart the kernels and expose the cotyledon, which contains the majority of the

carbohydrates and sugars; this makes it easier to extract the sugars during mashing (Garg, Garg

and Mukerji, 2010).

Marshing converts the starches released during the malting stage into sugars that can be

fermented. The milled grain is mixed with hot water in a large vessel known as a mash tun. In

this vessel, the grain and water are mixed together to create a cereal mash. During the mash,

naturally occurring enzymes present in the malt convert the starches 9long chain carbohydrates)

in the grain into smaller molecules or simple sugars (mono- di-, and tri-saccharides). This

“conversion” is called saccharification. The result of the mashing process is a sugar rich liquid or

“wort” (pronounced wert), which is then strained through the bottom of the mash tun in a process

known as lautering. Prior to lautering, the mash temperature may be raised to about 750C (165 –

1700F) (known as a mashout) to deactivate enzymes. Additional water may be sprinkled on the

grains to extract additional sugars (a process known as sparging) (Hall and Lindell, 2011).

The wort is moved into a large tank known as a “copper” or kettle where it is boiled with hops

and sometimes other ingredients such as herbs or sugars. This stage is where many chemical and

technical reactions take place, and where important decisions about the flavor, colour, and aroma

of the beer are made. The boiling process serves to terminate enzymatic processes, precipitate

proteins, isomerizes hop resins, and concentrates and sterilizes the wort. Hops add flavour,

aroma and bitterness to the beer. At the end of the boil, the hopped wort settles to clarify in a

vessel called a “whirlpool”, where the more solid particles in the wort are separated out

(Dasgupta, 2011).

After the whirlpool, the wort then begins the process of cooling. This is when the wort is

transferred rapidly from the whirlpool or brew kettle to a heat exchanger to be cooled. The heat

exchanger consists of tubing inside a bub of cold water. It is very important to quickly cool the

wort to a level where yeast can be added safely as yeast is unable to grow in high temperatures.

After the wort goes through the heat exchanger, the cooled wort goes into a fermentation tank. A

type of yeast is selected and added, or “pitched”, to the fermentation tank. When the yeast is

added to the wort, the fermenting process begins, where the sugars turn into alcohol, carbon

dioxide and other components. When the fermentation is complete the brewer may rack the beer

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into a new tank, called a conditioning tank. Conditioning of the beer is the process in which the

beer ages, the flavor becomes smoother, and flavours that are unwanted dissipate. After

conditioning for a week to several months, the beer may be filtered and force carbonated for

bottling, or fined in the cask (Hornsey, 2004).

Mashing

Mashing is the process of combining a mix of milled grain (typically malted barley with

supplementary grains such as corn, sorghum, rye or wheat), known as the “grain bill”, and water,

known as “liquor”, and heating this mixture in a vessel called a “mash tun”. Mashing is a form of

steeping, and defines the act of brewing, such as with making tea, sake, and soy sauce.

Technically, wine, cider and mead are not brewed but rather vinified, as there is no steeping

process involving solids. Mashing allows the enzymes in the malt to break down the starch in the

grain into sugars, typically maltose to create a malty liquid called wort. There are two main

methods – infusion mashing, in which the grains are heated in one vessel; and decoction

mashing, in which a proportion of the grains are boiled and then returned to the mash, raising the

temperature. Mashing involves pauses at certain temperatures (notably 450C, 620C and 730C),

and takes place in a “mash tun” – an insulated brewing vessel with a false bottom (Lewis and

Young, 2002; Howell and Schaefer, 2012). The end product of mashing is called a “mash”.

Marshing usually takes 1 to 2 hours, and during this time the various temperature rests activate

different enzymes depending upon the type of malt being used, its modification level, and the

intention of the brewer. The activity of these enzymes converts the starches of the grains to

dextrins and then to fermentable sugars such as maltose. A mash rest from 49-550C (120-1310F)

activates various proteases, which break down proteins that might otherwise cause the beer to be

hazy. This rest is generally used only with undermodified (i.e. undermalted) malts which are

decreasingly popular in Germany and the Czech Republic, or non-malted grains such as corn and

rice, which are widely used in North American beers. A mash rest at 600C (1400F) activates B-

glucanase, which breaks down gummy B-glucans in the mash, making the sugars flow out more

freely later in the process. In the modern mashing process, commercial fungal based B-glucanase

may be added as a supplement. Finally, a mash rest temperature of 65-710C (149-1600F) is used

to convert the starches in the malt to sugar, which is then usable by the yeast later in the brewing

process. Doing the latter rest at the lower end of the range favour B-amylase enzymes, producing

more low-order sugars like maltotriose, maltose, and glucose which are more fermentable by the

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yeast. This in turn creates a beer lower in body and higher in alcohol. A rest closer to the higher

end of the range favours α-amylase enzymes, creating more higher-order sugars and dextins

which are less fermentable by the yeast, so a fuller-bodied beer with less alcohol is the result.

Duration and PH variances also affect the sugar composition of the resulting wort (Black, 2010).

Lautering

Lautering is the separation of the wort (the liquid containing the sugar extracted during mashing)

from the grains. This is done either in a mash tun outfitted with a false bottom, in a lauter tun, or

in a mash filter. Most separation processes have two stages: first wort run-off, during which the

extract is separated in an undiluted state from the spent grains, and sparging, in which extract

which remains with the grains is rinsed off with hot water. The lauter tun is a tank with holes in

the bottom small enough to hold back the large bits of grist and hulls. The bed of grist that settles

on it is the actual filter. Some lauter tuns have provision for rotating rakes or knives to cut into

the bed of grist of maintain good flow. The knives can be turned so they push the grai, a feature

used to drive the spent grain out of the vessel. The mash filter is a plate-and-frame filter. The

empty frames contain the mash, including the spent grains, and have a capacity of around one

hectoliter. The plates contain a support structure for the filter cloth. The plates, frames, and filter

cloths are arranged in a carrier frame like so: frame, cloth, place, cloth with plates at each end of

the structure. Newer mash filters have bladders that can press the liquid out of the grains between

spargings. The grain does not act like a filtration medium in a mash filter (Unger, 2007).

Boiling

After mashing, the beer wort is boiled with hops (and other flavourings if used) in a large tank

known as a “copper” or brew kettle – though historically the mash vessel was used and is still in

some small breweries. The boiling process is where chemical and technical reactions take place,

including sterilization of the wort to remove unwanted bacteria, releasing of hop flavours,

bitterness and aroma compounds through isomerization, stopping of enzymatic processes,

precipitation of proteins, and concentration of the wort. Finally, the vapours produced during the

boil volatilize off-flavours, including dimethyl sulfide precursors. The boil is conducted so that it

is even and intense – a continuous “rolling boil”. The boil on average lasts between 45 and 90

minutes, depending on its intensity, the hop addition schedule, and volume of water the brewer

expects to evaporate (Goldhammer, 2008).

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At the end of the boil, the hopped wort settles to clarify in a vessel called a “whirlpool”, where

the more solid particles in the wort are separated out (Denny, 2012).

The simplest boil kettles are direct-fired, with a burner underneath. These can produce a vigorous

and favourable boil, but are also apt to scorch the wort where the flame touches the kettle,

causing caramelisation and making clean up difficult. Most breweries use a steam-fired kettle,

which uses steam jackets in the kettle to boil the wort (Denny, 2009). The steam is delivered

under pressure by an external boiler. State-of-the-art breweries today use many interesting

boiling methods, all of which achieve a more intense boiling and a more complete realization of

the goals of boiling.

Many breweries have a boiling unit outside of the kettle, sometimes called a calandria, through

which wort is pumped (Hough, Briggs, Stevens and Young, 1982). The unit is usually a tall, thin

cylinder, with many atubes upwards through it. These tubes provide an enormous surface area on

which vapour bubbles can nucleate, and thus provides for excellent volatilization. The total

volume of wort is circulated seven to twelve times an hour through this external boiler, ensuring

that the wort is evenly boiled by the end of the boil. The wort is then boiled in the kettle at

atmospheric pressure, and through careful control the inlets and outlets on the external boiler, an

overpressure can be achieved in the external boiler, raising the boiling point by a few Celsius

degrees. Upon return to the boil kettle, a vigorous vaporization occurs. The higher temperature

due to increased vaporization can reduce boil times up to 30%. External boilers were originally

designed to improve performance of kettle which did not provide adequate boiling effect, but

have since been adopted by the industry as a sole means of boiling wort.

Wort cooling

After the whirlpool, the wort must be brought down to fermentation temperatures (20-

260Celsius) (47) before yeast is added. In modern breweries this is achieved through a plate heat

exchanger. A plate heat exchange has many ridged plates, which form two separate paths. The

wort is pumped into the heat exchanger, and goes through every other gap between the plates.

The cooling medium, usually water, goes through the other gaps. The ridges in the plates ensure

turbulent flow. A good heat exchanger can drop 950C wort to 200C while warming the cooling

medium from about 100C to 800C. The last few plates often use a cooling medium which can be

cooled to below the freezing point, which allows a finer control over the wort-out temperature,

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and also enables cooling to around 100C. After cooling, oxygen is often dissolved into the wort

to revitalize the yeast and aid its reproduction.

While boiling, it is useful to recover some of the energy used to boil the wort. On its way out of

the brewery, the steam created during the boil is passed over coil through which unheated water

flows. By adjusting the rate of flow, the output temperature of the water can be controlled. This

is also often done using a plate heat exchanger. The water is then stored for later use in the next

mash, in equipment cleaning, or wherever necessary (Kunze, 2004).

Another common method of energy recovery takes place during the wort cooling. When cold

water is used to cool the wort in a heat exchanger, the water is significantly warmed. In an

efficient brewery, cold water is passed through the heat exchanger at a rate set to maximize the

water’s temperature upon existing. This now-hot water is then stored in a hot water tank

(Boulton, 2001).

Fermenting

Fermentation in brewing is the conversion of carbohydrates to alcohols and carbon dioxide or

organic acids using yeast, bacteria, or a combination thereof, under anaerobic conditions. A more

restricted definition of fermentation is the chemical conversion of sugars into ethanol. The

science of fermentation is known as zymurgy.

After the wort is cooled and aerated – usually with sterile air – yeast is added to it, and it begins

to ferment. It is during this stage that sugars won from the malt are metabolized into alcohol and

carbon dioxide, and the product can be called beer for the first time. Fermentation happens in

tanks which come in all sorts of forms, from enormous cylindro-conical vessles, thorugh open

stone vessels, to wooden vats.

Most breweries today use cylindro-conical vessels, or CCVs, which have a conical bottom and a

cylindrical top. The cone’s aperture is typically around 600, an angle that will allow the yeast to

flow towards the cone’s apex, but is not so steep as to take up too much vertical space. CCVs can

handle both fermenting and conditioning in the same tank. At the end of fermentation, the yeast

and other solids which have fallen to the cone’s apex can be simple flushed out of a port at the

apex.

Open fermentation vessels are also used, often for show in brewpubs, and in Europe in wheat

beer fermentation. These vessels have no tops, which makes harvesting top-fermenting yeast

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very easy. The open tops of the vessels make the risk of infection greater, but with proper

cleaning procedures and careful protocol about who enters fermentation chambers, the risk can

be well controlled.

Fermentation tanks are typically made of stainless steel. If they are simple cylindrical tanks with

beveled ends, they are arranged vertically, as opposed to conditioning tanks which are usually

laid out horizontally. Only a very few breweries still use wooden vats for fermentation as wood

is difficult to keep clean and infection-free and must be repitched more or less yearly.

Fermentation methods

There are three main fermentation methods, warm, cool and wild or spontaneous. Fermentation

may take place in open or closed vessels. There may be a secondary fermentation which can take

place in the brewery, in the cask or in the bottle.

Brewing yeast may be classed as “top-cropping” (or “top-fermenting”) and “bottom-cropping”

(or “bottom-fermenting”). This distinction was introduced by the Dane Emil Christian Hansen.

Top-cropping yeasts are so called because they form a form at the top of the wort during

fermentation. They can produce higher alcohol concentrations and in higher temperatures,

typically 16 to 240C (61 to 750F), produce fruitier, sweeter beers. An example of top-cropping

yeast is Saccharomyces cerevisiae. Bottom-cropping yeast are typically used to produce cool

fermented, lager-type beers, though they can also ferment at higher temperatures if kept under

34C. These yeast ferment more sugars, creating a dryer beer, and grow well at low temperatures

(Esslinger, 2009). An example of bottom-cropping yeast is Saccharomyces pastorianus, formerly

known as Saccharomyces carlsbergensis.

For both types, yeast is fully distributed through the beer while it is fermenting, and both equally

flocculate (clump together and precipitate to the bottom of the vessel) when fermentation is

finished by no means do all top-cropping yeasts demonstrate this behaviour, but it features

strongly in many English yeasts that may also exhibit chain forming (the failure of budded cells

to break from the mother cell), which is in the technical sense different from true flocculation.

The most common top-cropping brewer’s yeast, Saccharomyces cerevisiae, is the same species

as the common baking yeast. However, baking and brewing yeasts typically belong to different

strains, cultivated to favour different characteristics: baking yeast strains are more aggressive, in

order to carbonate dough in the shortest amount of time; brewing yeast stains act slower, but tend

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to produce fewer off-flavours and tolerate higher alcohol concentrations (with some stains, up to

22%).

To ensure purity of strain, a clean sample of brewing yeast is sometimes stored, either dried or

refrigerated in a laboratory (Goldhammer, 2008). After a certain number of fermentation cycles,

full scale propagation is produced from this laboratory sample. Typically, it is grown up in about

three or four stages using sterile brewing wort and oxygen.

Warm-fermenting

In general, yeasts such as Saccharomyces cerevisiae are fermented at warm temperatures

between 15 and 20°C (59 and 68 `F), occasionally as high as 24°C (75°F), while the yeast used

by Brasserie Dupont for saison ferments even higher at 29°C (840F) to 35°C (95°F). They

generally form foam on the surface of the fermenting beer, as during the fermentation process its

hydrophobic surface causes the floes to adhere to C02 and rise; because of this, they are often

referred to as "top-cropping" or "top-fermenting" - though this distinction is less clear in modern

brewing with the use of cylindro-conical tanks (McFarland, 2009). Generally, warm-fermented

beers are ready to drink within three weeks after the beginning of fermentation, although some

brewers will condition them for several months.

Cool fermenting

Lager is beer that has been cool fermented at around 10°C (500F), compared to typical warm

fermentation temperatures of 18°C (640F). It is then stored for 30 days or longer close to the

freezing point, and during this storage sulphur components developed during fermentation

dissipate.

Though it is the cool fermenting that defines lager, the main technical difference with lager yeast

is its ability to process raffinose (a trisaccharide composed of the sugars galactose, fructose, and

glucose), which means that all sugars are fermented, resulting in a well attenuated beer; warm

fermenting yeast only cleaves and ferments the fructose portion of raffinose, leaving melibiose,

which it cannot further cleave into two monasaccharides due to its lack of me1ibiase, so ale

remains sweeter with a lower conversion of sugar into alcohol. Raffinose is a minor dry

component of Carlsberg barley, but once malted is practically nonexistent (Denny, 2009).

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While the nature of yeast was not fully understood until Emil Hansen of the Carlsberg brewery in

Denmark isolated a single yeast cell in the 1800s, brewers in Bavaria had for centuries been

selecting cold-fermenting lager yeasts by storing (Lagern) their beers in cold alpine caves. The

process of natural selection meant that the wild yeasts that were most cold tolerant would be the

ones that would remain actively fermenting in the beer that was stored in the caves. Some of

these Bavarian yeasts were brought back to the Carlsberg brewery around the time that Hansen

did his famous work. Today, lagers represent the vast majority of beers produced. Examples

include Budweiser Budvar, Birra.

Moretti, Stella Artois, Red Stripe, and Singha. Some lager-style beers market themselves as

Pilsner, which originated in Pilsen, Czech Republic (Plzen in Czech). However, Pilsners are

brewed with 100% barley malt and aggressive hop bitterness, flavour, and aroma.

Lager yeast normally ferments at a temperature of approximately 5 °C (40 °Fahrenheit). Lager

yeast can be fermented at a higher temperature normally used for top-fermenting yeast, and this

application is often used in a beer style known as California Common or colloquially as “steam

beer”. Saccharomyces pastorianus is used in the brewing of lager.

Spontaneous fermentation

Lambic beers are brewed primarily around Brussels, Belgium. They are fermented in oak barrels

after being inoculated with wild yeast and bacteria while cooling in a Koelschip. Wild yeast and

bacteria ferment the wort in the oak barrels. The beers fermented from yeast and bacteria in the

Brussels area are called Lambic beers. These bacteria add a sour flavour to the beer. Of the many

styles of beer very few use bacteria, most are fermented with yeast alone and bacterial

contamination is avoided.

