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EFFECT OF LEAN MANUFACTURING PRACTICES ON

OPERATIONAL PERFORMANCE OF MANUFACTURING

FIRMS IN MOMBASA COUNTY, KENYA

BY:

FATMA KASYOKA SAID

REG. NO: D61/77096/2012

SUPERVISOR: MR.STEPHEN ODOCK

A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENT FOR THE AWARD OF MASTERS OF BUSINESS

ADMINISTRATION DEGREE, SCHOOL OF BUSINESS,

UNIVERSITY OF NAIROBI

MAY, 2014

DECLARATION

I declare that this research project is my original work and has never been submitted to

any other University for assessment or award of a degree.

Signature…………………………….. Date………………………………

FATMA KASYOKA SAID

D61/77096/2012

This project has been submitted with our authority as the university supervisor.

Signature……………………………………. Date ………………………………………

MR. STEPHEN ODOCK

Lecturer,

Department of Management Science, School of Business

University of Nairobi

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Acknowledgement

Firstly, I would like to express my gratefulness to my supervisor, Mr.stephen Odock.

Thank you for the support and guidance.

My gratitude also goes to various scholars whose works in the areas of lean

Manufacturing, lean in supply chain, and supply chain management, enabled to

understand the context of this study and also in the writing of this project.

To my dear husband, Mr. Ali Hassan and children, I will not forget to appreciate your

priceless support during the my study period. Thank you for the love and

understanding you have shown to me despite my working for long hours.

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DEDICATION

This project is dedicated to my family.

My husband Mr. Ali Hassan ,my children Fahima Ali, Maryam Ali, Farsan Ali, Said

Ali,and Talaldost Ali for their undying love for me.

And to my parents Mr. Said Shaban and Mrs Maryam Shaban, thank you so much for

your endless encouragement and support and for believing in me.

God bless you so much.

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ABSTRACT

Companies operating in the presents' rapidly changing and highly competitive market

have been pressured to improve all aspects of operational performance; quality,

flexibility and customer response time. They have retorted by adopting a set of

practices that is fast becoming the dominant paradigm in manufacturing - Lean

Manufacturing. Lean Manufacturing involves continuous elimination of waste from

the value chain of manufacturers thus enhancing customer value through continuous

improvement of operational performance of the manufacturers. The research surveyed

the effect of lean practices on the operational performance of manufacturing firms in

Mombasa county. The study aimed to achieve three objectives: To determine the

extent to which manufacturing firms in Mombasa County have adopted lean

manufacturing practices; To establish the effect of lean manufacturing practices on

operational performance of manufacturing firms in Mombasa County and the

challenges faced by firms adopting lean manufacturing. The data analyzed was

gathered using a semi-structured questionnaire targeting operations managers of

manufacturing firms in Mombasa. The results were presented using tables,

percentages, mean scores, frequencies and charts for easy understanding. The findings

indicated that most manufacturing firms in Mombasa practiced Lean manufacturing.

It was also clear that Lean Manufacturing firms had seen improvement in their

operational performance. The results however show that JIT as Lean practice showed

a negative relationship with Operational Performance. The study found that the most

experienced challenge was high costs of implementation. The study recommends

more awareness of the importance of lean practices in Manufacturing firms and

support from the top management as critical tool for takeoff. It also recommends that

for Lean manufacturing to be successful it has be practiced in the entire Supply Chain.

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TABLE OF CONTENTS

LIST OF ABBREVIATIONS......................................................................................i

CHAPTER ONE: INTRODUCTION........................................................................1

1.1 Background of the study........................................................................................1

1.1.1 Lean Manufacturing......................................................................................1

1.1.2 Operational performance...............................................................................1

1.1.3 Lean Manufacturing and Operational Performance......................................1

1.2 Research Problem...................................................................................................1

1.3 Research Objectives...............................................................................................1

1.4 Value of the study..................................................................................................1

CHAPTER TWO: LITERATURE REVIEW...........................................................1

2.1 Introduction............................................................................................................1

2.2. Theoretical foundation of the study......................................................................1

2.2.1 Theory of Constraints (TOC)........................................................................1

2.2.2 Resource Based Theory (RBV).....................................................................1

2.3 Lean Manufacturing Practices................................................................................1

2.3.1 Just -In Time (JIT)........................................................................................1

2.3.2 Total Productive Maintenance (TPM)...........................................................1

2.3.3 Continuous Improvement/ Kaizen................................................................1

2.3.4 Automation/Jidoka........................................................................................1

2.3.5 Value Stream Mapping..................................................................................1

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2.4 Challenges of adopting Lean Manufacturing.........................................................1

2.7 Empirical Literature Review..................................................................................1

CHAPTER THREE: RESEARCH METHODOLOGY...........................................1

3.1 Introduction............................................................................................................1

3.2 Research Design.....................................................................................................1

3.3 Population of Study................................................................................................1

3.4. Sample and Sampling technique............................................................................1

3.5 Data Collection........................................................................................................1

3.6 Data Analysis..........................................................................................................1

REFERENCES.............................................................................................................1

APPENDIX 1................................................................................................................1

A1.Questionnaire..........................................................................................................1

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LIST OF ABBREVIATIONS

CI - Continuous Improvement

HRM - Human Resource Management

JIT - Just -in -Time

KAM – Kenya Association of Manufacturers

LM - Lean Manufacturing

NSE- Nairobi Stock Exchange

NVA - Non Value Adding Activities

RBV – Resource Based View

SMED - Single Minute Exchange of Die

TOC – Theory of Constraints

TPM - Total Productive Maintenance

TPS -Toyota Production System

TQM - Total Quality Management

VA - Value Adding

VSA - Value Stream Analysis

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VSM –Value Stream Mapping

ISO- International Standards

NEMA-National Environment Management Authority

OEE- Overall Equipment Effectiveness

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CHAPTER ONE: INTRODUCTION

1.1 Background of the study

Today, firms are operating in a fast changing and highly competitive globalized

market, thus pressuring them to improve quality, flexibility, and customer response

time (Womack & Jones, 2003). To achieve these improvements, researchers have

increasingly proposed the implementation of lean in the manufacturing process as a

way to achieve the required competitive advantage (Womack & Jones, 2003; Taylor,

2006; Cudney & Elrod, 2011). The focus of LM is on specifying value from the

perspective of the customer and removing waste from the value stream of a product

(Womack & Jones, 2003). LM results to increased productivity, improved product

quality, reduced inventory, reduced lead time and elimination manufacturing waste

(Shah & ward, 2003).

The ideologies of Lean Manufacturing, Resource based view (RBV) and Theory of

constraints theories (TOC), compliment each other's goals and objectives, since their

foundation is based on using less resources to produce maximum output. According to

RBV the resources that a firm controls are the determinants of a firm's performance

(Barney, 2001) and thus they should be utilized sparingly and economically in order

to achieve maximum output. Equally, lean manufacturing involves creating more

value for customers through waste minimization (using resources cautiously).

Goldratt (1988), asserts that TOC views a manufacturing firm as system and there's

always something that limits its performance. LM offers a profound toolkit which can

be applied to constrained processes in order to significantly improve the operational

performance whereas TOC focuses on eliminating the constraint in the process for

continuous improvement.

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Mombasa County is the second biggest city in Kenya and forms a big part of the

Kenyan manufacturing sector. Kenya has a large manufacturing sector serving both

the local and the world market and is dominated by subsidiaries of multinationals. The

performance of the sector has been affected by low capital injection, use of obsolete

technologies, scarce resources, overproduction, and lack of knowledge on how to

become lean resulting to enormous wastes impeding their growth (Kimani, 2013).

This study will aim to identify the lean manufacturing practices can improve

operational performance of manufacturing firms in Mombasa County, Kenya and the

success factors in their implementation.

1.1.1 Lean Manufacturing

Lean Manufacturing is a systematic approach that improves value to the customer by

identifying and eliminating waste through continuous improvement by flowing the

product at the pull of the customer in pursuit of perfection (Manrodt, Vitasek &

Tompson, 2008). Shah & Ward, (2003), describe lean manufacturing as a business

system for managing product development, operations, suppliers, and customer

relations that requires less human effort, space, capital, and time to make products

with fewer defects to precise customer desires. Womack and Jones (1996) define

Lean Manufacturing as a business and production philosophy that shortens the time

between order placement and product delivery by eliminating waste from a product’s

value-stream. Lean Manufacturing is simply the systematic removal of waste by all

members of the organization from all areas of the value stream (Worley, 2004).

It is commonly believed that Lean started in Japan (Toyota, specifically) but Henry

Ford had been also using similar concept about Lean as early as the 1920’s when he

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confirmed they were able to keep the price of Ford products low by constantly

minimizing the production process (Kilpatrick, 2003). The LM concept was

conceived by Toyota Motor Company, Japan and was known as Toyota Production

System (Shah & Ward, 2007) . Toyota Production System (TPS) was operationalized

by Taiichi Ohno, who was trying to ensure survival of the Toyota Motor Company

after the post world war II economic depression (Womack & Jones, 1996).

The main focus of lean manufacturing practices is on value, more than on cost, and

seeks to remove all non-value adding components especially processes whilst

improving those that add value. This approach involves an extremely rigorous,

questioning analysis of every detail of product development and production, seeking

to continuously establish the ultimate source of problems. Only by eliminating the

cause at source can the possibility of that fault recurring be removed (Womack &

Jones, 2003). The concept of Lean Manufacturing is considered to improve a firms'

performance through elimination of waste (Shah & Ward, 2007). There are eight

wastes highlighted in TPS which are overproduction, waiting, conveyance, over

processing, excess inventory, excess movement, defects and unused employee

creativity, and the biggest one being overproduction (Wee &Wu, 2009).

Lean is a comprehensive set of practices that when combined and developed, make a

company more flexible and more responsive to customer demand (Shah &Ward,

2003). The major benefits of LM are increased productivity, improved product quality

and manufacturing cycle time, reduced inventory, reduced lead time and elimination

of manufacturing waste (Agus & Hajinoor, 2012). To achieve those benefits, the LM

philosophy uses several concepts such as Continuous improvement(CI), Total

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Productive Maintenance (TPM), Jidoka, Just-in-Time (JIT), and Value Stream

Mappings (VSM), (Belekoukias, Garza-Reyes & Kumar, 2014).

1.1.2 Operational performance

In today's dynamic and rapidly changing workplace and globalised economy, the

ability to achieve and maintain high performance and productivity in organizations is

a key challenge facing manufacturing firms today (Womack & Jones, 2003). To be

able survive in those harsh conditions is the objective of any organization, because it’s

only through continuous operations and improved performance that organizations are

able to grow and progress.

Operational performance refers to the measurable aspects of the outcomes of a firms

processes, such as reliability, production cycle time, and inventory turns which in turn

affects business performance measures such as market share and customer

satisfaction(Voss, Ahlstrom, & Blackmon, 1997). Operational performance is

simply the effectiveness and efficiency of an organization in transforming inputs

into outputs.

Knowing the determinants of operational performance is vital important especially in

the context of the current economic crises because it enables the identification of

those factors that determine if there's indeed any progress or improvement in the

operations of a manufacturing firm. This information will help managers to find and

diagnose the problems in their processes and irregular performance or unsatisfactory

progress can be revealed clearly (Anvari, Zulkifli & Yusuff, 2013).This study will

consider the most important measures of operational performance; cost, speed,

dependability, quality and flexibility (Belekoukias, Garza-Reyes & Kumar, 2014).

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1.1.3 Lean Manufacturing and Operational Performance

Wheatley (2005) discussed the following to be reasons why organizations are

adopting LM; continued pressure to improve operational performance, pressure to

maintain competitive advantage in price and service, pressure to improve profit,

customers demanding shorter order-cycle times and reduced prices. All the reasons

are relative to exemplary operational performance. Operational performance metrics

determine if LM will most definitely improve performance of manufacturing firms.

Several studies support that LM has evolved to become a leading approach in

improving operations performance specifically inventory turnover, quality, lead time,

labor productivity, space utilization, flexibility (Shah &Ward, 2003; Fullerton &

Wempe,2009; Agus & Hajinoor, 2012). Shah and Ward (2003) examined the effect

on operational performance of the lean practices and contextual factors and concluded

that LM practices are positively related to operational performance. Lean

manufacturing is designed to eliminate waste and improve operations in every area

extending from production to customer relations, product design, supplier networks

and factory management (Agus & Hajnoor, 2012).

Lean manufacturing practices offers a manufacturing firm the platform to achieve

superior operational performance and a distinctive competitive edge (Womack &

Jones, 2003). LM practices bear a direct relationship to improvements in operational

performance (Agus & Hajinoor, 2012). LM philosophy if carefully adopted and

implemented can definitely form the roadmap to global manufacturing excellence

(Papadopoulou & Ozbayrak, 2005).