However, with the advent of yeast banks and the National Collection of Yeast Cultures, brewing

these beers - albeit not through spontaneous fermentation - is possible anywhere. Specific

bacteria cultures are also available to reproduce certain styles.

Brettanomyces is a genus of yeast important in brewing larnbic, a beer produced not by the

deliberate addition of brewer's yeasts, but by spontaneous fermentation with wild yeasts and

bacteria (Markowski, 2004).

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Taking inspiration from Belgium-style brews, American microbreweries produce beer with

microorganisms other than Saccharomyces, usually Srettanomyces. These fall in the broad

category of American wild ale. Conditioning

After an initial or primary fermentation, beer is conditioned, matured or aged, in one of several

ways, which can take from 2 to 4 weeks, several months, or several years, depending on the

brewer's intention for the beer. The beer is usually transferred into a second container, so that it

is no longer exposed to the dead yeast and other debris (also known as “trub”) that have settled to

the bottom of the primary fermenter, This prevents the formation of unwanted flavours and

harmful compounds such as acetylaldehydes (Markowski, 2004).

Krausening

Krausening is a conditioning method in which fermenting wort is added to the finished beer. The

active yeast will restart fermentation in the finished beer, and so introduce fresh carbon dioxide;

the conditioning tank will be then sealed so that the carbon dioxide is dissolved into the beer

producinq a lively “condition” or level of carbonation. The krausenlnq method may also be used

to condition bottled beer (Lea and Piggott, 2003).

Lagering

Lagers are stored at near freezing temperatures for 1- 6 months while still on the yeast. The

process of storing, or conditioning, or maturing, or aging a beer at a low temperate for a long

period is called “Iagering”, and while it is associated with lagers, the process may also be done

with ales, with the same results - that of cleaning up various chemicals, acids and compounds

(Bamforth, 2005).

Secondary fermentation

During secondary fermentation, most of the remaining yeast will settle to the bottom of the

second fermenter, yielding a less hazy product (Stevens et al, 2004).

Bottle fermentation

Some beers undergo fermentation in the bottle, giving natural carbonation. This may be a second

or third fermentation. They are bottled with a viable yeast population in suspension. If there is no

residual fermentable sugar left, sugar and or wort may be added in a process known as priming.

The resulting fermentation generates C02 that is trapped in the bottle, remaining in solution and

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providing natural carbonation. Bottle-conditioned beers may be either filled unfiltered direct

from the fermentation or conditioning tank, or filtered and then reseeded with yeast.

Cask conditioning

Cask ale or cask-conditioned beer is the term for unfiltered and unpasteurised beer that is

conditioned (including secondary fermentation) and served from a cask without additional

nitrogen or carbon dioxide pressure (Dornbusch, 2011).

Filtering

Filtering the beer stabilizes the flavour, and gives beer its polished shine and brilliance. Not all

beer is filtered. When tax determination is required by local laws, it is typically done at this stage

in a calibrated tank. Filters come in many types. Many use sheets or candles. Others use a fine

powder such as diatomaceous earth, also called kiese1guhr. The powder is added to the beer and

recirculated past screens to form a filtration bed.

Filters range from rough filters that remove much of the yeast and any solids (e.g., hops, grain

particles) left in the beer, to filters tight enough to strain colour and body from the beer.

Filtration ratings are divided into rough, fine, and sterile. Rough filtration leaves some cloudiness

in the beer, but it is noticeably clearer than unfiltered beer. Fine filtration removes almost all

cloudiness. Sterile filtration removes almost all microorganisms.

Sheet (pad) filters

These filters use sheets that allow only particles smaller than a given size to pass through. The

sheets are placed into a filtering frame, sterilized (with boiling water, for example) and then used

to filter the beer. The sheets can be flushed if the filter becomes blocked. The sheets are usually

disposable and are replaced between filtration sessions. Often the sheets contain powdered

filtration media to aid in filtration.

Pre-made filters have two sides;one with loose holes, and the other with tight holes. Flow goes

from the side with loose holes to the side with the tight holes, with the intent that large particles

get stuck in the large holes while leaving enough room around the particles and filter medium for

smaller particles to go through and get stuck in tighter holes. Sheets are sold in nominal ratings,

and typically 90% of particles larger than the nominal rating are caught by the sheet.

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Filters that use a powder medium are considerably more complicated to operate, but can filter

much more beer before regeneration. Common media include diatomaceous earth and perlite.

Packaging

Packaging is putting the beer into the containers in which it will leave the brewery. Typically,

this means putting the beer into bottles, aluminum cans and kegs/casks, but it may include

putting the beer into bulk tanks for high-volume customers.

Brewing methods

There are several additional brewing methods, such as barrel aging, double dropping, and

Yorkshire Square.

Brewing by-products are “spent grain” and the sediment (or “dregs”) from the filtration process

which may be dried and resold as “brewers dried yeast” for poultry feed, or made into yeast

extract.

Yeast extract is used in brands such as Vegemite and Marmite. The process of turning the yeast

sediment into edible yeast extract was discovered by a German scientist Justus Liebig (Bamforth,

2009).

Spent grain

Brewer's spent grain (also called spent grain, brewer's grain or draft) consists of the residue of

malt and grain which remains in the mash-kettle after the mashing and lautering process. It

consists primarily of grain husks, pericarp, and fragments of endosperm. As it mainly consists of

carbohydrates and proteins, and is readily consumed by animals, spent grain is used in animal

feed. Spent grains can also be used as fertilizer, whole grains in bread, as well as in the

production of biogas. Spent grain is also an ideal medium for growing mushrooms, such as

shiitake, and already some breweries are either growing their own mushrooms or supplying spent

grain to mushroom farms. This, in turn, makes the grain more digestible by livestock. Spent

grains can be used in the production of red bricks, to improve the open porosity and reduce

thermal conductivity of the ceramic mass (Blair, 2008).

Brewing industry

The brewing industry is a global business, consisting of several dominant multinational

companies and many thousands of smaller producers known as microbreweries or regional

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breweries depending on size and region (Blair, 2008). More than 133 billion liters (35 billion

gallons) are sold per year-producing total global revenues of $294.5 billion (£147.7 billion) as of

2006. SABMiller became the largest brewing company in the world when it acquired Royal

Grolsch, brewer of Dutch premium beer brand Grolsch. InBev was the second-largest beer-

producing company in the world and Anheuser-Busch held the third spot, but after the

acquisition of Anheuser-Busch by InBev, the new Anheuser-Busch InBev Company is currently

the largest brewer in the world (Blair, 2008).

Brewing at home is subject to regulation and prohibition in many countries. Restrictions on

homebrewing were lifted in the UK in 1963, Australia followed suit in 1972, and the" USA in

1978, though individual states were allowed to pass their own laws limiting production

(Verachert, 1995).

Research indicates that brewing has taken place since around the 6th millennium BC, and

archaeological evidence suggests that this technique was used in most emerging civilisations

including ancient Egypt. Descriptions of various beer recipes can be found in cuneiform from

Sumer, some of the oldest known writing of any sort (Forsell, 2008).

2.7 SUMMARY OF LITERATURE REVIEW


organization to meet changing demands for its products (North Caroline State University). In the

context of capacity planning, ‘design capacity’ is the maximum amount of work that an



such as quality problems, delays, material handling, etc.





low, coordination can be achieved through rules and heuristics (Cyert and March, 1963).

Capacity planning in the literature has been applied to the brewing industry. The Research Gap

here is to determine the influence of capacity planning on the performance of the Brewing

Industry in South Eastern Nigeria. In brewing management, the planning-focused methods have

been developed around the concept of material requirements planning (MRP, Orlicky, 1975),

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while the methods that emphasize rule-based control and simplicity are founded on the just-in-

time (JIT) methodology (Ohno, 1988).


leverage, activity and morale (Nwanchukwu, 2004). Dictionary’s definition of efficiency as

fitness or power to accomplish or success in accomplishing the purpose intended, adequate

power, effectiveness, efficacy. Later on, it is pointed out that efficiency acquired a second

meaning – the ratio between input and output, between effort and results, expenditure and

income, cost and the resulting pleasure, this second meaning became current in Business and

Economics, only since the beginning of the 20th Century. Still later on, influenced by the

scientific management movement, efficiency was defined as the ratio of actual performance to

the standard performance (Bell, 2006).

The performance of the brewing industry was constrained by high cost of production which was

attributable mainly to substantial depreciation of the naira exchange rate. The resultant sharp rise

in cost of importation of raw materials, machinery and spare parts resulted in corresponding







(CBN, 2002).

It is expected that there will be a research gap in the area of determining the extent to which

capacity planning enhanced the performance in the brewing industry in South Eastern Nigeria. It

is also expected that there will be a research gap in the area of ascertaining the nature of the

relationship between capacity requirements planning and materials requirements planning. It is

expected that there will be a research gap in the area of ascertaining the extent to which capacity

planning sustained the organizations’ competitive advantage.

Thus, there is a research gap in the area of determining the extent of the relationship between

capacity planning and capacity building. There is also a research gap in the area of accessing the

steps towards developing a capacity plan and the profitability in the brewing firms in the area

studied. The research work would attempt to fill the gaps created using data.

101

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112

CHAPTER THREE

METHODOLOGY

This chapter focused on the procedures, techniques and methods used for the study. It focuses on

research design, sources of data, description of research instruments, the population of the study,

sample size determination, validity of data collection and reliability of instruments and methods

of data analysis.

3.2 RESEARCH DESIGN

The research design study is the framework which specifies the type of information to be

collected, the sources of data and collection procedure. It is the basic plan for data collection and

analysis of the study. The research designs chosen for this study are the survey method, oral

interview and model modification. The survey research design chosen was considered quite

appropriate because it deals with large population of people with different characteristics and

domicile in different locations. While the oral and model adaptations, adds more credence to this

work..

The oral interview specifically, is very useful in research works because open ended questions

could be asked in a face to face interview, does clarifying some questions that otherwise would

have been difficult for the respondents to understand.

Also, model is a representation of reality and not reality itself. This is because not all factors in

the real system have to be in the model (Asika, 2006).

3.3 SOURCES OF DATA

In the conduct of this research, necessary information was obtained from two sources namely:

(a) Primary data.

(b) Secondary data.

a. Primary Data

Primary data refer to original first hand data or information collected by the researcher through

the use of structured questionnaire, personal interview and observations (Asika, 2006:17), and

this was precisely what the researcher did.

113

b. Secondary Data

Secondary data is the data that is existing and it is not the data collected by the researcher. It is

available in such sources like the Internet, Books, Journals, technical Magazines, Annual Reports

of the Brewing firms. Due to the fact that the researcher was not the original collector, all the

biases, mistakes and all the other exaggerations are inherited. However, the researcher evaluated

any secondary data that was used in this research work to make for internal consistency.

3.4 POPULATION OF THE STUDY

The population of study included all the senior and junior staff of the brewing industriesstudied.

The population size per firm is given here under:

Firms Population size per brewing company

1) Nigerian Breweries Plc 297

2) Guinness Nigeria Plc 224

3) Premier Breweries Plc 149

4) Continental Breweries Plc 75

Total 745

Source: Fieldwork, 2013

3.5 THE SAMPLE AND SAMPLING TECHNIQUE

Sampling represents the process of selecting a subset of observations from among many possible

observations for the purpose of drawing conclusions about the larger set of possible observations.

It is a strategy a researcher adopts in order to arrive at a good representation of the population.

The sampling method adopted in this study is the stratified sampling method. Sampling is a

compromise between arbitrary guess work in one extreme and perfection in the other extreme.

In calculating the sample size for this study, the researcher appllied the statistical formula for

selecting from a finite population as propounded by Taro Yamane’s

Assigning values to these symbols, the sample size was calculated thus:

2)025.0(7451

745

+=n

509=

The sample sizes were 203 for Nigerian Breweries Plc, 153 for Guinness Nigeria Plc, 102 for

Premier Breweries and 51 for Continental Breweries Plc. However, a census was done usingthe

populationof 745.

114

3.6 DESCRIPTION OF RESEARCH INSTRUMENTS

The major research instruments that were used in gathering data in this investigation were the

structured questionnaire, oral interview schedule and dichotomous (yes or no oral interview

schedule). The questionnaire was tailored in the sense that all the questions were logically

sequenced, asked to all the respondents in the same manner and no follow up questions were

allowed. The questions on the personal dataare designed to capture the demographic data of the

respondents, such that the respondents were given the answers to tick. The questions related to

the five objectives were of the Likert scale form in which there were statements with the answers

of strongly agree, agree, undecided, disagree and strongly disagree.

In the oral interview schedule, there are five open-ended questions containing the research

questions with a focuse group discussion. The answers to the questions from the two schedules

were content-analyzed.

3.7 DATA ANALYSIS TECHNIQUE(S)

The first hypothesis was tested using the Z test of population proportions. The second hypothesis

was tested using the Spearman’s rank correlation coefficient. The third hypothesis was tested

using Z test of population proportions. Test of the fourth hypothesis was using the Spearman’s

rank correlation while the fifth hypothesis was done using Z test of population proportions. So in

summary, tests of hypothesis number 1, 3 and 5 were using Z test of population proportion while

hypothesis 2 and 4 were using Spearman’s Rank Correlation.

Spearman’s Rank Correlation Coefficient

It is used in determining the relationship between independent variable and a dependent variable

and is given by the formula.

( )( )11

61

21

+−−= ∑

nnn

dr

Where:

d = is the difference in ranks for the two variables

n = is the number of years

rs = is the Spearman’s Rank Correlation Coefficient

the significance of the Spearman’s rank correlation is to be tested using the t test.

115

t – test statistic

When hypothesis is formulated using the correlation coefficient, it must be known that

population parameter is involved. Therefore, it is always advisable to transform the correlation

coefficient that would be obtained into students’ t – distribution (Agbadudu, 2004). This would

indicate whether the correlation coefficient obtained would show a real relationship or whether it

could be reasonably attributed to chance. In order to transform the r to t, the following

transformation formula would be used.

( )2

1 2

−−

=

n

r

rt

where:

t = t – statistic

r = Correlation Coefficient

n = No of Paired value

n – 2 = No Degree of freedom

Decision Rule: reject Ho if t computed is > t critical otherwise accept.

The z – test statistic

The z – test is normally used like the t – test but only when the sample size is equal or greater

than 30 (i.e. n> 30) (Agbadudu, 2004). The z – test statistic can also be computed exactly the

same way the t – test statistic is computed using the same formula. According to Ewurum (2003)

a z-test statistic can be used interchangeably with a t-test statistic when there are at least 150

degrees of freedom.

Z-test = tn –1 = ns

UX −

with n – 1 degrees of freedom.

Where:

X = Sample mean

U = Hypothesized mean of the sample

s = Sample standard deviation

n = Sample size

116

Decision Rule: Reject the Ho and uphold Hi if the Z calculated value exceeds the critical or

the Z-table values. But if the reverse is the case, do not reject the null hypotheses.

3.8 VALIDITY OF INSTRUMENT

Validity of a test reflects what a test measures and how well it measures it (Enikanselu et al,

2009). It is concerned with how a measuring instrument is actually measuring the concept in

question and also how the concept is being measured accurately. In this study, the table of

random numbers used to select the respondents and the same version of the instrument is

administered to the respondents and this gives the instrument content validity.

3.9 RELIABILITY OF THE RESEARCH INSTRUMENT

Reliability has to do with the extent to which the measure contains an error. If the research

measure does not contain an error, it is said to be reliable. There are three types of reliability

namely; split-half, equivalent forms and test-retest methods. The test-retest method is to be used.

The same version of the research instrument is to be administered to the same respondents at two

point in time and the results are correlated. A Spearman’s rank correlation coefficient of 0.9 or

less than 1 or equal to 1 makes the research measure to be reliable.

117

REFERENCES

Agbadudu, A.B. (2004), Statistics for Business and the Social Sciences, Benin City: Uri

Publishing Limited.

Asika, N. (2006), Research Methodology: A Process Approach, Lagos: Mukugamu and Brothers

Enterprises.

Banjoko, S.A. (2006), Production and Operations Management, Lagos: Saban Publishers.

Enikanselu, S.A., Ojodu, H.O. and Oyende, A.I. (2009), Management and Business Research

Seminar, Lagos: Enykon Consult.


Nwachukwu, C.C. (1988), Management Theory and Practice, Onitsha: Africana FEP Publishers

Limited.

Nwana, O.C. (2000), Introduction to Educational Research, Ibadan: Heinemann Educational

Books Nigerian Plc.

O’brien, J.A. (2008), An Introduction to Computers in Business Management. Homewood:

Illinois: Richard D. Irwin Incorporated.


118

CHAPTER FOUR

DATA PRESENTATION AND ANALYSIS

4.1

In the last chapter, the Research Methodology was handled; adopting the research design was a

combination of a survey, oral interview and model modification. Primary data was collected on

the personal data of the respondents, on capacity planning and performance in the brewing


The data is presented by means of tables to make it amenable for further analysis. By analysis, is

meant the act of noting relationships and aggregating data on variables with similar

characteristics (Mills and Walter, 2008). Yomere and Agbonifoh (2000) have consistently

observed that it is at the analysis stage of a research work that meaning is given to the data that is

collected. If the data is not properly analysed, it will be difficult to discuss the results or findings.

It will also be difficult to write the summary of findings, conclusion, recommendations and

contribution to knowledge and suggestions for further research in the next chapter.

Podsakoff and Dalton (1987) have observed that the factual data is going to be a basis for

reasoning, calculation and discussion. Apart from the heading above, the other major headings

are Data Presentation, Data Analysis, Discussion of the findings related to the contingency

theory and multi period capacity problem and discussion of Findings.

4.2 DATA PRESENTATION

Table 4.1 shows the presentation of the response rate of the questionnaires administered.

Table 4.1: The presentation of the response rate of the questionnaires administered

Number Rate

Total number of questionnaire administered 745 1

Copies of questionnaire returned 740 0.993

Total copies of questionnaire not returned 5 0.007

Source: From the field survey (2014).

From Table 4.1, it is shown that 740 out of 745 copies of the questionnaire administered were

returned giving a return rate of 0.993, while 5 out of the 745 copies were not returned giving a

non return rate of 0.007. The total return rate and non return rate was 1.

119

Table 4.2 shows the summary of the personal data of the 740 respondents.

Table4.2: The summary of the personal data of the 740 respondents

1 Sex Frequency

Male

Female

Total

518

222

740

2 Marital Status Frequency

Married

Single

Total

562

178

740

3 Age Frequency

Under 20 years

21 – 30 years

31 – 40 years

41 – 50 years

51 – 60 years

Above 60 years

Total

15

155

192

200

170

8

740

4 Highest Educational Qualifications Frequency

Senior School Certificate Royal Society of Arts Diploma Ordinary National Diploma Higher National Diploma First Degree Second Degree Ph.D Associate of Chartered Accountants (ACA) Total

104 44 30 141 129 211 44 1

36

740

Source: From the Questionnaire Administered (2014)

From Table 4.2, it is shown that for the sex of the 740 respondents, 518 of them were males and

222 of them were females. For the marital statuses of the 740 respondents, 562 of them were

married and 178 of them were single. For the ages of 740 respondents, they were under 20 years,

21 – 30 years, 31 – 40 years, 41 – 50 years, 51 – 60 years and above 60 years. They had

frequencies of 15, 155, 192, 200, 170 and 8 of them respectively. For the highest educational

120

qualifications of the 740 respondents, they were Senior School Certificate, Royal Society of Arts,

Diploma, Ordinary National Diploma, Higher National Diploma, First Degree, Second Degree,

Ph.D and Associate of Chartered Accountants. They had frequencies of 104, 44, 30, 141, 129,

211, 44, 1 and 36 of them respectively.

Table 4.3 shows the presentation of the responses on the statuses and durations worked of the

740 respondents.

Table 4.3: The presentation of the responses on their statuses and their experiential years

Status Frequency

Senior Staff

Junior Staff

Total

252

488

740

Durations worked (experiential

years) Frequency

0 – 10 years

11 – 20 years

20 – 30 years

Above 30 years

Total

30

342

360

8

740

Source: From the Questionnaire Administered(2014)

From Table 4.3, it is shown that for the statuses of the 740 respondents, 252 of them were Senior

Staff while 488 of them were Junior Staff. For the durations worked, they were 0-10 years, 11 –

20 years, 21 – 30 years and above 30 years. They had frequencies of 30, 342, 360 and 8 of them

respectively. This implies that majority of the respondents are quite experienced having worked

for not less than 20 years.

4.3 DATA ANALYSIS

4.3.1 Percentage Analysis

A five point Likert-scale was used with values assigned ranging from 5(SA) to 1(SD) for

positive responses and vice versa for negative responses.

Table 4.4 gives the analysis of the responses related to the five objectives.

121

Table 4.4: The analysis of the responses related to the five objectives

STATEMENTS RESPONSES

X SA A U D SD

1. Capacity planning to a large extent

enhances the performance in the

brewing sector in South Eastern Nigeria

F

%

300

40.541

367

49.595

23

3.108

25

3.378

25

3.388

2 There is a significant positive

relationship between capacity

requirements planning and materials

requirements planning

F

%

304

41.081

370

50.000

22

2.973

23

3.108

21

2.838

3 Capacity planning to a large extent

sustains the organization’s competitive

position

F

%

302

40.811

380

51.351

20

2.703

19

2.568

19

2.568

4 There is a significant positive

relationship between capacity building

and capacity planning.

F

%

308

41.622

381

51.486

17

2.297

18

2.432

16

2.162

5 The steps towards developing capacity

plan positively affected profitability in

the brewing industry in the area studied

F

%

315

42.48

381

51.486

14

1.892

15

2.027

15

2.027

Source: From the Questionnaire Administered (2014)

Table 4.4 shows the statements and the responses namely Strongly Agree (SA), Agree (A),

Undecided (U), Disagree (D) and Strongly Disagree (SD).For the statement that capacity

planning to a large extent enhances the performance in the brewing sector in South Eastern

Nigeria, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.

They had frequencies of 300, 367, 23, 25 and 25 out of 740 respectively. These gave percentages

to 3 decimal places of 40.541, 49.595, 3.108, 3.378 and 3.378 respectively.

For the statement that there is a significant positive relationship between capacity requirements

planning and materials requirements planning, the responses were Strongly Agree, Agree,

Undecided, Disagree and Strongly Disagree. They had frequencies of 304, 370, 22, 23 and 21 out

of 740 respectively. These gave percentages of 41.081, 50.000, 2.473, 3.108 and 2.838

respectively.

122

For the statement that capacity planning to a large extent sustains the organizations competitive

position, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.

They had frequencies of 302, 380, 20, 19 and 19 out of 740 respectively. These gave percentages

of 40.811, 51.351, 2.703, 2.568 and 2.568 respectively.

For the statement that there is a significant positive relationship between capacity building and

capacity planning, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly

Disagree. They had frequencies of 308, 381, 17, 18 and 16 out of 740 respectively. These gave

percentages of 41.622, 51.486, 2.297, 2.432 and 2.162 respectively.

For the statement that the steps towards developing a capacity plan positively affected

profitability in the brewing industry in the area studied, the responses were Strongly Agree,

Agree, Undecided, Disagree and Strongly Disagree. They had frequencies of 315, 381, 14, 15

and 15 out of 740 respectively. These gave percentages of 42.568, 51.486, 1.892, 2.027 and

2.027 respectively.

Table 4.5 gives the analysis of the 12 steps towards developing a capacity plan that have

positively affected profitability in the brewing industry in the area studied.

123

Table 4.5: The analysis of the 12 steps towards developing a capacity plan that have

positively affected profitability in the brewing industry in the area studied

s/n The 12 steps Frequency Percentages

1 To determine service level requirements 69 9.624

2 To define workloads 66 8.919

3 To determine the unit of work 65 8.784

4 To determine the service levels of each workload 64 8.649

5 To analyse the current system capacity 63 8.514

6 To measure service levels 62 8.378

7 To measure the overall resource usage 61 8.243

8 To measure the resource usage by workload 60 8.108

9 To identify the components of response time. 59 7.973

10 To plan for the future 58 7.838

11 To determine the future processing requirements 57 7.703

12 To plan the future system configuration 56 7.568

Total 740 100

Source: The 12 steps and the numbers are got from the questionnaires administered (2014)

From Table 4.5, the 12 steps were to determine service level requirements, to define workloads,

to determine the unit of work, to determine the service levels of each workload, to analyse the

current system capacity, to measure service levels, to measure the overall resource usage, to

measure the resource usage by workload, to identify the components of the response time, to

plan for the future, to determine the future processing requirements and to plan the future system

configuration. They had frequencies of 69, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57 and 56 out of

740 respectively. These gave percentages of 9.324, 8.919, 8.784, 8.649, 8.514, 8.378, 8.243,

8.108, 7.973, 7.838, 7.703 and 7.568 respectively.

124

4.3.2 Relative Frequency Analysis

Table 4.6 shows the analysis of the responses opposite in meaning to the objectives.

Table 4.6: The analysis of the responses opposite in meaning to the objectives

STATEMENTS RESPONSES

X SA A U D SD

1. Capacity planning to a little extent

enhances the performance in the brewing

sector in South Eastern Nigeria

F

R.F

25

0.034

25

0.034

23

0.031

367

0.496

300

0.405

2 There is a significant negative relationship

between capacity requirements planning

and materials requirements planning

F

R.F

21

0.028

23

0.031

22

0.030

370

0.500

304

0.411

3 Capacity planning to a low extent sustains

the organization’s competitive position

F

R.F

19

0.026

19

0.026

20

0.027

380

0.514

302

0.408

4 There is a negative correlation between

capacity building and capacity planning.

F

R.F

16

0.022

18

0.024

17

0.023

381

0.575

308

0.416

5 The are no steps towards developing

capacity plan to improve the profitability in

the brewing industry in the area studied

F

R.F

15

0.020

15

0.020

14

0.019

381

0.515

215

0.426

Source: the statements, responses and the numbers are got from the questionnaire

administered (2014)

Table 4.6 shows the statements, the responses and the numbers and the relative frequencies

which summed up to 1. For the statement that capacity planning to an appreciable extent enhance

the performance in the brewing sector in South Eastern Nigeria, the responses were Strongly

Agree, Agree, Undecided, Disagree and Strongly Disagree. They had frequencies of 25, 25, 23,

367 and 300 out of 740 respectively. These gave relative frequencies of 0.034, 0.034, 0.031,

0.496 and 0.405 respectively.

For the statement that there is a significant negative relationship between capacity requirements

planning and materials requirements planning, the responses were Strongly Agree, Agree,

Undecided, Disagree and Strongly Disagree. They had frequencies of 21, 23, 22, 370 and 304 out

of 740 respectively. These gave relative frequencies of 0.028, 0.031, 0.30, 0.500 and 0.411

respectively.

125

For the statement that capacity planning to a low extent sustains the organization’s competitive

position, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.

They had frequencies of 19, 19, 20, 380 and 302 out of 740. These gave relative frequencies of

0.026, 0.026, 0.027, 0.514 and 0.408 respectively.

For the statement that there is a negative correlation between capacity building and capacity

planning, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly


relative frequencies of 0.022, 0.024, 0.023, 0.575 and 0.416 respectively.

For the statement that there are no steps towards developing a capacity plan to improve the

profitability in the brewing industry in the area studied, the responses were Strongly Agree,

Agree, Undecided, Disagree and Strongly Disagree. They had frequencies of 15, 15, 14, 381 and

315 out of 740. These gave relative frequencies of 0.020, 0.020, 0.019, 0.515 and 0.426

respectively.

4.3.3 Analysis using the Coefficient of Variation

Table 4.7 shows the analysis of the other responses related to the first four objectives.

Table 4.7: The analysis of the other responses related to the first four objectives

s/n Statements X SA A U D SD X S

S

X

1 Adding capacity in anticipation of an increase in demand increases the performance in the brewing sector in South Eastern Nigeria.

F 298 366 24 26 26 4.195 0.925 4.535

2 Adding capacity only after the organization is running at full capacity due to increase in demand increases the performance in the brewing sector in South Eastern Nigeria.

F 26 26 24 366 298 1.805 0.962 1.979

3 There is no relationship

between capacity

requirements planning and

F 20 22 21 371 306 1.755 0.863 2.039

126

materials requirements

planning

4 Materialsrequirements

planning has a positive

correlation with capacity


F 306 371 21 22 20 4.244 0.863 4.918

5 The extent to which capacity

planning sustains the

organization’s competitive

advantage is not obvious.

F 19 20 19 381 361 1.750 0.843 2.076

6 The extent to which capacity

planning sustains the

organizations competitive

position is obvious.

F 301 381 19 20 19 4.250 0.843 5.042

7 There is a positive correlation

between capacity planning

and capacity building.

F 307 382 18 17 16 4.280 0.805 5.317

8 There is a negative


planning and capacity

building.

F 16 17 18 382 307 1.720 0.805 2.137

Source: From Questionnaire Administered (2014)

Table 4.7 given the statements, responses, sample mean, sample standard deviation and the

coefficient of determination. For the statement that adding capacity in anticipation of an increase

in demand increases the performance in the brewing sector in South Eastern Nigeria, the

responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree. They had

frequencies of 298, 366, 24, 26 and 26 out of 740 respectively. These gave a sample mean of

4.195, sample standard deviation of 0.925 and coefficient of variation of 4.535.

For the statement that adding capacity only after the organization is running at full capacity due

to increase in demand increases the performance in the brewing sector in South Eastern Nigeria,

the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree. They

127

had frequencies of 26, 26, 24, 366 and 298 out of 740 respectively. These gave a sample mean of

1.805, a sample standard deviation of 0.962 and a coefficient of determination of 1.979.

For the statement that there is no relationship between capacity requirements planning and

materials requirements planning, the responses were Strongly Agree, Agree, Undecided,

Disagree and Strongly Disagree. They had frequencies of 20, 22, 21, 371 and 306 out of 740

respectively. These gave a sample mean of 1.755, a sample standard deviation of 0.863 and a

coefficient of variation of 2.039.

For the statement that materials requirements planning have a positive correlation with capacity

requirements planning, the responses were Strongly Agree, Agree, Undecided, Disagree and

Strongly Disagree. They had frequencies of 306, 371, 21, 22, 20. These gave a sample mean of

4.244, a sample variance of 0.863 and a coefficient of variation of 4.918.

For the statement that the extent to which capacity planning sustains the organization’s

competitive position is not obvious, the responses were Strongly Agree, Agree, Undecided,

Disagree and Strongly Disagree. They had frequencies of 19, 20, 19, 381, 301 out 740

respectively. These gave a sample mean of 1.750, a sample standard deviation of 0.843 and a

coefficient of variation of 2.076.

For the statement that the extent to which capacity planning sustains the organization’s

competitive position is obvious, the responses were Strongly Agree, Agree, Undecided, Disagree

and Strongly Disagree. They had frequencies of 301, 381, 19, 20 and 19 out of 740 respectively.

These gave a sample mean of 4.250, a sample variance of 0.843 and a coefficient of

determination of 5.042.

For the statement that there is a positive correlation between capacity planning and capacity

building the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.

They had frequencies of 307, 382, 18, 17 and 16 out of 740 respectively. These gave a sample

mean of 4.280, a sample standard deviation of 0.805 and a coefficient of determination of 5.317.

For the statement that there is a negative relationship between capacity planning and capacity

building, the responses are Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.

They had frequencies of 16, 17, 18, 382 and 307 out of 740 respectively. These gave a sample

mean of 1.720, sample standard deviation of 0.805 and a coefficient of variation of 2.137.

128

In all the positive statements had a higher sample mean, and higher coefficient of variation than

the corresponding negative statements.In all the negative statements had a lower sample mean

and lower coefficient of determination than the corresponding positive statements.Most of the

740 respondents agreed with the positive statements.

4.3.4 Hypotheses Testing

Five hypotheses are to be tested in the null that:

1. Capacity planning to a non appreciable extent enhanced the performance in the brewing


2. There is no significant positive relationship between capacity requirements planning and

materials requirements planning.

3. Capacity planning to a little extent, sustains organizations’ competitive advantage.

4. There is a negative significant relationship between capacity planning and capacity

building.

5. The steps towards developing a capacity plan that would affect profitability in the

brewing industry in South Eastern Nigeria are not of the same order of magnitude.

The alternative hypotheses are that:

1. Capacity planning to a large extent enhanced the performance in the brewing industry in

South Eastern Nigeria.

2. There is a significant positive relationship between capacity requirements planning and

materials requirements planning.

3. Capacity planning to a large extent sustains organization’s competitive advantage.

4. There is a positive significant relationship between capacity planning and capacity

building.

5. The steps towards developing a capacity plan that would affect profitability in the

brewing industry in South Eastern Nigeria are of the same order of magnitude.

129

Table 4.8 shows the computational details of the first hypothesis.

Table 4.8: The computational details of the first hypothesis

Hypothesis number Calculated Z

value

Table Z value Statistical

Decision

1 6.893 1.645 Reject Ho

( )

( )

892637062.6

4.0

)101351351.0(740

740

)8.01)(8.0(

8.0740

667

)1(

=

=

−

−=

−−

=

Z

Z

Z

n

PP

PZ

OO

OnX

893.6=Z to 3 decimal places

Source: The number of respondents that strongly agree or agree with the statement x is got from

the questionnaires administered, n = 740, Po, the prescribed proportion is 0.8 and the rest are

calculated.