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1.1.4 Manufacturing industry in Mombasa

Manufacturing is the process of transforming of raw materials into either intermediate

goods or final products to meet customer demand. Kenya has a large manufacturing

sector serving both the local market and the rest of the world. According to KAM

(2014), there are about three thousand and seven hundred (3700) established multi-

sector manufacturing firms in Kenya. Manufacturing activities are concentrated

around the three largest urban centers; Nairobi, Mombasa, and Kisumu cities.

Mombasa is the second-largest city in Kenya, and is home several giant

manufacturing firms which include, the Kenya Oil Refinery, Bamburi cement, several

steel manufacturers, and bottling companies. Other manufacturing firms within the

county include the Export processing zones and small and medium scale

manufacturing firms such as bakeries and plastic manufacturers. The study will focus

on Mombasa County due to its familiarity and easy accessibility to the researcher to

save time and cost.

The Manufacturing sector is currently facing a lot of challenges as a result of

increasing cost of doing business culminating from increased costs of transportation,

poor infrastructure and rains, increased costs of labor as a factor of production,

increased energy charges and increasing power costs, power outages and fluctuations,

higher lending rates and fluctuating exchange rates in addition to that congestion,

delays and redundant checks of merchandise plus hefty regulations by regulatory

bodies. These challenges have hindered the growth of the sector since they have

negatively affected the supply of inputs, rising time and cost of transfer of raw

materials to producers (KAM, 2014). This scenario has made manufacturers to realize

that their value chain needs improvement to able survive in the harsh settings.

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Management of the value chain using LM practices is one way to cope, because it

results to improved operational performance, growth in market share, suppliers and

distribution channels and provide the platform for continuous improvement

(Njunguna, 2013).

1.2 Research Problem

Manufacturers are nowadays facing intense global competition, rapid technological

changes and fast changing customer needs. LM is the new paradigm, since both the

developed and developing countries are practicing it in order to improve operational

performance (Ferdousi & Ahmed, 2009). The concept of LM evolved from the

Toyota Production System (TPS) and is considered to continuously improve a firms

performance through elimination of waste (Mannan & Ferdousi, 2007). Lean

manufacturing is a system for improving a firms value creation and customer relations

using requires less human effort, space, capital, and time in the precise customer

requirements (Shah & Ward,2003). The main focus of lean manufacturing is on value,

more than on cost, and seeks to remove all non-value adding components especially

processes whilst improving those that add value (Womack, et al.2003).

Under the economic pillar of the Kenya vision 2030, manufacturing sector is

expected to deliver the envisaged 10 per cent economic growth rate per annum, by

increasing and sustaining its contribution to Gross Domestic Product by at least 10

percent per annum. Mombasa being the second largest city, has a large contribution

towards the projected objectives. One possibility for manufacturing firms to achieve

the projected results is to turn to LM .Manufacturing companies that do not keep up

with the lean manufacturing paradigm will eventually lose out to competitors. (Agus

& Iteng 2013).

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In theory, many academicians have asserted that lean manufacturing can result in

positive operational performance outcomes (Lewis, 2000; Cua, Mckone & Schroeder,

2001; Shah &Ward, 2003 and Agus & Hajinoor, 2012). The researchers claim that the

implementation of lean manufacturing had resulted in better operational performance

such as increasing production volume, reducing lead time, enhancing customer

satisfaction and flexible production method. Consistent with this, Shah and Ward

(2003) in their study, emphasize that the implementation of lean manufacturing had a

significant and positive relationship with operational performance. Agus and

Hajinoor(2012) in their study on Malaysian manufacturing firms have addressed the

key relationships between lean production, product quality performance and business

performance. Their results support the conceptual model, demonstrating strong

association between lean manufacturing, product quality performance, and business

performance.

In Kenya, studies done were consistent with other researchers that lean manufacturing

will ultimately lead to improved in operational performance of manufacturing firms

(Kisombe, 2012; Kimani, 2013; Rono, 2013 and Rubeya, 2013).To validate those

findings Ondiek(2012) and Kisombe (2012) found out that lean manufacturing is a

continuous process whose objective to produce high quality products at the pace of

customer with little or no waste. Rono, 2013 did a case study on Bamburi cement and

concurred with Tourki (2010) that many organizations have realized that adopting

Lean manufacturing will enable them survive in the global market. Rono also admits

that the benefits of lean manufacturing are consistent with the research done by other

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scholars that lean manufacturing not only reduces operational costs but also targets to

drastically elevate the competitiveness of a company (Mehta, Mehta & Mehta ,2012).

However, Lewis (2000) warns that LM may not directly improve operational

performance as it may be intervened by other variables. Moori, Pescarmona and

Kimura, (2013), argues that, some studies found that the adoption of JIT tools does

not improve profitability. The empirical results regarding improvement in operational

performance of LM companies are vague and paint an ambiguous picture (Agus &

Hajinoor, 2012). To add on that, most studies in Kenya had not examined the

relationship of LM and operational performance quantitatively as it was the case in

this study. A related study done by Kanyanya, 2013 on Lean manufacturing practices

and performance of organizations listed at the Nairobi Stock Exchange (NSE). This study

specifically focused on operational performance and targeted manufacturing firms in

Mombasa county. This study aimed to uncover: What is the effect of adopting LM

practices on the operational performance of manufacturing firms in Mombasa

County?

1.3 Research Objectives

The study seeks to achieve the following research objectives:

i. To determine the extent to which manufacturing firms in Mombasa County

have adopted lean manufacturing practices.

ii. To establish the effect of lean manufacturing practices on operational

performance of manufacturing firms in Mombasa County.

iii. To find out the challenges faced by manufacturing firms in adopting lean

manufacturing practices.

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1.4 Value of the study

The study will be of significance to other researchers since information from the

literature review is limited and thus provide an elementary reference for future

studies.

The study will serve as a point of reference for firms aiming to adopt the lean

practices to improve their performance and inform them of the challenges they are

likely to face.

The findings will help in the formulation of polices which will help in the regulation

of the sector. As well as act as a guide in the ISO policy formulation and NEMA

regulations to help protect the scarce resources and environment at large.

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CHAPTER TWO: LITERATURE REVIEW

2.1 Introduction

This chapter focuses on a review of literature on lean manufacturing. It outlines the

theoretical literature, Lean practices and challenges faced in implementation of LM.

Finally it gives the overview of the empirical studies on lean practices.

2.2. Theoretical foundation of the study

In this study the concept lean manufacturing will conceptualized using two related

theories. These are The Theory of Constraints and the Resource Based View theory.

2.2.1 Theory of Constraints (TOC)

The Theory of Constraints (TOC) is a business philosophy that was developed by Dr.