From Table 4.8, it is shown that the calculated Z value which is 6.893 is greater than the table Z

value which is 1.645. So the null hypothesis is rejected and the alternative hypothesis is

accepted. So capacity planning to a large extent enhanced the performance in the brewing


Table 4.9 shows the computational details of the second hypothesis.

130

Table 4.9: The computational details of the second hypothesis

Year Increase in capacity


Rank Increase in materials


Rank d d2

1 5 10 4.5 9 1 1

2 4 7.5 4 7.5 0 0

3 3 5.5 3 5.5 0 0

4 2 2.5 2 2.5 0 0

5 2 2.5 2 2.5 0 0

1

95.0

20

19

20

01

20

20

20

11

)6)(4)(5(

)1)(6(1

)1)(1)((

61

2

=

=−=−=

−=

+−∑−=

s

s

s

s

r

r

r

nnn

dr

Source: The increases in the capacity requirements planning and materials requirement planning

are got from the questionnaires administered.

From Table 4.9, the Spearman’s rank correlation coefficient is 0.95 which is very close to 1, so

the null hypothesis is rejected and the alternative hypothesis is accepted. So there is a significant

positive relationship between capacity requirements planning and materials requirements

planning.

131

Table 4.10 shows the computational details of the third hypothesis.

Table 4.10: The computational details of the third hypothesis

Hypothesis number Calculated Z

value

Table Z value Statistical

Decision

3 8.271 1.645 Reject Ho

( )

271169499.8

4.0

308966909.3

4.0

)740)(8.0921621621.0(

)2.01)(8.0(

8.0740

682

)1(

=

=

−=

−

−=

−

−=

Z

Z

Z

n

Z

n

PP

PZ

OO

OnX

271.8=Z to 3 decimal places

Source: The number of respondents that either agreed or strongly agree, x is got from the

questionnaires administered, the prescribed proportion is 0.8 and the rest are calculated.

From Table 4.10, it is shown that the calculated Z value which is 8.271 is greater than the Table

Z value at 95% confidence level which is 1.645. So the null hypothesis is rejected and the

alternative hypothesis is accepted. So, capacity planning to a large extent sustains the

organization’s competitive advantage.

132

Table 4.11 shows the computational details of the Fourth hypothesis.

Table 4.11: The computational details of the Fourth hypothesis

Year Increase in capacity planning

Rank Increase in capacitybuilding

Rank d d2

1 5 9.5 5 9.5 0 0

2 4.5 8 4 7 1 1

3 3.5 6 3 5 1 1

4 2 2.5 2 2.5 0 0

5 2 2.5 2 2.5 0 0

2

9.010

9

10

1

10

10

10

11

)6)(4)(5(

)2)(6(1

)1)(1)((

61

2

=−==

−=

−=

+−∑−=

s

s

s

s

r

r

r

nnn

dr

Source: The increases in the capacity planning and capacity building over 5 years are got from

the questionnaires administered.

From Table 4.11, it is shown that the calculated Spearman’s correlation coefficient which is 0.9

is high and it is very close to 1. This shows that there is to a large extent, a positive relationship

between capacity planning and capacity building.

133

Table 4.12 shows the computational details of the fifth hypothesis.

Table 4.12: The computational details of the fifth hypothesis

Hypothesis number Calculated Z value Table Z value Statistical Decision

5 9.558 1.645 Reject Ho

( )

558.9

4.0

)740)(8.094859064.0(

740

)2.0)(8.0(

8.0740

696

)1(

=

−=

−=

−−

=

Z

Z

Z

n

PP

PZ

OO

OnX

Source: The number of respondents who either strongly agreed or agreed with the statement x is

got from the questionnaire administered, n = 740 and the prescribed proportion is 0.8 the rest are

calculated.

From Table 4.12, it is shown that the calculated Z value which is 9.558 is greater than the Table

Z value which is 1.645 at 95% confidence level. So, the null hypothesis is rejected and the

alternative hypothesis is accepted. So, the 12 steps of the capacity plan positively affected

profitability in the brewing industry in South Eastern Nigeria at 5% level of significance.

4.4 ANALYSIS OF THE RELATIONSHIP OF THE CONTINGENCY THE ORY AND

THE FIVE OBJECTIVES

Table 4.13 shows the analysis of the responses to the dichotomous oral interview questions on

the relationship between the contingency theory and the five objectives

134

Table 4.13: The analysis of the responses to the dichotomous oral interview questions on

the relationship between the contingency theory and the five objectives

s/n Question Yes in Number

% No in Number

% Total in Number

Total in %

1 Is the contingency theory related to

capacity planning to enhance the

performance in the brewing

industry?

738 99.73 2 0.27 740 100


the nature of the positive


requirements planning and

materials requirements planning?

736 99.46 4 0.56 740 100

3 Is contingency theory related to

ascertaining the large extent of the

positive relationship between

capacity planning and capacity

building?

734 99.19 6 0.81 740 100

4 Does contingency theory relate to

the large extent to which capacity

planning sustains the organization’s

competitive advantage?

737 98.92 8 1.08 740 100


the steps of capacity planning

which to a large extent aims to

develop the capacity plan that

positively affects the profitability of

the brewing industry?

730 98.65 10 1.35 740 100

Source: The questions and the responses were obtained from the dichotomous oral interview schedule distributed.

135

Table 4.13 shows the questions and the yes or no responses in absolute numbers and in

percentages. 740 respondents that were asked if contingency theory is related to capacity

planningto enhance the performance in the brewing industry, 738 out of 740 respondents making

99.73% of them said yes, while 2 of them making 0.27% of them said no. The 740 respondents

were asked if the contingency theory is related to the nature of the positive relationship between

capacity requirements planning and materials requirements planning, 730 out of the 740

respondents making 98.64% of them said yes, while 10 of them making 1.35% of them gave

answer to the contrary. The 740 respondents were asked if the contingency theory is related to

ascertaining to a large extent the relationship between capacity planning and capacity building.

734 out of the 740 respondents, 99.19% affirmed to it, while 6 of them had a negative view, that

is, 0.81%.

740 respondents were asked if the contingency theory is related to the view that, capacity

planning sustained the organization’s competitive advantage. 732 out of the 740 respondents,

making 98.92% affirmed to it while a handful, that is 8 (1.08%) of the respondents think

otherwise.

The 740 respondents were asked if contingency theory is related to the steps of capacity plan that

positively affects to a large extent the profitability in the brewing industry. A significant number,

730 (98.65%) of the 740 respondents gave a positive answer, while 10 (1.35%), an insignificant

number had a contrary opinion.

Table 4.14 shows the analysis of the responses on the relationship between the multi period

capacity problem and the five objectives.

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Table 4.14: The analysis of the responses on the relationship between the multi period capacity problem and the five objectives s/n Question Yes in

Number % No in

Number % Total in

Number Total in %

1 Is the multi period capacity

problem relevant for capacity

planning to enhance the

performance in the brewing

industry?

739 99.86 1 0.14 740 100


problem relevant to the nature of

the positive relationship between

capacity requirements planning

and materials requirements

planning?

737 99.59 3 0.41 740 100


problem relevant to ascertaining

that to a large extent there is a

positive relationship between

capacity planning and capacity

building?

735 99.32 5 0.68 740 100


problem relevant to a large extent

to ascertain how capacity planning

sustains the organizations


733 99.05 7 0.95 740 100

5 Is the multi period capacity problem relevant to the development of the steps of the capacity plan that positively affect profitability in the brewing industry?

731 98.78 9 1.12 740 100

Source: The questions and responses were sourced the dichotomous oral interview schedule distributed.

137

740 respondents were asked if the multi period was relevant for capacity planning to enhance the

performance in the brewing industry. 739 out of 740 of them giving 99.86% said yes.

Incidentally, a lone voice thatis only 1 person (0.14%) had a different view.

740 respondents were asked if the multi period capacity problem was relevant to the nature of the

positive relationship between capacity requirements planning and materials requirements

planning. 737 of them making 99.59% of them said yes, while 3 of them making 0.41% of them

gave answer to the contrary. The 740 respondents were asked if the multi period capacity

problem was relevant for ascertaining the extent of the positive relationship between capacity

planning and capacity building. 735 of them making 99.32% of them said yes, while 5 of them

making 0.68% of them said no.

740 respondents were asked if the multi-period problem was relevant to a large extent to how

capacity planning sustained the organization’s competitive advantage. 733 of them making

99.05% of them said yes, while 7 of them making 0.95% gave answer to the contrary. The 740

respondents were asked if the multi period capacity problem was relevant to the development of

the steps of the capacity plan that positively affect the profitability in the brewing industry. 731

of them making 98.78% said yes, and 9 of them making 1.22% had a contrary opinion.

Table 4.15 Gives the analysis of the data on how indigenous capacity building theory relates to

the five objectives.

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Table 4.15: The analysis of the data on how indigenous capacity building theory relates to

the five objectives

S/N STATEMENT RESPONSES X Z SA A U D SD

1 The indigenous capacity

building theory has a positive

relationship with the extent

to which capacity planning

enhances the performance in

the brewing industry in south

Eastern Nigeria

501 172 27 21 20 4.508 7.444



relationship with the nature

of relationship between the

capacity requirements

planning and materials


502 75 23 21 19 4.678 7.812



relationship with the extent

to which capacity planning

sustains the organizational

competitive advantage

601 84 20 18 17 4.668 8.547


planning theory has a

positive correlation with the

extent of the relationship

between capacity planning

and capacity building.

601 88 16 18 17 4.673 8.914


planning theory has a

positive relationship with the

602 97 14 15 12 4.705 9.833

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12 steps towards developing

a capacity plan and the

profitability in the brewing

firms in the area studied

Table 4.15 shows the statements, responses Strongly Agree (SA), Agree (A), Undecided (U),

Disagree (D) and Strongly Disagree (SD), the sample mean (X ) and the Z value. For the

statement that the indigenous capacity building theory has a positive relationship with the extent

to which capacity planning enhances the performance in the brewing industry in South Eastern

Nigeria, the sample mean is 4.508 which is greater than 3. The calculation Z value was 7.444

which is greater than the Table Z score at 95% confidence level which is 1.645. This shows that

most of the respondents strongly agree with the statement.

For the statement that the indigenous capacity building theory has a positive correlation with the

nature of the relationship between capacity requirements planning and materials requirements

planning, the sample mean is 4.648 which is more than the value of 3. The calculated Z value is

7.812 which is more than the Table Z value at 95% confidence level which is 1.645. This shows

that most of the respondents strongly agreed with the statement.

For the statement that the indigenous capacity building theory has a positive relationship with the

extent that capacity planning sustains an organization’s competitive advantage, the sample mean

is 4.668 which is greater than 3. The calculated Z value is 8.547 which is more than the table Z

value at 95% confidence level which is 1.645. This shows that most of the respondents strongly

agreed to the statement.

For the statement that the indigenous capacity building has a positive correlation with the extent

of the relationship between capacity planning and capacity building, the sample mean is 4.673

which is greater than 3. The calculated Z value is 8.914 which is more than the table Z value at

95% confidence level which is 1.645. So it shows that most of the respondents strongly agreed

with the statement.

For the statement that the indigenous capacity building theory has a positive relationship with the

12 steps towards developing a capacity plan and profitability in the brewing firms in the area

studied, the sample mean is 4.705, which is greater than 3. The calculated Z value is 9.833 which

140

is greater than the table Z value at 95% level of significance which is 1.645. This shows that

most of the respondents strongly agreed with the statement.

Discussion of findings relating the indigenous capacity building to the first objective

It was found that there is a positive relationship between the indigenous capacity theory and the

extent to which capacity planning enhances the performance in the brewing industry in South

Eastern Nigeria. The sample mean is 4.508 which is greater than three. So the sample mean lies

in the strongly agree part of the Likert scale continuum. The calculated Z value is 7.444 which is

greater than the Table Z value at 95% confidence level. This shows that most of the respondents

strongly agree with the statement.

Capacity Building

UNDP defined capacity building as the creation of an enabling environment with appropriate

policy and legal framework, institutional development, including community participation (of

women in particular), human resources development and strengthening of managerial systems,

adding that, UNDP recognizes the capacity building is a long-term, continuing process, in which

all stakeholders participate (ministries, local authorities, non-governmental organizations and

water user groups, professional associations, academics and others). Furthermore, capacity

building is the process of developing and strengthening the skills, instincts, abilities, processes

and resources that organizations and communities need to survive, adapt, and thrive in the fast-

changing world.

Capacity building is much more than training (Urban Capacity Building Network, 2010) and

includes the following:

• Human resource development, the process of equipping individuals with the

understanding, skills and access to information, knowledge and training that enables them

to perform effectively.

• Organizational development, the elaboration of management structures, processes and

procedures, not only within organizations but also the management of relationships

between the different organizations and sectors (public, private and community).

• Institutional and legal framework development, making legal and regulatory changes to

enable organizations, institutions and agencies at all levels and in all sectors to enhance

their capacities.

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For organizations, capacity building may relate to almost any aspect of its work: improved

governance, leadership, mission and strategy, administration (including human resources,

financial management, and legal matters), program development and implementation,

fundraising and income generation, diversity, partnerships, and collaboration, evaluation,

advocacy and policy change, marketing, positioning, planning, etc. For individuals, capacity

building may relate to leadership development, advocacy skills, training/speaking abilities,

technical skills, organizing skills, and other areas of personal and professional development.

Thus, capacity building is the elements that give fluidity, flexibility and functionality of a

program/organization to adapt to changing needs of the population that is served (Linnell, 2003).

Discussion of findings of the relationship between the indigenous capacity building theory

and the second objective

It was found that the indigenous capacity building theory has a positive correlation with the

nature of the relationship between capacity requirements planning and materials requirements

planning. The sample mean is 4.668. This shows that this mean score is in the strongly agree

Likert scale continuum. The calculated Z value is 7.812 which is greater than the table Z value at

95% confidence level which is 1.645. This shows that most of respondents strongly agree with

the statement.

Materials Requirement Planning (MRP) systems do not perform capacity planning, but they can

make it easier to plan and stay within the productive capacity. Using the MRP system, a manager

can select key productive units, such as the component facility for KBC, and have the computer

print out a “load projection” for that unit. This is done by examining orders that are currently in

production or planned. The result is a summary of the future activity of the productive unit that

allows the manager to look forward in planning the capacity. (Capacity is “planned” by

scheduling overtime, extra shifts, or subcontracting, for example.) (McClain and Thomas, 2007).

Some industries plan for very long periods of time, and therefore the plan must also include

expected orders, which are ones that may materialize. These can be listed as planned orders in

developing a load projection, but expected orders should be removed from the system after the

projection is made, to maintain the system’s validity. This information can help management to

see when a capacity problem is coming. Then overtime can be scheduled, orders can be

rescheduled, capacity can be expanded (by hiring, for example), or other actions can be taken. In

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this manner, the detailed scheduling tool (MPR) feeds into the higher-level problem of capacity

planning (Bufer, 2007).

In the intermediate run, managers face the aggregate production work-force planning problem.

The load projection feeds back to that plan to indicate how subunits are faring within the overall

aggregate plan. Prior to that, the aggregate plan was used as input to the MRP system in two

ways. First, the work-force decisions set the capacity level shown as “current capacity”. Second,

seasonal inventory plans lead to large lot sizes to build up the required seasonal inventory. The

MRP system responds to this in the same way it responds to any demand. This two-way

interaction allows the coordination of the aggregate plan and the detailed MRP (McClain and

Thomas, 2007).

The planning horizon for the MRP system is chosen considering the capacity planning problem.

The horizon should be longer than the cumulative lead time (total lead time for the product and

its predecessors) for any product, as stated before. It should also be long enough to allow

meaningful information to pass from the MRP system to the aggregate planner, with time for

appropriate action. In a company with seasonal inventory, this means that several months would

be a minimal planning horizon and a year would be better.

Finally, there is an interaction between the capacity in a unit and the lead time required. If there

is excess capacity, planned lead time can be set close to actual production time, in that waiting

time will be small. However, during peak demand periods when capacity is fully utilized, actual

lead time will frequently be much larger than production time. This makes the selection of

planned lead times difficult. A low planned lead time will occasionally be insufficient, and a

large planned lead time will cause excessive work-in-process inventories. A manager may

choose to invest in some additional capacity in the long run to avoid this problem. In addition,

capacity usage (load) projections can be used to predict and plan for production bottlenecks and

the associated increase in lead times (Unyimadu, 2007).

It is possible to use linear or integer programming methods to plan lead times and stay within

capacities, including the potential use of overtime. This approach is not in common use today.

The use of mathematical programming is explored in several problems.

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Discussion of findings on the relationship between the indigenous capacity planning and

the third objective

It was found that there was a positive relationship between the indigenous capacity planning

theory and the extent that capacity planning sustains an organization’s competitive advantage.

The sample mean is 4.608 greater than 3. This shows that the sample mean score lies in the

strongly agree part of the Likert Scale continuum. The calculated Z value is 8.547 which is

greater than the table Z value at 95% confidence level which is 1.645. So it shows that most of

the respondents strongly agree with the statement.

Competitive advantage refers to the particular properties of individual product and markets

which will give the firm a strong competitive position. It is something that the organization does

especially well with respect to its product and market and thus gives it an advantage over its

competitors (Evbayiro-Osagie, 2008).

A case in point is the competitive advantage which Guinness has in brewing Guinness Stout

since 1759. No other competing company, not even Nigerian Breweries Plc could brew a stout

brand that is patronized by consumers like Guinness Stout. Also, multinational brewing

companies have a competitive advantage that they can get raw materials fast from the parent

company. They can also get good prices for the raw materials.

Discussion of the Findings on the relationship of the indigenous capacity building theory

and the fourth objective

It was found that the indigenous capacity building theory has a positive relationship with the

extent of the relationship between capacity planning and capacity building. The mean score is

4.673 which is in the strongly agree part of Likert Scale Continuum. The calculated Z value is

8.914 which is greater than the table Z value at 95% confidence level which is 1.645.

An indigenous capacity-building model transcends the tendencies of the Western scientific

community to adhere to a more linear, static, time-oriented format, which is likely to impede

community involvement and discourage indigenous ownership. Rather, it must establish a

participatory process where mutual learning is taking place without the potential for abuses and

exploitation and repair lines of trust between non-indigenous researchers and tribal communities.

At the same time, however, the model must incorporate strategies for non-Native partners to

144

raise their awareness of tribal sovereignty and community issues, ensure adherence to

appropriate tribal guidelines and protocols, and become effective allies of indigenous people.

An indigenous model must reflect indigenous reality. It must integrate the past, the present and

the people’s vision for the future. It must acknowledge resources and challenges and allow

communities to build a commitment to identifying and resolving business concerns and issues.

An indigenous model works from the ground up, reversing the top-down application of Western

science to classic public enterprise that too often results in programs that are outside-in and

community placed, rather than community based (Goodman, 1998).

This literature identifies varies dimensions of capacity, such as participation, leadership, social

supports, sense of community, access to resources, and skills, and their importance in developing

and empowering local coalitions. Other parallel constructs have informed the literature on

community capacity, such as empowerment, the readiness of a community to work to improve

existing conditions, and the social capital, necessary for communities to move forward and

collaborate. Although some capacity-building models recognize the importance of community

history, they have yet to consider the importance of culture, language, issues of identity and

place, and the need for tribal people to operate in both traditional and dominant cultures. There is

now increasing dialogue among indigenous researchers about indigenous approaches to

knowledge that contrast with Western ways of knowing. These concepts go beyond cultural

competence and partnerships between Western institutions and indigenous community groups to

what Labonte (2002) called the transformation of power relationships, and to creating

frameworks based on community values and indigenous perspectives not typically included in

Western Models. Cajate (2000), for example, defined models that go beyond objective measures

and honour the importance of direct research agenda based on indigenous-centered priorities,

linking self-determination with decolonization, healing, mobilization, and transformation, which

suggests that indigenous people not only take charge of their own agenda but also name the

processes and employ methodologies that fit indigenous framing of place, community, values,

and culture.

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Discussion of the findings on the relationship of the indigenous capacity building theory

and the fifth objective

It was found that the indigenous capacity building theory has a positive relationship with the 12

steps towards developing a capacity plan and the profitability of the brewing firms in the area

studied. The sample mean is 4.705 which is in the strongly agree part of the Likert Scale

Continuum. The calculated Z score which is 8.833 is greater than the table Z value at 95%

confidence level which is 1.645. This shows that most of the respondents strongly agreed with

the statement.

Why is capacity building needed in Nigeria?

Nigeria is a nation of slow pace of progress made her leaders inspite of the abundant natural

resources. It is therefore, pertinent to the courting of implementing capacity building on the

following major development issues, prominent among them are:

i. Human resources development through reduction of abject poverty and corruption.

ii. Improvement in the provision of social services and infrastructures.

iii. Growth in employment and income generation.

iv. Increased agricultural productivity, science and technological advancement.

v. Increased emphasis on harnessing science and technology for rapid development.

vi. Environmental protection and regeneration of the natural resources bases.

vii. Good standard of education, in order to eradicate illiteracy.

viii. Better health condition for the masses.

ix. Women empowerment – this is necessary because women play an important role,

directly or indirectly in the listed activities above.

x. Provision of good governance and strategic management of resources.

xi. The imperative need for changing current practices for national development.

4.5 DISCUSSION OF FINDINGS

4.5.1 Results related to the first objective

Research Objective One: To determine the extent to which capacity planning enhances

performance in the brewing industry in South Eastern Nigeria

For the purpose of the concise and focus discussion, Table 4.4 will be relevant. 300 out of the

740 respondents making 40.501% of them strongly agreed while 367 of them making 49.595 per

cent of them agreed that capacity planning to a large extent enhances the performance in the

146

brewing industry in South Eastern Nigeria. The extent of agreement in this statement is also

shown in which the mean score is 4.239 which exceeds the cutoff point of 3.00, and this is in

agreement with the contention of Schuler and Youngblood, 2006) that says that brewing

companies in general and brewing companies in particular take the issue of capacity planning

and performance as very important. This is because without capacity planning, they will not be

able to determine the production capacity of their facilities in terms of the inputs, throughput or

produces and output and performance in terms of the extent to which they achieve or achieving

their organizational objectives

The finding that 9 out of 10 respondents said that capacity planning to a large extent enhanced

the performance in the brewing sector in South Eastern Nigeria has some implications. It meant

that the production capability of a brewingfirm could improve the ability of the brewing

organization to achieve its organizational objectives. Yomere and Osaze (2000) explained that an

objective is a short-term aim at a point in the organization’s mission.

No wonder Davis and Mabert (2000) have observed that in brewing organizations, many

important capacity planning decisions are made in production planning activities. These

decisions make the production planners to know when and with what resources organizations

produce their outputs optimally. These outputs are one of the ways of determining capacity

planning. The methods used to create the capacity plans are crucial in enhancing organization

performance. Organizational performance apart from being seen from the perspective of the

ability to achieve organizational objectives is also seen from the perspective of the ability of the

organization to fulfil the promises made to the stakeholders.

In the research question to this objective, the 740 respondents were asked whether capacity

planning to a large extent enhanced the performance in the brewing industry in South Eastern

Nigeria. 9 out of 10 of them said that to a large extent capacity planning enhanced the

performance in the brewing industry in South Eastern Nigeria and 1 out of 16 respondents had a

contrary opinion. This finding is consistent with the contention of Davis and Mabert (2000) that

in organizations, many capacity planning decisions are made in production planning which

enhance organizational performance.

In the hypothesis of this objective, it was found that capacity planning to a large extent enhanced

the performance in the brewing industry in South Eastern Nigeria. This finding was consistent to

147

that of Davis and Mabert (2000) that in many brewing organisations, many capacity planning

decisions are made in production planning that enhance organizational performance.

The alternative hypothesis was that capacity planning to a large extent enhanced the performance

in the brewing industry in South Eastern Nigeria. The hypothesis was tested in Table 4.8. The

calculated Z value was 6.893 to 3 decimal places which was greater than the Table value of

1.645. So the Null hypothesis was rejected and the alternative hypothesis was accepted. This

showed that capacity planning to a large extent enhanced the performance in the brewing

industry in South Eastern Nigeria. This means that to a large extent as capacity planning

increased, the performance of the organizations studied increased.

Item 1 of table 4.4 states that capacity planning to a large extent enhances the performance in the

brewing sector in South Eastern Nigeria, in support of this statement, 300 (40.541%) and 367

(49.595%) convincingly agreed.

Item 1 of table 4.6 states that capacity planning to a little extent enhances the performance in the

brewing sector in South Eastern Nigeria. , the responses were Strongly Agree, Agree, Undecided,

Disagree and Strongly Disagree. They had frequencies of 25, 25, 23, 367 and 300 out of 740

respectively. These gave relative frequencies of 0.034, 0.034, 0.031, 0.496 and 0.405

respectively.

From the oral interview, the 740 respondents were asked the extent to which capacity planning

enhanced the performance in the brewing sector in South Eastern Nigeria and 9 out of 10

respondents said it enhanced it to a large extent while 1 out of 10 respondents gave answers to

the contrary.

In the analysis of the responses to the dichotomous oral interview questions on the relationship

between the contingency theory and the five objectives, the 740 respondents were asked whether

contingency theory is related to capacity planning to enhance the performance in the brewing

industry, 738 out of 740 respondents making 99.73% of them said yes. 2 of them making 0.27%

of them said no.

In the analysis of the responses on the relationship between the multi period capacity problem

and the five objectives, the 740 respondents were asked whether the multi period was relevant

148

for capacity planning to enhance the performance in the brewing industry. 739 out of 740 of

them giving 99.86% of them said yes. 1 of them making 0.14% of them said no.

In the results related to the contingency theory and multi period capacity problem, it was found

that contingency theory was related to capacity planning to enhance the performance in the

brewing industry.

4.5.2 Results related to the Second Objective

Objective two: To ascertain the nature of relationship between capacity requirements

planning and materials requirements planning

The finding that 91 out of 100 respondents said that capacity requirements planning had a

positive relationship with materials requirements planning had some implications, the mean

value was got as 4.234 which is more than 3.00. This meant that as the determination of the

capacity needs of the brewing organization increases, the determination of the materials needs

increases. This is because as Berry, Schmidt and Vollmann (2004) have observed, capacity

requirements planning utilizes the time-phased material plan information produced by the

materials requirements planning system. So as the capacity needs and the time to utilize them are

determined the material’s needs and when and how they should be met are also determined.

This includes the consideration of all actual lot sizes as well as the lead times of when orders are

placed and when the materials are received. It includes balancing the open shop orders, schedules

and their receipts. If this is not done both capacity and materials may not be sufficient when they

are needed. This may lead to interruptions in production and this may lead to customer

complaints and loss of goodwill (Arnold and Chapman, 2011).

The research question to this objective, the 740 respondents were asked the nature of the

relationship between capacity requirements planning and materials requirements planning, 91 out

of 100 respondents said that capacity requirements planning had a positive relationship with

materials requirements planning. This finding is consistent with the contention of Berry et al

(2004) that capacity requirements planning utilized time phased materials plan information

produced by the materials requirements plan.

In the hypothesis of this objective, it was found that there was a significant positive relationship

between capacity requirements planning and materials requirements planning. This finding was

149

consistent with that of Berry et al (2004) that capacity requirements planning utilized time-

phased materials plan information produced by the materials requirements plan.

The alternative hypothesis was that there was a significant positive relationship between capacity

requirements planning and materials requirements planning. The hypothesis was tested in Table

4.9. The Spearman’s rank correlation between capacity requirements planning and materials

requirements planning was 0.95 which is very close to 1, so the null hypothesis was rejected and

the alternative hypothesis was accepted. This showed that there was a very high positive

correlation between capacity requirements planning and materials requirements planning. This

meant that as one increased, the other increased at the same rate.

Item 2 of table 4.4 states that there is a significant positive relationship between capacity

requirements planning and materials requirements planning. In support of this statement 304

(41.081%) and 370 (50.000%) strongly agreed and agreed.

Item 2 of table 4.6 states that there is a significant negative relationship between capacity

requirements planning and materials requirements planning, the responses were Strongly Agree,


304 out of 740 respectively. These gave relative frequencies of 0.028, 0.031, 0.30, 0.500 and

0.411 respectively.

From the oral interview, the 740 respondents were asked the nature of the relationship between

capacity requirements planning and materials requirements planning and 91 out of 100

respondents said that capacity requirements planning had a positive relationship with materials

requirements planning while 9 out of 100 respondents gave answers to the contrary.



the contingency theory is related to the nature of the positive relationship between capacity

requirements planning and materials requirements planning, 730 out of the 740 respondents

making 99.46% of them said yes. 6 of them making 0.54% of them said no.


and the five objectives, the 740 respondents were asked whether the multi period capacity

problem was relevant to the nature of the positive relationship between capacity requirements

150

planning and materials requirements planning. 737 of them making 99.59% of them said yes. 3

of them making 0.41% of them said no.


that contingency theory related to the nature of the positive relationship between capacity

requirements planning and materials requirements planning.

4.5.3 Results related to the Third Objective

Objective three: To ascertain the extent to which capacity planning sustains organizations’

competitive advantage

The finding that 23 out of 25 respondents said that to a large extent, capacity planning sustained

the organization’s competitive position had some implications, the mean value was 4.253 which

is greater than 3.00. It meant that ensuring the adequate production capability of a

brewingfacility gives the brewing organization advantages over firms in the same line of

business. The ability to match capacity requirements planning with materials requirements

planning gives the particular brewing company a competitive edge (Chen and Paulraj, 2004).

No wonder the two brewing companies namely the Nigerian Breweries Plc and Guinness Nigeria

Plc with the highest capacities as shown by the numbers of factories they have are multinational

in their operations. The multinational nature of their operations gives them high competitive

positions because they have access to malted barley from their parent companies, they have good

financial resources and they have access to good training and development facilities for their

staff (Guinness Nigeria Plc, 2011). As Chen, Paularaj and Lado (2004) put it; capacity planning

to a large extent sustains the organization’s competitive position in such areas as strategic

purchasing and supply management giving the organization good product-market scope, goals

and objectives and distinctive competence.

The research question to this objective, the 740 respondents were asked the extent to which

capacity planning sustained the organizations’’ competitive advantage, 23 out of 25 respondents

said that to a large extent capacity planning sustained the organizations’ competitive position

while 2 out of 25 respondents gave answers to the contrary. This finding agreed with the

contention of Chen et al (2004) that capacity planning enhanced the organizations’ competitive

position in such areas as strategic purchasing and supply chain management.

151

In the hypothesis of this objective, it was found that capacity planning to a large extent sustained

the organizations’ competitive position. This finding is in line with that of Chen et al (2004) that

capacity planning enhanced the organizations’ competitive position in such areas as strategic

purchasing and supply chain management.

The alternative hypothesis was that capacity planning to a large extent sustained organization’s

competitive advantage. The hypothesis was tested in Table 4.10. The calculated Z value was

8.271 which is greater than the Table value of 1.645. So the null hypothesis was rejected and the

alternative hypothesis was accepted. This shows that capacity planning to a large extent

sustained the organization’s competitive advantage. So this showed that capacity planning

increased along the same line with the competitive advantage of the organization. So it meant

that capacity planning could be used as a tool to make an organization have a better competitive

edge over its rivals.

Item 3 of table 4.4 states that capacity planning to a large extent sustains the organization’s

competitive position, in response to this statement, 302 (40.811%) and 380 (51.351%)

convincingly agreed.

Item 3 of table 4.6 states that capacity planning to a low extent sustains the organization’s

competitive position, the responses were Strongly Agree, Agree, Undecided, Disagree and

Strongly Disagree. They had frequencies of 19, 19, 20, 380 and 302 out of 740. These gave


From the oral interview, the 740 respondents were asked the extent to which capacity planning

sustained the organization’s competitive position and 20 out of 5 respondents said that capacity

planning to a large extent sustained the organization’s competitive position while 5 out of 25

respondents gave answers to the contrary.



the contingency theory is related to ascertaining the large extent of the relationship between

capacity planning and capacity building. 734 out of the 740 respondents making 99.19% of them

said yes, while 6 of them said no making 0.81% of them.

152



problem was relevant for ascertaining the extent of the positive relationship between capacity

planning and capacity building. 735 of them making 99.32% of them said yes. 5 of them making

0.68% of them said no.

In the results related to the contingency theory and multi period capacity problem, it was also

found that the contingency theory related to ascertaining the large extent of the positive

relationship between capacity planning and capacity building

4.5.4 Results related to the fourth objective

Objective four: To determine the extent of relationship between capacity planning and

capacity building

The finding that 93 out of 100 respondents said that capacity planning to a large extent had a

positive relationship with capacity building has some implications, and the mean value was

4.280 which was greater than 3.00. It meant that as the production capability of the brewing

facility increased, its creation of an enabling environment with appropriate policy and legal

framework, institutional development, including community participation of women in

particular, human resources development and strengthening of managerial systems increases. No

wonder Billey and Tesar (2008) have observed that capacity planning to a large extent has a

positive correlation with capacity building through internalization.