Eliyahu M. Goldratt, and usually applied to running and improving an organization. The

Theory of Constraints is a methodology for identifying the most important limiting

factor (the constraint) that stands in the way of achieving a goal and then

systematically improving that constraint until it is no longer the limiting factor (Lean

Enterprise,2009). The Theory of Constraints takes a scientific approach to

improvement. It assumes that every complex system, for example the manufacturing

process, consists of multiple linked activities, and consequently one of them will

become a constraint upon the entire system (the constraint activity is the “weakest

link in the chain”). According Mabin and Balderstone (1999), if TOC is applied

logically and methodically it will answer these three questions essential to any process of

ongoing improvement: “What to change?”, “To what to change?”, “How to implement

the change?”

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TOC offers a practical structure for directing LM efforts where they will do the most

good and avoiding the pitfalls of applying them where they will add no value (Leach,

2000). One of the appealing characteristics of the Theory of Constraints is that it

inherently prioritizes improvement activities same as lean does through continuous

improvement. The top priority in TOC is always on the current constraint and in lean

is waste elimination. A successful Theory of Constraints implementation will have the

following benefits as in lean manufacturing: Increased profits, quick improvement in

production, improved capacity, reduced lead time, reduced inventory (Lewis 2000).

2.2.2 Resource Based Theory (RBV)

A resource-based view is the ability of a firm to deliver sustainable competitive

advantage when resources are managed such that their outcomes cannot be imitated

by competitors (Hooley & Greenley, 2005). The RBV theory as a basis for

competitive advantage of a firm lies primarily in the application of a bundle of

valuable tangible or intangible resources at the firm's disposal. RBV says that a firm’s

sustainable competitive advantage is reached by virtue of unique resources being rare,

valuable, inimitable, non-tradable, and non-substitutable, as well as firm-specific

(Barney, 2001).

To transform a short-run competitive advantage into a sustained competitive

advantage requires that these resources are heterogeneous in nature and not perfectly

mobile (Barney, 1991). If these conditions hold, the bundle of resources can sustain

the firm's above average returns. RBV supports efficient use of resources in an

unmatched way and thus compliments the core of LM which is to minimization of all

forms of waste and continuously imporove procesess (Keyser & Sawhney, 2013).

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2.3 Lean Manufacturing Practices

Lean manufacturing is implemented through some practices which are undertaken to

bring about improvements in organization. This study will focus on five of the most

essential practices of LM as advocated by Belekoukias, Garza-Reyes, &Kumar,

(2014) in their study, Just-in-Time (JIT), Total Productive Maintenance (TPM),

Automation, Value Stream Mapping (VSM) and Continuous Improvement (CI).

2.3.1 Just-in Time (JIT)

JIT is a technique in a flow process where the needed parts, components or materials

are delivered to the point of need only at the time of need and the amount needed

(Ohno, 1988). JIT states that an organization should produce the right item at the right

time (Womack & Jones 2003); this helps in reducing inventories and possible wastes.

The tools of JIT are; one piece flow, pull system, takt time, cell manufacturing,

kanban, multifunctional employees (Rocha-Lona, Garza-Reyes, and Kumar 2013).

JIT is a tool if well implemented, improves performance through reduction of costs,

better quality products and increased production (Rono, 2013). JIT is based on pull

production, top management and employee involvement, elimination of wastes, good

supplier relations, and total quality control (Pheng & Chuan, 2001).

2.3.2 Total Productive Maintenance (TPM)

TPM is a holistic approach to maintenance that focuses on proactive and preventive

repairs to maximize operational time of machines and equipment. It capitalizes on

progressive maintenance methodologies and calls upon the knowledge and

cooperation of operators, equipment vendors, engineering, and support personnel to

ensure production flows smoothly (Shah & ward, 2007). TPM relies on tools such as

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Overall equipment effectiveness (OEE), single minute exchange of die (SMED),

autonomous repairs and quality repairs (Belekoukias, Garza-Reyes, & Kumar, 2014).

Results of machine optimized performance include; elimination of breakdowns,

reduction of unscheduled and scheduled downtime, improved utilization, higher

throughput, and better product quality (Shah & Ward, 2003). This results to lower

operating costs, longer equipment life, and lower overall maintenance costs.

2.3.3 Continuous Improvement/ Kaizen

Continuous improvement is the technique of endless creation of value and removal of

waste from a value chain. This entails the continual pursuit of improvements in

quality, cost, delivery and design (Bhasin & Burcher, 2006). Once implanted as part

of an organization's culture, kaizen acts as a platform for the sustainment of lean

manufacturing. Rocha-Lona, Garza-Reyes and Kumar (2013), suggest brainstorming,

continuous flow, data check sheet, run charts, Pareto chart, VSM, mistake proofing

and process maps as tools that most commonly used to achieve the kaizen strategy.

The preliminary radical improvement in a process does not stop at the initial

achievement; instead, it is followed by continuous incremental improvements in order

to pursue perfection (Womack & Jones, 1996). The authors argue that the concept of

perfection in lean manufacturing refers to endless improvement. It focuses on

elimination of non value added activities in the process.

2.3.4 Automation/Jidoka

Automation, also known as Jidoka, is a lean method that targets the reduction of

quality defects with the use of tools that include mistake proofing devices (Poka-

yokes),visual control systems (Andons) and a full working system (Belekoukias,

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Garza-Reyes, & Kumar, 2014). Jidoka means providing machines and operators the

ability to detect an abnormal condition and instantly take action based on the detected

condition. The equipment becomes capable of discriminating against unacceptable

quality, making the process more reliable (Khalil, Khan, & Mahmood, 2006).

Jidoka is the process that focuses on quality control and the automation of the functions

of the production supervision, which means that the personnel is warned in case of an

abnormal situation in order to stop the production line. This helps prevents wastage and

defects in the output. It aids by focusing on understanding why the problems occurred and

how they can be avoided in future (Kanyanya, 2013).

2.3.5 Value Stream Mapping

Value stream Mapping(VSM) is a process of understanding what actually happens

along the products´ value chain by visually mapping the flow of information and

material, (Rao, Subbaiah, Rao & Rao, 2011). VSM is often considered as the basis for

the implementation of other lean techniques, and it helps to track activities for value

creation, starting from conception till delivery to the end customer (Cudney & Elrod,

2011). Taylor (2006) describes VSM as a very important first step that helps to

achieve the desired alignment in value chain activities in order to improve it.

The steps in VSM include: education on the importance of VSA, creation of value

chain structure and selection of focus value stream, mapping of individual facilities

and activities within the focus value stream, development of current state map for the

value chain, identification of issues and opportunities for improvement within the

chain, development of future state map for the value chain and recommendation for

improvements (Taylor, 2006).