Internalisation which is the process of increasing the involvement in international operations has

developed overtime. A very good example of a brewing company that has been involved in

internationalization is Guinness Overseas Limited. From 1759 when the first Guinness beer was

brewed in Saint James’s gate in Dublin Ireland to date, the company has grown tremendously.

Guinness is now brewed in over 20 countries in the world. In Nigeria there are Guinness

Breweries at Ogba, Ikeja and Benin. Guinness is also brewed at Aba at the former Dublic

Factory Plant. Guinness is also brewed in Jos in the Jos metropolitan Factory Plant (Guinness

Nigeria Plc, 2011).

In the research question to this objective, the 740 respondents were asked the extent of the

relationship between capacity planning and capacity building and 93 out of 100 of them said that

to a large extent there was a positive relationship between capacity planning and capacity

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building and 7 out of 100 respondents gave answers to the contrary. This finding is consistent

with the contention of Billey and Tesar (2008) that capacity planning to a large extent has a

positive correlation with capacity building through internationalization.

In the hypothesis of this objective, it was found that there was a positive significant relationship

between capacity planning and capacity building. This finding is consistent with that of Billey

and Tesar (2008) that capacity planning to a large extent has a positive correlation with capacity

building.

The alternative hypothesis was that there was a positive significant relationship between capacity

planning and capacity building. The hypothesis was tested in Table 4.11. It was found that the

Spearmans’ rank correlation between capacity planning and capacity building was 0.9 which was

very close to 1. The null hypothesis was rejected and the alternative hypothesis was accepted at

5% level of significant. This showed that there was a significant positive relationship between

capacity planning and capacity building. This showed that as one increased, the other also

increases at the same rate and in the same direction.

Item 4 of table 4.4 states that there is a significant positive relationship between capacity

building and capacity planning, in response to this statement, 308 (41.622%) and 381 (51.486%)

convincingly agreed.

Item 4 of table 4.6 states that there is a negative correlation between capacity building and

capacity planning, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly



From the oral interview, the 740 respondents were asked the extent of the relationship between

capacity planning and capacity building and 93 out of 100 respondents said that to a large extent

there was a positive relationship between capacity planning and capacity building, 7 out of 100

respondents gave answers to the contrary.



the contingency theory related to the large extent to which capacity planning sustained the

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organization’s competitive advantage. 734 out of the 740 respondents making 98.92% of them

said yes. 8 of them making 1.08% of them said no.


and the five objectives, the 740 respondents were asked whether the multi-period problem

relevant to a large extent to which capacity planning sustained the organization’s competitive

advantage. 733 of them making 99.05% of them said yes. 7 of them making 0.95% of them said

no.


that the contingency theory was related to the large extent to which capacity planning sustained

the organization’s competitive position.

4.5.5 Results related to the fifth objective

Objective five: To access the steps toward developing a capacity plan and the profitability

in the brewing firms in the area studied

The finding that there are 12 steps of the capacity plan to improve the profitability of the brewing

industry in South Eastern Nigeria has some implications, the mean value was 4.305 which was

greater than 3.00. The steps could be summarized as determining the future capacity needs,

ascertaining the present capacity needs, deciding what to do if the future capacity needs exceed

or are lower than the present capacity needs; and executing the capacity decision and

implementing the capacity decision. Guinness Nigeria Plc has taken capacity decisions which

involved brewing Guinness, Harp and Malt in former Dubic Factory Plant at Aba and Jos

Metropolitan Factory Plant at Jos (Guinness Nigeria Plc, 2011).

This was when the future capacity needs were more than the present capacity needs. Other

capacity decisions they could have taken included outsourcing, or building new brewing plants.

To build a new factory plant is very costly. The brewing firm has to do a plan to determine the

project concept, facility location and layout, demand, financial feasibility, legal feasibility etc

which is not easy to do (Unyimadu, 2008).

In the research question to this objective, the 740 respondents were asked the steps that would be

used to develop capacity planning that would affect profitability in the brewing industry in South

Eastern Nigeria, it was found that there were 12 steps in a descending order starting from the

determinants of service requirements and ending at planning the future system configuration.

155

In the hypothesis of this objective, it was found that the steps towards developing a capacity plan

that would affect profitability in the brewing industry in South Eastern Nigeria were of the same

order of magnitude.

The alternative hypothesis was that the steps towards developing a capacity plan that would

affect profitability in the brewing industry in South Eastern Nigeria were of the same order of

magnitude. The hypothesis was tested in Table 4.12 and the calculated Z value was 9.558 while

the Table Z value was 1.645, so the null hypothesis was rejected and the alternative hypothesis

was accepted at 5% level of significance. This showed that the steps towards developing a

capacity plan that would affect profitability in the brewing industry were of the same order of

magnitude. This meant that each of the 12 steps even though they were in a hierarchy, each had

equal weight though the observed frequencies differed.

Item 5 of table 4.4 states that the steps towards developing capacity plan positively affected

profitability in the brewing industry in the area studied, in response to this statement, 315

(42.48%) and 381 (51.486%) convincingly agreed.

Item 5 of table 4.6 states that there are no steps towards developing capacity plan to improve the

profitability in the brewing industry in the area studied. the responses were Strongly Agree,


315 out of 740. These gave relative frequencies of 0.020, 0.020, 0.019, 0.515 and 0.426

respectively.

From the oral interview, the 740 respondents were asked the steps that could be used to develop

a capacity plan to improve the profitability in the brewing industry in South Eastern Nigeria. The

12 steps found were to determine service level requirements, to define work loads, to determine

the unit of work, to determine the service levels of each work load, to analyse current system

capacity, to measure service levels, to measure the overall resource usage, to measure resource

usage by workload, to identify the components of response time, to plan for the future, to

determine the future processing requirements and to plan the future system configuration in a

descending order of magnitude.



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contingency theory related to the steps of capacity plan that positively affects to a large extent to

which capacity planning positively affected the profitability in the brewing industry. 730 out of

the 740 respondents making 98.65% of them said yes. 10 of them making 1.35% of them said no.



problem was relevant to the development of the steps of the capacity plan that positively affect

the profitability in the brewing industry. 731 of them making 98.78% said yes. 9 of them making

1.22% of them said no.


that the steps of capacity planning which to a large extent aimed to develop a capacity plan that

positively affected the profitability of the brewing industry.

4.6 DISCUSSION RELATED TO THE CONTINGENCY THEORY AN D MULTI

PERIOD CAPACITY PROBLEM

The contingency theory assumes that the situation dictates the extent to which capacity planning

enhances the performance in the brewing industry in terms of profitability, effectiveness and


In brewing organizations, many important decisions are made in production planning activities.

Production planners decide when and with what resources organizations produce their outputs.

The methods that are used to create the plans are crucial to organizational performance (Kanet

and Sridharan, 1998; Davis and Mabert, 2000; Zwikael and Sadeh, 2007). Poor methods yield

plans that are either too loose and result in excessive lead times or too tight and result in failures

to keep promised delivery dates. Consequently, it is not surprising that planning methods have

represented a major research area in the operations management literature. Different planning

techniques have been studied especially in analytical and simulation-based research (Kouvelis et

al, 2005). That stream of research has produced various sophisticated algorithms that enable the

leveling and optimization of production plans (e.g., Davis and Mabert, 2000; Yang et al, 2002;

Deblaere et al, 2007).

Meanwhile, however, empirical researchers have repeatedly observed that most practitioners use

considerably less sophisticated planning methods than what is discussed in the academic

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literature. Moreover, empirical evidence indicates that those practitioners using advanced

planning methods are on average less satisfied with their plans then those who use simpler and

less accurate methods (Johnson and Mattsson, 2003). This section aims to use process

complexity as a contingency factor that explains why the practices of production planning often

differ from the academic model of production planning.

The analysis of this section employs the logic of strong inference and the contingency theory of

organizations to explain the determinants of different planning methods’ effectiveness. The

strong-inference logic refers to a research design, where theory building is based on tests of

competing hypotheses (Platt, 1964). The contingency-theoretical perspectives to process

complexity (e.g., Thompson, 1967) are used to propose that sometimes the most sophisticated

planning methods may be less effective than the simpler techniques. The contingency hypothesis

is tested against a hypothesis about the universal superiority of the most advanced planning

methods. The statistical results from the survey dataset are complemented by the interview

dataset that sheds light on the reasons why practitioners end up using certain planning methods.


units may pursue the possibly conflicting objectives of their own (March and Simon, 1958).



low, coordination can be achieved through rules and heuristics (Cyert and March, 1963). In

manufacturing management, the planning-focused methods have been developed around the

concept of material requirements planning (MRP, Orlicky, 1975), while the methods that

emphasize rule-based control and simplicity are founded on the just-in-time (JIT) methodology

(Ohno, 1988).

A classic way to pursue simplification in brewing is to isolate operations from external

uncertainties (Thompson, 1967). The extent of the isolation depends greatly on the order

penetration point (Olhager, 2003): the earlier the order-specific requirements are taken into

account, the higher is the exposure to the environment. That is why planning methods are most

important in the MTO manufacturing and the JIT methods are at their best in the make-to-stock

environments (Karmarkar, 1989; Vollmann et al, 2005). Usually both approaches coexist in

assemble-to-order systems and other intermediate settings. The postponement of the order

penetration point enables the use of JIT methods in the upstream operations of customized

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manufacturing (Olhager and Rudberg, 2002). However, the inherent complexity of producing

according to individual orders cannot be eliminated by forcing JIT methods upon the MTO parts

of the processes (Hopp and Spearman, 2004). Hence, the time-phased planning has remained as a

vital part of manufacturing management despite the important contributions of JIT. Recent

literature has described several techniques for integrating the benefits of the two paradigms. The

techniques are known by many names (e.g., CONWIP, POLCA, COBACABANA, etc.) and they

differ in details but they share the main idea of using the pull logic of JIT for the purposes of

shop floor control and time-phased planning methods for the creation of production schedules

(Spearman et al, 1990; Suri, 1998; Land, 2009).

Contemporary methods of time-phased production planning are based on the manufacturing

resource planning (MRPII) framework. It was originally developed to complement MRP with

capabilities to check material plans’ feasibility against capacity constraints (Landvater and Gray,

1989). Later, more advanced applications of MRPII have been developed so that the feasibility

checks could be extended to other factors such as delivery schedules and financial constraints

(Yusuf and Little, 1998). However, the practical implementations of such solutions have

remained rare (McKay and Wiers, 2004). In fact, it has been observed that even the capacity

planning features of MRPII are far less utilized than what could be expected on the bases of the

academic literature (Halsall et al, 1994; Kemppainen, 2007). As the material-planning parts of

MRPII are well-established (Vollmann et al, 2005), the observation implies that companies’

production planning practices can be measured through the methods that they use in capacity

planning.

Recent developments in enterprise software deliver a promise of easily applicable capacity

planning tools. While the conventional ERP systems are well-suited for the simpler capacity

checks (Wortmann et al., 1996), the so-called advanced planning and scheduling (APS) systems

promote the more sophisticated methods (Kreipl and Pinedo, 2004; Stadtler and Kilger, 2005).

However, companies’ diligence in applying their enterprise systems’ features is known to vary

considerably (e.g.., Bendoly and Cotteleer, 2008). Thus, variance may be found also in the

utilization of the capacity planning features. That variance enables testing whether complex

organizations that do not put efforts in planning suffer from the lack of coordination (e.g., March

and Simon, 1958; Zwikael and Sadeh, 2007). Consequently, the following hypothesis is

presented as the underlying assumption of the study:

159

It is reasonable to assume that not only the efforts in planning but also the ways of planning

matters. The main method of time-phased production planning according to the framework of

Vollmann et al (2005) is described below:

1. Non-Systematic capacity planning

- Materials (Mater Production Scheduling) (Material Requirements Planning)

- Bills of materials (Material Requirements Planning)

- Priority scheduling rules (Input/output Control)

2. Rough-Cut Capacity Planning (RCCP)

- Rough-cut profiles

3. Capacity Requirements Planning

- Capacities (labour and machines)

- Work enters

- Routings

4. Finite Loading with Capacity Leveling

- Shift schedules

- Move time matrices

- Setup time matrices

- Capacities (tool, jigs, etc.)

5. Finite Loading with Optimization

- Objective functions

- Parameters (processing/setup/delay/costs, products/customer priorities, etc.)

Alternative methods in capacity planning.

The practical relevance of the framework is high because dominant ERP software providers have

structured their production planning modules in the same fashion (e.g., SAP, 2009). In addition,

most textbooks either refer to it directly or provide illustrations that closely resemble it (e.g.,

Hill, 2005; Slack et al., 2007; Stevenson, 2004).

The backbone of the planning process is in the material planning activities, that is: master

production scheduling (MPS), MRP, and the input/output (I/O) control (Vollmann et al., 2005).

The optional activities are on the side of capacity planning. In the illustration, they are numbered

in the order of sophistication. The illustration shows that the amount of required data records

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increases as the methods get more sophisticated. The increase is cumulative because the records

do not fully substitute each other. Brief descriptions of each method are given in the following:

Non-systematic capacity planning represents inexplicit consideration of capacity constraints. At

the level of mater schedules, it means that planners use their personal experience to evaluate the

feasibility of plans (Proud, 2007). In MRP, the inexplicit capacity considerations are realized

through the lead time parameters of bills of materials. The processing lead times represent the

averages, while the variances around the averages are taken into account with safety lead times

(Vollmann et al, 2005). In the I/O control, priority scheduling rules can be used to level capacity

utilization without formal planning activities (Green and Appel, 1981; Kemppainen, 2007).

Rough-cut capacity planning (RCCP) is the simplest systematic method. It can be done with

several techniques but they all share the common characteristic of aggregation (Wortmann et al.,

1996). Materials are aggregated to end products or product groups and capacities to production

lines or resource groups (Proud, 2007). RCCP simplifies planning by ignoring subassembly

inventories, operations’ sequences, setups, and batch sizes but still provides the planners with a

systematic means to supervise how the resource utilization accumulates during the MPS activity

(Vollmann et al, 2005). That is an advantage if mater schedules are updated frequently, MPS

items are numerous, or different MPS items load the same resources. In such situations, the non-

systematic methods are prone to human errors and easily result in overloaded schedules.

Capacity requirements planning (CRP) provides a more detailed technique for checking material

plans’ feasibility. The CRP calculations are done not only for the end products but also for the

subassemblies. In addition, the routing data enable calculation loads at individual resources and

considering the effects of operations’ sequences, setups, and batch sizes. Thus, CRP corrects for

the simplifications of RCCP and helps generating more reliable schedules. Iterating the plans to

achieve feasibility in terms of resources’ capacity limits is done manually by human planners

(Burcher, 1992; McKay and Wiers, 2004).

The next step from CRP is to automate the iterations of the plans. It can be done with finite

loading methods that are usually featured in APS systems (McKay and Wiers, 2004). The

process of using them is typically the following: first, material plans are downloaded from an

ERP system. Then, the algorithms of the finite leading software are used to find a solution,

where capacity constraints are satisfied with the fewest breaches of due dates. Finally, the

161

revised plans are uploaded back to the ERP system, where they are executed (Stadtler and

Kilger, 2005). The obvious benefit of automating the capacity leveling is that it reduces the room

for human errors.

In addition to capacity leveling, the finite loading algorithms can be used to solve more

complicated scheduling problems. The finite leading tools with optimization may be used, for

example, to maximize throughput or to minimize setups or downtimes (e.g., Davis and Mabert,

2000). Such techniques require the most planning parameters and their outputs are highly

dependent on the accuracy of the parameters. Yet, the data maintenance efforts and the

investments in the software may well be justified in some manufacturing environments, for

example in capital intensive production systems (Kreipl and Pinedo, 2004; Stadtler and Kilger,

2005).

The planning methods are by no means mutually exclusive. Instead, several methods can be used

simultaneously for different purposes (Meal, 1984). For example, plant managers can use RCCP

to evaluate sales plans, master schedulers may use CRP to supervise their processes, and

production planners can do the finite loading of critical resources. A concept that brings clarity to

this plurality is bottom-up re-planning (Fransoo and Wiers, 2008; Vollmann et al., 2005). It

means that master schedules are updated on the bases of the lower-level planning activities. In a

closed-loop planning system, themaster schedules are based on the finite loading of critical

resources (Kenat and Sridharan, 1998). In an intermediate solution, the master schedules are

revised on the bases of CRP. Consequently, the main method of planning can be identified. It is

the method that determines the output to which the manufacturing function commits itself.