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2.4 Challenges of adopting Lean Manufacturing

Despite the merits of adopting LM, implementation challenges are surmountable.

Resistance to change by people is one of the major challenge in adopting lean (Wong,

Wong, & Ali, 2009) and evidence on its likely benefit to end users is hard present

(Rono, 2013). LM radically impacts every person in every function of an

organization, and literally changes the organizational culture. Another challenge to a

lean organization is to create a culture that will generate and sustain long-term

dedication from top management to the entire workforce (Prakash &Kumar, 2011).

Under LM employees are most likely to become productive but at the same time may

find their work more stressful (Womack et al., 1996).

Failure to expand LM to the supply chain is another drawback of lean. If critical

suppliers cannot deliver on time, and in smaller quantities, the benefits of Lean will be

greatly diminished or even non-existent. Building and managing a lean supply chain

poses a challenge owing to the highly interconnected nature of the activities in the

supply chain (Agus & Hajinoor, 2012).

According to this authors the size of the firm might be a setback as evidenced in their

study that large US manufacturers adopted JIT practices more frequently than small

manufacturers and that large manufacturer were doing well than the small scale (Shah

& ward, 2003).

A company may implement the building blocks in the wrong sequence. For example,

if batch sizes are reduced prior to reducing changeover time, and changeover times

are lengthy, equipment utilization will drop, and the ability to serve customers will be

16

reduced. According to Bhasin &Burcher (2006) the major difficulties companies

encounter in attempting to apply lean are a lack of direction, a lack of planning and a

lack of adequate project sequencing. These challenges can be dealt with so as to

enable manufacturing firms enjoy the countless benefits of adopting LM.

2.7 Empirical Literature Review

Various studies have concluded that LM has helped numerous companies improve

operational performance (Shah & Ward, 2007; Agus &Hajinoor, 2012; Ghosh, 2012;

Nawanir, Teong & Othman, 2013). Adoption of lean manufacturing practices leads to

a positive and significant impact on both operational and business performance

(Nawanir, Teong & Othman, 2013). Ghosh (2012) found out that the most vital

operational metrics in LM firms improved: high productivity, reduced lead time,

improved output and reduced inventory. Agus and Hajinoor (2012) their study

revealed a strong association between LM, product quality performance and

manufacturing performance. Cua et, al. (2006) found out that JIT, TPM and TQM can

positively and radically affect quality, cost, flexibility and delivery of a firm.

In Kenya, studies concede that LM improves the performance of a manufacturing firm

(Kisombe, 2012; Kanyanya, 2013;Rono, 2013). Kanyanya (2013) confirmed that LM

firms are able to be more responsive to market trends, use materials economically, cut

lead times, enabling them to produce products and services less expensively than their

non-lean counterparts. In a case study on Bamburi Cement Limited in Mombasa,

Rono (2013) found a positive outcome after implementation LM within all systems in

the value chain. To validate those findings, Kisombe (2012) found out that LM was a

continuous process whose objective to produce high quality products at the pace of

customer with little or no waste. Rono, 2013 also acknowledges that LM not only

17

reduces operational costs but also targets to drastically elevate the competitiveness of

a company.

Nonetheless some studies have identified that the adoption of JIT tools or the use of

models based on TQM does not improve profitability (Moori, Pescarmona & Kimura

2013). Lewis (2000), in his case-based research, argues LM methods involve not only

benefits but also costs and that becoming lean does not automatically result in

improved operational performance. In addition, Bhasin and Burcher (2006) insist that

less than ten percent of the companies succeed in implementing lean manufacturing

practices. Moreover findings show that focus over value creating activities towards

the final customer is still missing in most of the companies implementing lean

(Kanyanya, 2013).

SUMMARY OF LITERATURE REVIEW

Theory and concept of lean manufacturing is not yet fully developed, in spite of its

potential for gains in operations (Anand & Kodali, 2008). Just as effect of lean

manufacturing on operational performance is an open question, given the differences

of empirical studies (Mannan & Ferdousi 2007). Few studies on the relationship of

lean manufacturing and operational performance have been done in Kenya. Thus there

was a need to clearly paint the right picture of the relationship between LM and

operational performance. It was necessary to determine if adopting LM will lead to an

improved operational performance. This research was driven by the fact that Kenyan

researchers have noted that there an urgent need for further research in the area of

Lean manufacturing (Ondiek, 2012; Kisombe, 2012; Kimani, 2013; Rono, 2013). This

research will add more knowledge to the concept and bring in the latest fad in lean

manufacturing practice.

18

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 Introduction

This chapter outlined the research methodology to be used, the study design, target

population, data collection instruments procedures and data analysis instruments.

3.2 Research Design

The research design used in this study was Descriptive Cross-sectional survey. A

Descriptive Research design emphasis is on determining the frequency with which

something occurs or the extent to which two variables co-vary. It attempts to describe

and explain conditions of the present by using many subjects and questionnaires to

fully describe a phenomenon. Cross-sectional study is where the sample selected is

representative of the target population and the emphasis is on the generation of

summary statistics such as averages and percentages. This research design was

favorable for this study because it used questionnaires to collect data from a sample of

manufacturing firms in Mombasa county to be able to understand the how LM co-

vary with the operational Performance of the firms.

3.3 Population of Study

The population of study was all manufacturing firms in Mombasa County which were

members of KAM by end of year 2013. Some firms which have headquarters in

Nairobi but have branches in Mombasa County were also be considered. The total

population was one hundred and thirty manufacturing firms representing all sectors of

the industry.

19

3.4. Sample and Sampling technique.

The study took the sample from the KAM directory, 2014. It adopted a stratified

proportionate sampling design. According to Mugenda and Mugenda (2008) a sample

space of a 20%, 30% or 40% of the total population can to be chosen if the population

of study is small. In this study 40% of the target population will be used as the target

population is a small one: the sample will be 49 rounded off to 50 firms. A sample

size of 50 manufacturing firms will be calculated using stratified sampling such that

each subsector is represented the study.

As worked out below:

Sample Size = x/n * Z = y

Where x = population of a particular sub-sector

n = target population

Where: x/n = weight over population

Z = sample space

Y = sample size

Hence, sample size determined in each stratum as shown in table 1 below:

20

Table1: Manufacturing firms and sub-sectors and sample.

No. Sector

Total

Pop. size

Weight Sample

size

1 Building construction and mining 7 7/130*50 3

2 Chemical and allied 10 10/130*50 4

3 Energy, electrical and electronics 6 6/130*50 2

4 Fresh produce 0 0 0

5 Food and beverages 24 24/130*50 9

6 Leather and footwear 0 0 0

7 Metal and allied 16 16/130*50 6

8 Motor vehicle and Accessories 7 7/130*50 3

9 Paper and paperboard 4 4/130*50 2

1 Pharmaceutical & Med equipment 2 2/130*50 1

1 Plastic and Rubber 9 9/130*50 3

1 Services and Consultancy 27 27/130*50 10

1 Textile and Apparels 18 18/130*50 7

1 Timber, wood and furniture 0 0 0

Total 130 50

Source: KAM directory, 2014.

21

3.5 Data Collection

Primary data was collected using a closed ended questionnaire that aided in

specifically addressing the issues were relevant to the study and to obtain unbiased

information that is up to date. Obtaining first-hand data would also improve the

validity of the research. The respondents were operations managers of manufacturing

firms.

Closed ended questionnaires were used in the collection of quantitative data for

analysis using a five point Likert-scale. The questionnaire comprises of three sections,

first section, seeks information on the manufacturing firm, the second section is

divided into two parts, first part will evaluate the extent to which the manufacturers

have adopted of Lean Manufacturing practices, part two will test the effect of lean

manufacturing adoption on operational performance, third section, will establish the

challenges facing manufacturing firms when adopting lean manufacturing. The

questionnaires will be self-administered to the operations managers on a ‘give and

take later' basis, who shall be given a period of three days to fill them.

3.6 Data Analysis and Presentation

The completed questionnaires were first edited carefully to detect errors and

omissions this helped ensure consistency and completeness. Classification of the data

was done to reduce raw data into homogeneous groups. Descriptive statistics was

used to analyze objective one and the findings were presented using tables ,frequency

distribution charts and pie charts. To analyze objective two, regression analysis and

standard deviations was used and their findings were presented in tables and pie

charts. The value of the coefficient of correlation (R) was computed to determine the

magnitude and direction of the relationship.

22

The model will be constructed using the lean practices and operational metrics

discussed in the study as follows:

Y = β0 + β1X1 + β2X2+ β3X3+ β4X4 + β5X5 + ε

Y is the dependent variable representing operational performance; β0 is a constant

factor which is also the value of the dependent variable when X1, X2, X3, X4 & X5 are

equal to zero. X1 is JIT variable, X2 is TPM variable, X3 is CI variable and X4 is

Jidoka variable and X5 is VSM variable. β1, β2, β3, β4and β5 are constants associated

with X1, X2, X3, X4 and X5 respectively. Random error ε represents all other minor

effects on the model which have not been captured. The measures used in this study

were derived from several criteria, which have been conceptualized and used in

previous empirical studies on LM and operational performance (Belekoukias, Garza-

Reyes & Kumar, 2014). Challenges to LM implementation which was objective

three was analysed using mode to determine the frequency of a challenge. The

findings were presented in tables and frequency distribution charts.

A copy of the survey questionnaire is provided in appendix 1

23

CHAPTER FOUR: DATA ANALYSIS AND FINDINGS

4.1 Introduction

This chapter contains data analysis and presentation of findings. The research

questions are answered in this chapter in line with the objectives of the study and

information sought to determine the outcome.

4.2 General Findings

4.2.1 Response rate

The number of questionnaires presented to the respondents was 50 a total of 42 were

completed satisfactorily and returned; this gave the study an eighty four percent

response rate. The table below shows the study’s response rate.

Table 2: Response rate

Valid responses Respondents Percentage Number

(%)

Expected Responses 50 100%

Received Responses 42 84%

Un received Responses 8 16%

Source: Research Data (2014)

24

From Table 2 above it can be deduced that the respondents were cooperative and

provided sufficient responses to the questionnare. The study had a response rate of

84% based on the duly and correctly filled questionnaires.

4.2.2 Firms operation period

The study sort to determine the period of time the firms under study had been in

operation, this would help the researcher determine if the sample was experienced in

Manufacturing. The findings of the study are as shown in the table below:

Table 3: Duration in Business

Frequency Percentage

Cumulative

Percentage

Valid 1-3 years 1 2.4 2.4

3-5 years 9 21.4 23.8

5-10 years 16 38.1 61.9

over 10 years 16 38.1 100.0

Total 42 100.0

Source: Research data

The respondents were asked to indicate the number of years they have been in

business. Majority of the respondents representing 38.1% responded that they have

been in business for 5-10years consequently another 38.1% also indicated that they

have been in business for over 10years. 21.4% of the respondents said they have been

in business for 3-5 years, also 2.4% of the respondents said they have been in business

for 1-3 years. From the results it can be inferred that majority of the respondents had

25

the necessary experience in manufacturing and had utilized lean manufacturing

practices. Therefore they could give an objective response.

Figure 1: Graph showing duration in business

The figure above shows a diagrammatic representation of the number of years the

respondents have been in business.

4.2.3 Annual Turnover

The researcher sought to find out the annual turnover of manufacturing firms in

Mombasa county.

The findings were presented in the table below

26

Table 4: Annual Turnover

Frequency percentage

Cumulative

Percentage

Valid less than kshs 500,000 1 2.4 2.4

less than kshs 1 million 1 2.4 4.8

less than kshs 2 million 6 14.3 19.0

over kshs 2 million 34 81.0 100.0

Total 42 100.0


Respondents were asked to indicate their annual turnover, majority of the respondents

representing 81% said their turnover was over kshs 2million. 14.3% said their annual

turnover was less than kshs 2million, while 2.4% said that their turnover was less than

kshs 500,000. From the above results it can be inferred that the majority of the

respondents have an annual turnover of more than kshs 2million and thus can afford

to practice lean manufacturing.

27

Figure 2: Graph showing Annual Turnover of Manufacturing firms in Mombasa

County

The figure above shows a diagrammatic representation of the annual turnover of the

respondents.

4.2. Improvement in operational performance

Table 5: Improvement in operational performance

Frequency Percent Valid Percent

Cumulative

Percent

Valid yes 42 100.0 100.0 100.0


28

The respondents were asked to indicate if there was improvement in operational

performance; all the respondents representing 100% responded that there was

improvement in operational performance.

Figure 3: A pie chart of improvement in operational performance

The figure above shows a diagrammatic representation of improvement in operational

performance.

4.3 Lean Manufacturing Practices

In this section the descriptive of the lean manufacturing practices are analyzed and

interpreted. The study sought to determine the extent to which lean manufacturing

practices were implemented in the manufacturing firms under study. The findings are

as shown below:

29

4.3.1.JUST IN TIME

Table 4: Descriptive statistics of Just in Time

N Mean

Std.