As all of the advanced planning methods aim to reduce errors in planning, it can be proposed that

they should have a positive effect on operational performance. Some studies have already

implied evidence of such as effect (Sheu and Wacker, 2001; Wacker and Sheu, 2006). Yet, they

have not included finite loading techniques, which is a major shortcoming because substantial

effort has been put into their development (Kouvelis et al, 2005). The development of

progressive algorithms and software would be well justified if there was evidence on the

relationship between the accuracy of planning and performance.

Another perspective to different planning methods’ effectiveness is to assume that methods’

suitability would depend on the context of their usage. Preliminary support for such an argument

162

can be found in the surveys of Jonsson and Mattsson (2002:2003). They show that practitioners’

satisfaction with different planning techniques depends on the type of their production processes:

the managers of job shops are content with RCCP, the most satisfied users of CRP work in

batch-process plants, and the finite loading methods are most popular in production lines.

The observations are aligned with the systematic review of Sousa and Voss (2008) which

indicate that the process type is a typical contingency factor for the effectiveness of various

operations management practices. In the context of planning, the influence of the process type

can be explained with two classic contingency-theoretical constructs: the repetitiveness and the

complexity of the tasks that constitute the processes (Perrow, 1967; Woodward, 1965).

- RCCP fits with the job shops because in low-volume and high-variety

environments, the data records of the more detailed methods are difficult to

maintain. Moreover, the more detailed resource-specific plans are not necessary

because the complexity of the system is limited with general-purpose machinery

and widely skilled workforce (Blackstone and Cox, 2005; Hill, 2007).

- CRP fits with the batch processes because the more repetitive operations make the

maintenance of the data records worthwhile. Furthermore, information about the

resource-specific workloads is necessary because the resources are more

specialized, and different products utilize them differently (Jonsson and Mattsson,

2003; Wortmann et al., 1996).

- Finite loading methods fit with batch processes, whose complexity is reduced

with bottleneck control (Goldratt and Cox, 1984; Vollmann, 1986). Finite loading

works in a batch process if a stationary bottleneck can be identified and all other

resources are subordinated to its schedule. Otherwise, each finite loading of one

resource can make another resource a new bottleneck, and consequently the

iteration of the plans may become endless.

- In production lines, the complexity is low because all resources are subordinated

to the flow of the line. Thus, the capacity of the entire line can be planned as a

single resource. Detailed planning is desirable because untimely changeovers can

be costly in larger assembly lines (Hayes and Wheelwright, 1979; Kreipl and

Pinedo, 2004) or cause congestion in smaller manufacturing cells (Venkatesan,

1990; Vandaele et al, 2008). In addition, the repetitiveness of operations makes it

163

easier to maintain the parameters of the most sophisticated methods (Safizadeh

and Ritzman, 1997; Stadtler and Kilger, 2005).

The relationship between the process types and planning methods can also be explained with the

interdependence between the resources of the processes. As discussed earlier, the alternative

types of interdependence are pooled, sequential, and reciprocal (Thompson, 1967; Donaldson,

2001). The pooled and the sequential processes are the simplest to coordinate but they have very

different implications for planning (Barki and Pinsonneault, 2005). The processes with pooled

resources are inherently flexible, and that is a capability that should not be constrained with too

stringent planning. A job shop is an archetype of pooled interdependence (Galbraith, 1973).

Meanwhile, the sequential processes are suited for efficiency, while is a capability that can be

fostered with detailed planning. In manufacturing environments, sequential relationships exist in

production lines and around the bottlenecks of batch processes (Thompson, 1967; Woodward,

1965).

The most difficult processes to coordinate are those where resources are reciprocally

interdependent. That is because all actions by any resource may affect multiple other resources

(Galbraith, 1973; Monahan and Smunt, 1999). Some specificity in planning is necessary to

prevent undesirable cascade effects but getting into the details is difficult because the possible

interactions are numerous (Tushman and Nadler, 1978). Therefore, a moderately sophisticated

planning method such as CRP is the most suitable option for the reciprocal processes of batch

shops (Reeves and Turner, 1972).

In summary, classic contingency-theoretical concepts produce a meaningful fit proposition that

challenges the hypothesis on the universal superiority of the most sophisticated planning

methods.

The existence of two competing hypotheses calls for a strong inference research design. It is an

inductive approach, where theory building is based on tests of mutually excluding hypotheses

(Platt, 1964). Strong inference studies must be carefully designed so that the research settings do

not favour any of the rival hypotheses (MacKenzie and House, 1978). Multiple data sources are

also necessary: quantitative data enable the testing of the hypotheses while qualitative data

provide the understanding that is needed in the development of theory (Jick, 1979; Gupta et al,

2006). Although the strong inference research design was originally developed for experimental

164

studies (e.g., Nadler et al., 2003), it has been employed successfully in non-experimental

empirical research as well (e.g., Shaw et al., 2005).

The efforts in capacity planning were operationalized with two formative indicators. They

represent the main aspects of efforts in formal routines: the organizational deployment of the

routine (i.e., the structuration aspect), and individuals’ efforts to follow the routine in their work

(i.e., appropriation, DeSanctis and Poole, 1994). The formative operationalization is suitable

because the latter aspect does not always follow from the former and because studies have shown

that both aspects are necessary for the routines to be effective (Devaraj and Kohli, 2003). This

simple operationalization was used because the more sophisticated measures of planning efforts

are typically tied to certain planning methods (e.g., Zwikael and Sadeh, 2007).

A strategy in multi-period problem contains two types of decisions: the sequence of contracts to

be used and the amount of capacity to acquire after choosing the sequence of contracts. There are

an exponential number of combinations of contracts that the manufacturer can choose from. To

evaluate one strategy, the firm needs to solve a large scale stochastic linear problem, e.g.

Problem (11), to find the optimal contract sizes. Therefore, the multi-period problem is much

more complex than the single period problem.

In the following sections, we will develop an efficient heuristic algorithm that can find a good

capacity plan for the multi-period problem under Assumption 1. The same heuristic algorithm

will also provide a good upper bound to verify the effectiveness of the capacity plan.

The expectations of the demands during the planning horizon are given. Product 1 is introduced

to the market at the beginning of the first month. Its demand grows with time and reaches its

peak at the fifth month. After that, the market is saturated and the demand starts to drop. Product

2, on the other hand, is a mature product at the beginning and as time passes by, it phases out. At

the seventh month, the manufacturer introduces a new version of product 2 and it starts to gain

more demand from then on. The standard deviations of the demands of both products at each

period are 10.

Both products are sold at N9,750. All processes have the same price structure. Each process

offers contracts in four different durations: 1 month, 3 months, 6 months and 12 months. The

corresponding prices of the fixed-price and option contracts are also given. The contracts with

longer duration have lower per-period prices.

165

Given the supply chain structure, demand information and contract information, the

manufacturer needs to make the following decisions:

1. What sequence of contracts that it should use for each process,

2. What types of contract (fixed-price and option) that it should use, and

3. How much capacity it should reserve or buy for each type of contract (Barahona et al,

2005).

Decision 2 and 3 are the same as in the single period case while decision 1 is unique to the multi-

period problem. Since the example only contains dedicated resources, the manufacturer does not

need to choose suppliers. However, similar to the single period problem, the firm still faces the

other trade-offs that involve demand uncertainty, common process, coordination among the

processes of the same product, and option capacity. Moreover, the manufacturer needs to

consider the trade-off between contract flexibility and prices. Should it use shorter contracts to

match the demand or should it take advantage of lower prices by using longer contracts?(Simchi-

Levi, 2005).

For this example, the sequences of the contracts for the processes suggested by the algorithm are

given.

1. For process 1, the manufacturer should use two 1-month contracts to cover the first two

periods. It can then obtain a 6-month contract to cover month 3 to month 8. Following

another 1-month contract in month 9, it should get a 3-month contract to cover the rest of

the planning horizon.

2. For process 2, the manufacturer should take full advantage of the low price from a longer

contract and secure the capacity for 12 months with the 12-month contract.

3. For process 3, the manufacturer should use a 3-month contract to cover month 5, 6, and 7.

For the other months, it should use 1-month contracts (Devaraj and Kohli, 2003).

The contracts reserved for process 3 vary to match the demands. On the other hand, the contract

reserved for process 2 is fixed over the horizon and does not fluctuate with the demand. We also

notice that for the contracts with a long duration, the option capacity component is

significant.Finding the right level of flexibility in terms of shorter contracts and/or in the use of

option contracts, is a complex problem that needs to consider demand variability, product profits,

contract durations, and contract.

166

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172

CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSION, RECOMMENDATIONS,

CONTRIBUTION TO KNOWLEDGE AND SUGGESTIONS FOR

FUTURE RESEARCH

5.1 SUMMARY OF FINDINGS

The specific objectives of the study were:

To determine the extent to which capacity planning enhanced the performance in the brewing


To ascertain the nature of relationship between capacity requirement planning and material


To ascertain the extent to which capacity planning sustains organisations’ competitive

advantage.

To determine the extent of relationship between capacity planning and capacity building.

To determine the steps toward developing a capacity plan and the profitability in the brewing

firms in the area studied.

It was found that:

Capacity planning to a large extent enhanced the performance in the brewing industry in

South Eastern Nigeria. There was significant positive relationship between capacity

requirements planning and materials requirements planning.Capacity planning to a large

extent sustained the organizational competitive advantage.There was positive relationship

between capacity planning and capacity building.The 12 steps starting from to determine

service level requirements and ending in to plan the future system configuration were in a

descending order of magnitude and of the same order of magnitude.

5.2 CONCLUSION

The finding that capacity planning enhanced the performance in the brewing industry in South

Eastern Nigeria implied that it made the brewing companies studied to achieve their

organizational goals and objectives. It also made them to fulfil the promises the companies made

to their numerous stakeholders. It positively affected the behaviour of the factory senior and

junior staff towards striving to achieve the organizational goals and objectives.

173

The finding that there was a significant positive relationship between capacity requirements

planning and materials requirements planning implied that there was a positive correlation

between them. This meant that materials requirements planning which was a method of

coordinating the detailed production plans could lead to an enhancement of capacity

requirements planning which meant taking future decisions on the items needed for the

production capability of the brewing facility. Both processes were multi stage ones which began

with a master capacity schedule and master materials schedule. Both of them worked backwards

to determine when and how the component would be needed.

The finding that capacity planning to a large extent sustained the organizations’ competitive

position implied that capacity decision making, forecasting and simulating the capacity objective

sustained the organizations’ standing when compared with the firms in the same line of brewing

business. So to get a distinctive competence, a brewing company needed capacity planning. So

capacity planning was needed by a brewing company to retain its customers and present and get

potential customers in the future.

The finding that there was a positive relationship between capacity planning and capacity

building implied that as one increased the other increased. So planning for the production

capability of a brewing facility could enhance the capacity environment especially in such areas

as human resource development and planning. So capacity planning could enhance capacity

building through internationalization. This is pertinent in the petroleum industry where even

though the Nigerian people do not have the technology by joint venture relationship between

N.N.P.C and the crude oil producing and servicing companies, the Nigerian economy is

dependent on crude oil and associated gas exports.

The finding that there were 12 steps towards developing a capacity plan that positively affected

profitability in the brewing industry in South Eastern Nigeria, starting from determining the

service levels and ending in planning the future system and they were in a descending order of

importance but of the same order of magnitude had some implications. It meant that the 12 steps

could be arranged in a hierarchy. It also meant that statistically, each step was as important as the

other for proper functioning.

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1.3 RECOMMENDATIONS

It is recommended that the strategic and production managers of the brewing companies studied

should be backed bythese policies:

That the use ofcapacity planning as a technique to improve all performance factors.

That the gain from the correlation of capacity requirements plans and materials requirements

planning.

Sustain the organizational distinctive competence standing using capacity planning.

Exploit the advantages of the positive synergy between capacity planning and capacity

building.

Going through the 12 steps were of capacity planning for proper functioning and a balanced

score card.

1.4 CONTRIBUTION TO KNOWLEDGE

Davis and Mabert (2000) worked on the effect of capacity planning decisions in organizational

performance. They found that it was useful in enhancing production planning and improving

organizational performance. Bary et al worked on the effect of time-phased capacity

requirements planning on materials requirements planning in manufacturing organizations. They

found that time-phased capacity requirements planning utilized the counterpart materials

requirements planning system. So the capacity needs and the time to utilize them are determined

by the materials needs and when and how they are met. They also found that both capacity

requirements planning and materials requirements planning had lead times which was the time

lag from the time orders are placed and the time the order is required in the production process.

Chen et al (2004) worked on the relationship between capacity planning and brewing

organization’s competitive position. The aspects of competitive position they covered were in the

areas of strategic purchasing and supply management. Both sections were important aspects of

materials management aimed at ensuring that materials were available in the correct quantity,

quality and at the time needed to ensure a continuous production process. They found that the

ability to match capacity and material requirements planning gave the brewing company a

competitive edge.

Vollmann (2000) worked on the correlation between capacity building and capacity planning.

They emphasized the need for a conducive capacity environment by ensuring that the business

kept to all the legal rules and utilized all effective human resource development and planning. So

175

capacity building ensured a congenial work environment. They found that there was a positive

correlation between capacity planning and capacity building.

Eta (2012) worked on indigenous capacity building for internationalization of burgeoning

medium-scale enterprises (MSEs) in Nigeria for his M.Sc thesis. He identified the major areas of

strategy development by indigenous MSEs for building capacity and improving competitiveness

in a globalised market.

In all, there have been a lot of empirical studies on the effect of capacity planning on the

performance of brewing companies in Europe and America. Some Nigerian researchers like

Ohno and Nwachukwu in 1998 and 2004 repectively, have worked on the effect of capacity

building on internationalization. However, to the best knowledge of the researcher, no other

researcher has worked on how capacity planning enhances performance in the brewing industry

in South Eastern Nigeria a prime focus of this study in a developing African country. Also, the

contingency theory of capacity planning and the multi period theory of capacity planning were

empirically applied to the brewing industry in South Eastern Nigeria.

1.5 SUGGESTIONS FOR FUTURE RESEARCH

This Research work has concentrated onhow capacity planning enhances the performance of five

brewing companies located in the five South Eastern States (Abia, Anabmra, Eboyi, Enugu and

Imo States). It will be worthwhile if the study is extended to the other 31 States in the other five

geopolitical zones in Nigeria and the Federal capital to make for a better generalization of

capacity planning and performance in the brewing industry in Nigeria.

176

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190

APPENDIX I

SECTION I: Personal Data

(1) Sex: (a)Male [ ] (b)Female [ ]

(2) Marital Status: (a) Married [ ] (b)Single [ ] (c) Divorced [ ] (d) Widowed

[ ] (e) Separated [ ]

(3) Age: (a) Less than 20 years [ ](b) 21 – 30 years [ ] (c) 31 – 40 years [ ] (d) 41 – 50

years [ ] (e) 51-60 years [ ]

(4) Highest Educational Qualifications: (a)Senior school certificate [ ]

(b) R.S.A [ ] (c) Diploma [ ] (d) O.N.D [ ] (e) H.N.D [ ]

(f) Second Degree [ ] (g) Ph.D. [ ] (h) A.C.A [ ]

(5) Status: (a) Senior Staff [ ] (b) Junior Staff [ ]

(6) Duration worked (tenure): (a) 1-10 years [ ] (b) 11-20 years [ ]

(c) 21-30 years [ ] (d) 31-40 years [ ]

SECTION II: Data on the effect of capacity planning on performance.

From question 7, give answers not Strongly Agree (SA), Agree (A), Undecided (U),

Disagree (D) and Strongly Disagree (SD).

191

S/NO STATEMENT SA A U D SD

Objective 1: What is the extent to which capacity

planning enhances the performance in the brewing

sector?

(7) Capacity planning to a little extent enhances the

performance in the brewing sector in South Eastern

Nigeria.

(8) Capacity planning to a large extent enhances the

performance in the brewing sector in South Eastern

Nigeria.

(9) Adding capacity in anticipation of an increase in

demand increases the performance in brewing sector

in Southern Nigeria.

(10) Adding capacity only after the organisation is

running at full capacity due to increase in demand

increases the performance in the brewing sector in

Southern Nigeria.

192


Objective II: to ascertain the nature of the

relationship between capacity requirements

planning and material requirements planning?

(11) There is significant positive relationship between

capacity requirements planning and material

requirement planning.

(12) There is significant negative relationship between

capacity requirements planning and material


(13) There is no relationship between capacity

requirements planning and material requirements

planning.

(14) Material requirements planning have a positive

correlation with capacity requirements planning.

193


Objective III: to ascertain the extent to which

capacity planning sustains organization’s


(15) Capacity Planning sustains organization’s

competitive advantage to a large extent.

(16) Capacity Planning sustains organization’s

competitive advantage to a low extent.

(17) The extent to which Capacity Planning sustains

organization’s competitive advantage is not

obvious.

(18) The extent to which Capacity Planning sustains

organization’s competitive advantage is obvious.


Objective IV: To determine the extent of

relationship between capacityplanning and capacity

building.

(19) There is a significant positive relationship between

capacity building and capacity planning.

(20) There is a positive correlation between capacity

building and capacity planning.

(21) There is a negative correlation between capacity

building and capacity planning.


Objective V: To determine the steps towards

developing a capacity plan to improve the

profitability in the brewing sector in the area to be

studied.

(22) The first step is to determine service level requirements.

(23) The second step is to define workloads.

194

(24) The third step is to determine the unit of work.

(25) The fourth step is to identify service levels of each

workload.

(26) The fifth step is to analyze current system capacity.

(27) The sixth step is to measure service levels.

(28) The seventh step is to measure overall resource

usage.

(29) The eighth step is to measure resource usage by

workload.

(30) The ninth step is to identify components of response

time.

(31) The tenth step is to plan for the future

(32) The eleventh step is to determine future processing

requirements.

(33) The twelfth step is to plan future system

configuration.

195

APPENDIX II

ORAL INTERVIEW SCHEDULE

(1) What is the extent to which capacity planning enhances the performance in the brewing

sector in South Eastern Nigeria?

……………………………………………………………………………………………

…………………………………………….………………………………………………

(2) What is the nature of the relationship between capacity requirements planning and

material requirements planning?

……………………………………………………………………………………………

………………………………………..…………………………………………………

(3) What is the extent to which capacity planning sustain organisations; competitive

advantage?

……………………………………………………………………………………………

……………………………………………………………………………………………

(4) What is the extent of the relationship between capacity planning and capacity building?

……………………………………………………………………………………………

…………………………………………………….……………………………………..

(5) What are the steps that could be used to develop a capacity plan to improve the

profitability in the brewing sector in the area to be studied?

……………………………………………………………………………………………

……………………………………………………………………………………………

196

APPENDIX III

DICHOTOMOUS ORAL INTERVIEW SCHEDULE ON THE CONTINGE NCY

THEORY OF CAPACITY PLANNING

s/n Questions Yes No

1 Is the contingency theory related to capacity planning

to enhance the performance in the brewing industry?

2 Is the contingency theory related to the nature of the

positive relationship between capacity requirements

planning and materials requirements planning?

3 Is contingency theory related to ascertaining the large

extent of the positive relationship between capacity

planning and capacity building?

4 Does contingency theory relate to the large extent to

which capacity planning sustains the organization’s


5 Is the contingency theory related to the steps of

capacity planning which to a large extent aims to

develop the capacity plan that positively affects the

profitability of the brewing industry?

197

APPENDIX IV

DICHOTOMOUS ORAL INTERVIEW SCHEDULE FOR IMPLEMENTIN G THE

CAPACITY MULTI-PERIOD PROBLEM

S/n Question Yes No

1 Is the multi period capacity problem relevant

for capacity planning to enhance the

performance in the brewing industry?


to the nature of the positive relationship

between capacity requirements planning and

materials requirements planning?


to ascertaining that to a large extent there is a

positive relationship between capacity

planning and capacity building?


to a large extent to which capacity planning

sustains the organizations competitive

advantage?


to the development of the steps of the capacity

plan that positively affect profitability in the

brewing industry?

198

APPENDIX V

Calculation of Cronbach’s Alpha Co-efficient of Reliability

∑=

−=

2

2

11 x

yi

K

K

σσ

α

where:

α = Cronbach’s Alpha Co-efficient of Reliability

K = Number of questions in the questionnaire

2xσ = The variance of the observed total test scores

2yiσ∑ = The sum of the variance of the component, i for the pilot sample of persons

,53=k 2yiσ∑ = 0.0085, 2

xσ =0.05

∑=

−=

2

2

11 x

yi

K

K

σσ

α

=

=− 05.0

0085.01

153

53

α = 1.0192(1 – 0.17)

= 83.00192.1 ×

845936.0=

0.9

85.0

≈=α

199

APPENDIX VI

Results related to the Personal Data of the Respondents

For the purpose of the concise and focus discussion, Table 4.4 will be relevant. 300 out of the

740 respondents making 40.501 per cent of them strongly agreed while 367 of them making

49.595 per cent of them agreed that capacity planning to a large extent enhances the performance

in the brewing industry in South Eastern Nigeria. The extent of agreement in this statement is

also shown in which the mean score is 4.205 which exceeds the cutoff point of 2.00, and this is

in agreement with the contention of Schuler and Youngblood, 2006:) that says that

manufacturing companies in general and brewing companies in particular take the issue of

capacity planning and performance as very important. This is because without capacity planning,

they will not be able to determine the production capacity of their facilities in terms of the inputs,

throughput or produces and output and performance in terms of the extent to which they achieve

or achieving their organizational objectives.

It was found that out of the 740 respondents, for the sex of the respondents, 518 of them are

males while 222 of them are females, giving a ratio of 2.3:1. In the United States of America

there are equal opportunity laws that give women equal opportunity with men in recruitment

matters (Schuler and Youngblood, 2006). Recruitment is generally seen as the process of

searching for and obtaining qualified job candidates in sufficient numbers such that the

organization can select the appropriate people to fill its job needs. In Nigeria, there are now some

nongovernmental organizations and Women Associations that clamours for equal opportunities

in work organizations between women and men.

Schuler and Youngblood (2006) have observed in a study of the utilization goals and time tables

of some organizations, the ratio of men to women were as low as 193:7 in a sample of 100

respondents and percentages of 96.5:3.5. A utilization analysis determines the numbers of

different categories of people like men and women or white, Hispania, black, Asian, Indian and

Minorities in the organization. Tcheknavorian-Asenbaur (2004) has observed that despite the fact

that women are employed in low-skilled poorly paid positions, there is now an advance of an

increasing number of highly educated women who enter into senior decision positions.

For the marital statuses of the 740 respondents, it was found that 562 of them were married while

178 of them were single giving a ratio of 3:1 Marriage is seen from the perspective of maturity

200

and this is why some positions are reserved for married candidates hoping that married men and

women are more responsible than single men and women. Moreover, marital status is an

important demographic variable as it is only when people are married that they can freely give

birth without any social stigma. No wonder demographic trends are part of the socio-cultural

aspect of the social environment (Wheelen and Hunger, 2008). The marital bulge is at the point

where people are married and not when they are single.

Eight current socio-cultural trends in the United States of America are:

1. Increasing environmental awareness.

2. Growing health consciousness especially in the area of gynaecology for married women.

3. Expanding seniors’ market.

4. Impact of the Generation of Boomlet.

5. Declining mass market.

6. Changing pace and location of life.

7. Changing household composition. Single person households, especially those of single

women with children, could soon become the most common household type in the United

States of America. Married couple households decreased from nearly 80% in the 1950s to

50.7% of all households in 2002. A typical family household is no longer the same as it

was once portrayed in the Brady Bunch in the 1970s or even the Cosby show in the

1980’s (Wheelen and Hunger, 2008).

For the ages of the 740 respondents, it was found that they were less than 20 years, 21 – 30

years, 31 – 40 years, 41 – 50 years, 51 – 60 years and above 60 years with frequencies of 15,

155, 192, 200, 170 and 8 out of 740 respectively. This shows that the baby boomers with an age

range of 41 – 59 in the United States of America corresponds with those with the ages with the

highest frequency in this work. Wheeler and Hunger (2008) have observed that the group of 7

million people in their 405 and 505 is the largest age grade in all developing countries.

Although the median age in the United States of America will rise from 35 in 2000 to 40 in 2050

it will increase from 40 to 47 during the same time period in Germany. It will increase up to 50

in Italy as soon as 2025 (Wheeler and Hunger, 2008).

201

It was found that the highest educational qualifications of the 740 respondents were senior school

certificate, Royal Society of Arts, Diploma, Ordinary National Diploma, Higher National

Diploma, First Degree, Second Degree, Ph.D and Associate of Chartered Accountants at the

ratio of 104:44:30:141:129:211:49:1:30. The modal highest educational qualification is First

Degree with a frequency of 211 out of 740. Education is a process of teaching, training and

learning especially in schools, colleges, universities and tertiary institutions to improve

knowledge and develop skills further (Hornby, 2001).

Brewing companies in South Eastern Nigeria have become learning organizations, by the

technical nature of brewing. In the brewing department all the brewing staff are graduates that

studied such science subjects as brewing technology, botany, zoology, agriculture, etc. A

learning organization is one in which there is creation, acquisition and transfer of knowledge

which take place to seek relatively permanent change in behaviour (Sheikh, 2006) Human

behaviour is responsive to learning experiences. All individual activities in the organization such

as engendering loyalties, developing the awareness for organizational goals, performing on the

job, getting safety rewards and brewing are learnt.

The finding that out of the 740 respondents, 252 of them were senior staff while 408 of them

were junior staff, shows that status of the respondents is pyramidal with the tapering at the senior

staff and the junior staff at the base, no wonder Chiehezie, Nzewi and Ozogbu (2008) have

observed that the tapering starts from top managers to middle managers, first line managers and

non-managerial staff. The managers whether they are top managers, middle managers and first-

line managers are senior staff. The non-managerial staff are junior staff.

Managers at this level are the senior executive members of the organization that are responsible

for the overall management of the organization. These people occupy the topmost position of the

pyramid. They are the individuals who are responsible for making organization-wide decisions

and establishing the plans and goals that affect the entire organization. The top managers are the

strategic managers who are responsible for policy formulation implemented by people below

(Chiekezie et al, 2008).

The middle level managers are the managers that occupy the middle position of the

organizational hierarchy. They are responsible for implementation or executing the plans,

202

policies and programmes as directed by the top managers. The front line managers are the

operational managers. They occupy the bottom position in the organizational hierarchy. They are

responsible for implementing operations in support of the organizational strategies (Chiekezie et

al, 2008).

The durations of the work done by the 740 respondents in years were 0-10 years, 11 – 20 years,

21-30 years and above 30 years. They had the frequencies of 30, 342, 360 and 8 out of 740

respectively. If it is assumed that the workers are recruited when they are 20 years old then 21 –

30 years will correspond to the ages of 41-50 years with all that had been earlier written about

the baby boomers being applicable. This is because this duration class has the modal frequency.

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APPENDIX VII

Multi-Period Capacity Planning Problem

In the previous section, we have studied the single period capacity planning problem. We now

discuss how to extend the single period model to a multi-period setting. In practice, a contract

will have duration. In the existing literature that studies capacity contracts, there are two different

ways to model the duration of a contract. If the contracts require a long term commitment, after

the firm signs the contract to acquire capacity from its supplier, the firms reserve or buy the same

amount of capacity in each period until the end of the planning horizon. On the other hand, if the

contracts are short term, the firm can reserve different amounts of capacity for different periods.

For example, Huang, and Ahmed (2006), Barahona et al (2005), and Martinez-de-Albniz and

Simchi-Levi (2005) consider long term contracts while Yazlali and Erhun (2006) use one-period

short term contract.

In the context of the design of a new supply chain, the firm does not own the capacity itself but

reserves capacity from its suppliers. The contract does not need to be for either the short term

such as one period or the long term such as to the end of the planning horizon. The firm and its

suppliers can reach agreement on a duration that is beneficial to both parties. For instance, a

supplier might want to offer a contract with median duration and better price to encourage the

firm to commit. For the firm, signing a long term contract might be too risky; on the other hand,

short term contracts might be too expensive. In this sector, we will study how the firm should

plan its capacity when it has the flexibility to choose the durations of the contracts (Huang, and

Ahmed, 2006).

In the single period problem, we can specify each contract with three terms: per-period unit price

of the fixed-price capacity, per-period unit price to reserve the option capacity, and per-period

unit exercise price of the option capacity. In a multi-period setting, we will add another

specification, which is the contract duration. For example a supplier quotes a three-month

contract with fixed-price N7500, option reservation price N7500, and option exercise price

N15000 to the manufacturer. The manufacturer decides to reserve 100 units of fixed-price

capacity and 20 units of option capacity under this contract. It must pay the price of 100 units

fixed-price capacity (N7500 x 100 = N750,000) and 20 units option capacity (N7500 x 20 =

N15000) in each of the three consecutive months starting with the first month of the contract.

The manufacturer then has 100 units of fixed-price capacity and 20 units of option capacity for

each of the three consecutive months.

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The prices of the contract can depend on the duration. To encourage a longer commitment, the

prices might decrease as the duration of the contract increases. In these situations, the multi-

period capacity planning problem involves another type of tradeoff between the flexibility (or

duration) of the contract and its price. Contracts with shorter duration have more flexibility while

contracts with longer duration offer lower prices.

Let T be the length of the planning horizon. Resource k offers contracts with duration in the set

{ }LL ,,,1, , ikkk TTT = . To simplify the notation, we assume that for any resource all contracts

have different durations. This assumption can be relaxed and all the results still follow. Without

loss of generality, we assume that jkik TT ,, < if ji > . Therefore, we specify the set of contracts

that resource k offers as ( ) ( ) ( ){ }kikikkikkikk TTTeTqTp ∈,,,, ,,

Given the contracts that each resource offers, we assume that the firm will choose for each

resource a sequence of contracts { }LL ,,,1, , ikkk TTT = that satisfies the following conditions:

1. Contract ikT , has duration ikl , and it covers from period ∑−

=+1

1 , 1i

j jkl to period ∑ =

i

j jkl1 ,

2. Tt iki =∑ , for all k .

The first condition says a contract starts after the previous contract finishes. Condition 2

specifies that the manufacturer does not reserve capacity beyond the planning horizon. We call a

sequence feasible if it satisfies these two conditions. One implicit assumption here is that for

each period, we have only one contract active for each resource. In addition to deciding the

sequence of the contracts for each resource, the manufacturer needs to decide the corresponding

sizes: { }LL ,,,1, , ikk cc and { }LL ,,,1, , ikk gg we note that we permit zero capacity contracts at

zero cost, which allows the firm to not use a resource for any subset of periods. Since the first

two periods are cover by the same contract, the fixed-price and total capacity reserved for each

of these two periods are the same, which are 2c and 2g similarly a contract with duration 1

period is used to covered period 3 and a contract with duration 3 periods is used to cover the rest

of the horizon.

To simplify the notation and the representation of the multi-period capacity planning problem,

we will write a feasible sequence of contracts for resource k as follows:

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{ },, ,,,1, LL ikkk TTT = where ikT , has duration ,,ikt and Tt iki =∑ ,

{ }Tkk gg ,,1, ,L and jkik gg ,, = if ∃a such that [ ]∑ ∑−

= =+Ξ 1

1 1 ,, ,1,,a

t

a

l lkik ttji

{ }Tkk cc ,,1, ,L and jkik cc ,, = if ∃a such that [ ]∑ ∑−

= =+Ξ 1

1 1 ,, ,1,,a

t

a

l lkik ttji

We use superscript to indicate time period. Given that the firm has decided its capacity planning

strategy, the sequence and sizes of the contracts for each resource, and given a multi-period

demand realization vector d, we can write the multi-period production planning problem as:

( ) ( ) iT

i

iiT

i

HyezrzyxdgcTzyx ∑∑

==

′−′=11

,,,,,,ˆ,,

maxπ (1)

idzts ii ∀≤ ,..

( ) idyxBAz iiii ∀+≤ ,,

icHx ii ∀≤ ,

( ) igyxH iii ∀≤+ ,

izyx iii ∀≥ ,0,,

Similar to the single period case, in a multi-period setting, the firm’s ultimate purpose is to

choose the strategy to maximize its expected profit with expectation taken over the distribution

of the multi-period demand random vector:

( ) ( )[ ] ( ) ( ) ( )iiT

i

iT

i

i cgqpzyxDgcTEDgcTgcT

−′−′

−= ∑∑== 11

*** ,,,,,,ˆ,,,ˆ,,

maxππ

igcts ii ∀≤ ,.. (2)

kT are feasible for all k.

We assume that unfilled demands are lost and unused capacity cannot be saved for future usage.

We also assume that the manufacturer will not use any unused capacity to build and store

inventory. Even though we do not allow inventory, the multi-period capacity planning problem is

not separable since the firm can use a contract to cover multiple periods.

We assume that the manufacturer needs to decide the sequence and sizes of the contracts for each

resource at the beginning of the planning horizon. To this extent, we also assume that it has a

demand forecast for each period at the beginning of the first period. In practice, capacity

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decisions usually need to be made with a much longer lead time than the planning horizon. In

these situations, our two-stage decision process matches with the reality. Moreover, as we have

discussed in the earlier, since the manufacturer does not own the capacity, it is important for it to

secure the price and supply of the capacity by signing contracts at an early stage. However, this

is a restrictive assumption and it would be interesting to study the capacity planning problem in a

dynamic setting.

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