Deviation Variance

Rank

Statistic Statistic Std. Error Statistic Statistic Statistic

Production of products with same components and spares 42 3.81 .109 .707 .499 4

Reduced inventory levels and spare requirements 42 4.33 .106 .687 .472 3

Faster response to customer demands and order 42 4.60 .077 .497 .247 1

Output as per demand (Pull production) 42 4.38 .083 .539 .290 2

Cumulative average 4.28 .375 .6075 .377

Source: Research data,2014

From table 3, The study found that the cumulative average of variance for just in time

implementation practice was .377. Faster response to customer demands and orders

which had a mean of 4.60 was viewed by the respondents the JIT practice that was

most implemented. This was followed by Output as per demand (pull production)

which had a mean of 4.38. The least viewed as being implemented was production of

products with same components and spares was ranked 4th with a mean of 3.81. The

study also revealed that JIT was mainly implemented where there's need to reduce

inventory levels and creation of space. It was also revealed that JIT was being

implemented by the manufacturing to fasten response to customers demands and

orders and increase output per demand.

30

4.3.2 Continuous improvement or Kaizen

Table 5: Descriptive statistics of Continuous improvement or Kaizen

N Mean

Std.

Deviation Variance Rank

Statistic Statistic

Std.

Error Statistic Statistic

Statistic

Incremental improvement of manufacturing process 42 4.67 .074 .587 .228 2

Improved customer service and product quality 42 4.40 .091 .477 .344 1

Improving teamwork and innovation 42 4.24 .082 .532 .283 3



From table 5 above the total cumulative average variance for the extent to which

kaizen was being implemented by the manufacturing firms was found to be .532. The

study revealed that Kaizen was implemented continuously so as to improve the

manufacturing process which hada mean score of 4.67. Kaizen was also put into

practice to improve product quality and enhance customer services with the second

highest mean of 4.4. The impact of Kaizen in improving team work and innovation

was felt with a mean score of 4.24.

31

4.3.3 Total productive Maintenance

Table 6: Descriptive statistics of Total Product Maintenance

TPM parameters

N Mean

Std.



Shared responsibility for equipment 42 3.95 .068 .439 .344 3

Operators maintain their own

equipment42 4.00 .096 .625 .228

2

Maximized use of plant equipment 42 4.62 .083 .539 .283 1



From the table 6, the average variance for TPM was 0.285.It can be seen that the

respondents viewed that maximized use of plant equipment with a mean of 4.62 was

high after implementing TPM. Operators had being empowered to maintain their own

equipment having a mean of 4.00 and lastly shared responsibility for equipment

which had a mean of 3.95.This shows that TPM encourages operators to take care of

their working equipment encouraging teamwork through shared responsibility and

thus maximize its use or lifespan.

32

4.3.4. Automation/Jidoka

Table 7: Descriptive statistics of Automation/Jidoka

Descriptive Statistics

N Mean

Std.



Assist workers with masculine requirements of work 42 4.45 .103 .670 .449 2

Workers can monitor multiple stations 42 4.62 .083 .539 .290 1

Quickly identify and resolve manufacturing issues 42 4.36 .075 .485 .235 3



The table above shows the descriptive statistics for Jidoka. It can be seen that workers

can monitor multiple stations with a mean of 4.62 was viewed by respondents to have

been completely implemented. This was followed by Assist workers with masculine

requirements of work which had a mean of 4.45 and the least Jidoka practice

implemented was quickly identify and resolve manufacturing issues which had a

mean of 4.36.

33

4.2.5 Value streaming Mapping

Table 8: Descriptive statistic of Value stream mapping

N Mean

Std.



Interpreted flow of information and materials 42 4.10 .057 .370 .137 2

Visual picture of current and future objectives 42 4.02 .087 .563 .316 3

Improved communication and teamwork 42 4.21 .073 .470 .221 1



Table 8 above indicates that value streaming improved communication and team work

since it had a mean of 4.21. This was enabled because of the interpreted flow of

information and materials which had a mean of 4.10 .Lastly it was also found out that

the firms were visionary since both current and future objectives were taken into

account. This shows that the firms were geared towards improving the firm

performance.

4.2.6 summary of lean manufacturing practices

Table 9: Lean manufacturing practices

34

Lean manufacturing practices Mean Std. Deviation Variance Rank

Just in time 4.28 .6075 .377 3

Continuous improvement/ kaizen 4.44 .532 .283 2

Total productive maintenance (TPM) 4.19 .534 .285 4

Automation/ Jidoka 4.48 .565 .325 1

Value stream mapping (VSM) 4.11 .468 .225 5


From table 9 above, the lean manufacturing practices were analyzed to find out which

practices are implemented by the manufacturing firms. From the results, the a good

number of manufacturing firms automation with a mean of 4.48, then CI coming in

second, followed by JIT, TPM had also been implemented by manufacturing firms in

Mombasa county then VSM was least implemented with a mean of 4.11.From the

results we can deduce that there is extensive LM implementation by manufacturing

firms in Mombasa county.

Category 1 Category 2 Category 3 Category 4 catergory 5

00.5

11.5

22.5

33.5

44.5

5

Series 1Series 2

Series 3series 4

Series 1Series 2Series 3series 4

35

4.4 Lean manufacturing adoption challenges

The study sought to determine the challenges faced by the firms in adopting lean

manufacturing practices .The findings of the study were as shown below:

Table 9: Lean manufacturing adoption practices

Description 1 2 3 4 5 Total

Resistance to change 0.063 0.25 0.656 0.875 1.563 3.407

Lack of top management support 0.0625 0.313 0.563 1 1.25 3.1875

Failure to implement lean into the supply

chain

0.125 0.375 0.375 1.625 0.781 3.281

Wrong implementation sequence 0.125 0.25 0.375 1.125 0.938 2.813

Customer dissatisfaction 0 0 0.032 0.102 0 0.134

Plant size issues 0.313 0 0 0.656 1.125 2.094

High costs of implementation 0 1.25 0.475 1 0.656 3.381

Source: Research Data

From table 11 above it was deduced that the main challenges affecting the adoption of

lean manufacturing practices were resistance to change due to fear of the unknown

and high costs of implementation. Resistance to change had the highest score of 3.407

while costs of implementation was second with a score of 3.381.Failure to implement

lean into supply chain was thirdly ranked as a factor affecting the implementation of

lean manufacturing practices with a score of 3.281.The study revealed that plant size

was also a factor affecting the adoption of the lean practices as the size has direct

36

influence on the scale of operation. Wrong implementation sequence after adoption

was also deduced as a factor hindering the adoption of the lean manufacturing

practices.

4.4 Correlation Analysis

Correlation is a single number that describes the degree of the relationship between

two variables. A Pearson correlation indicated the direction, strength and significance

of the multi variety relationships for all variables in this study. According to Sekeran

(2003) theoretically there could be a perfect positive correlation between two

variables which is represented by 1.0 or a perfect negative correlation which is

represented by -1.0

37

Table 10: Correlation

Correlations

Operational

performance JIT CI TPM JIDOKA VSM

Operational performance Pearson Correlation 1 -.950 .889 .274 -.939 -.835

Sig. (2-tailed) .050 .111 .726 .061 .165

N 4 4 4 4 4 4

JIT Pearson Correlation -.950 1 -.843 -.325 .806 .875

Sig. (2-tailed) .050 .157 .675 .194 .125

N 4 4 4 4 4 4

CI Pearson Correlation .889 -.843 1 -.187 -.931 -.519

Sig. (2-tailed) .111 .157 .813 .069 .481

N 4 4 4 4 4 4

TPM Pearson Correlation .274 -.325 -.187 1 -.006 -.738

Sig. (2-tailed) .726 .675 .813 .994 .262

N 4 4 4 4 4 4

JIDOKA Pearson Correlation -.939 .806 -.931 -.006 1 .611

Sig. (2-tailed) .061 .194 .069 .994 .389

N 4 4 4 4 4 4

VSM Pearson Correlation -.835 .875 -.519 -.738 .611 1

Sig. (2-tailed) .165 .125 .481 .262 .389

38

N 4 4 4 4 4 4


The correlation analysis table provided the nature of the relationships between the

variables. The study found that operational performance was negatively correlated to

just in time with a negative correlation value of -0.95.The correlation analysis also

revealed that operational performance was positively correlated to continuous

improvement with the two having a correlation value of 0.889 .The effect of

continous improvement on operational performance was found to be significant since

the significant (0.111) value was above the threshold of 0.05.

The study also revealed that operational performance was positively correlated to total

productive maintenance with the two having a correlation value of 0.274.The effect of

total productive maintenance on operational performance was found to be highly

significant with the significant value being 0.726. Jidoka was found to be negatively

correlated to operational performance with a correlation of -0.939. The effect of

jidoka on operational performance was f0ound to be slightly significant with the

calculated significant value being 0.061 .

It was deduced by the study that value stream mapping was negatively correlated to

operational performance of the firms under study. Operational performance had a

correlation value of -0.835 with value stream mapping. The impact of value stream

mapping was found to be slightly significant with a value of 0.165 which was above

0.05 significant level.

39

The study also revealed how the implementation of one lean practice was correlated

to the other. The study found out that Just in time was negatively correlated to

continuous improvement and total productive maintenance with the two having a

negative correlation values of -.843 and -.325 respectively .This finding indicates that

the higher the implementation of continuous improvement practice and total

productive maintenance the lower the possibility or chances of just in time being put

into practice. It was also deduced that just in time was positively correlated to jidoka

and value stream mapping .Jidoka and value stream mapping had correlation values of

0 .806 and 0.875 with Just in time. Thus the adoption of value stream mapping and

jidoka propels the implementation of just in time practice.

4.5 Regression Analysis

The regression model was done with operational performance indicators such as

product quality, flexibility, delivery speed and manufacturing costs as the dependent

variable. The findings of the study are as shown below:

Table 11: Regression coefficients

40

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

T Sig.

95.0% Confidence Interval for B Collinearity Statistics

B Std. Error Beta Lower Bound

Upper

Bound Tolerance VIF

1 (Constant) -9.245 211.498 -4.371E-2 .972 -2696.582 2678.093

JIT -.857 .616 -.692 -1.392E0 .397 -8.684 6.969 .289 3.463

E0

CI

TPM

JIDOKA

VSM

1.249

1.120

-.526

-2.335

2.037

.892

.120

.542

.305

.269

-0.938

-1.338

6.131E-1

1.256E0

-4.379

-4.311

.650

.428

.143

.145

-24.639

-1033.927

-2.053

-9.219

27.138

954.608

1.001

4.548

.289

1.00

1.001

4.55

3.463

E0

1.00

1.001

2.196

a. Dependent Variable: Operational performance

The regression coefficient table above helped establish the following regression

equation

Op = - 9.245 - 0.857JIT + 1.249CI + 1.120TPM - 0.526JIDOKA - 2.335VSM

From the above equation the study found that holding just in time, continuous

improvement, total productive maintenance, jidoka and value stream mapping lean

manufacturing practices to constant zero, operational performance of the of the firm

would be -9.245.This observation indicates that the practices play an important role in

enhancing the operational efficiency and performance of the manufacturing firms. A

factor increase in continuous improvement would lead to an increase in the

operational performance of the firm by factor of 1.249 and also a unit increase in total

productive maintenance would lead to an increase in firm operational performance

by a factor of 01.120.Thus the study found that continuous improvement and total

41

productive maintenance were positively related to operational performance as shown

by table10 above. The study found that just in time, jidoka and value stream mapping

had an inverse relationship to operational performance. A factor decrease in just in

time would lead to an increase in operational performance by a factor of 0.857.Also a

factor decrease of jidoka and value stream mapping would increase operational

performance by a factors of 0.526 and 2.335 respectively. This information shows

that there’s a positive relationship between continuous improvement, total productive

maintenance and operational performance. It also showed that there was a inverse

relationship between just in time, jidoka ,value stream mapping and operation

performance.

Table 12: Regression model summary

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square Change F Change df1 df2 Sig. F Change

1 .964a .929 .786 7.27213201 .929 6.497 2 1 .267

a. Predictors: (Constant), CI, JIT,TPM,JIDOKA,VSM

The regression model summary helped in determining the nature and strength of the

variables relationship. It also showed the proportionate variability changes on the

dependent variable as a result of changes in independent variables.

From the data in table 11 above the adjusted R 2 was 0.786 which means that there

was 78.6% variation in operational performance due to changes in lean

manufacturing practices. The correlation coefficient tells us the strength of

relationship between the variables. The study found that the correlation coefficient

42

was 0.964 thus there was a strong positive relationship between the lean

manufacturing practices under study and the firms operational performance. The R2

equally confirmed that there was a high correlation between the lean manufacturing

practices and the firms operational performance with 92.9% of the manufacturing

firms operational performance changes depending on the changes of just in time,

continuous improvement, jidoka, total productive maintenance and value stream

mapping(lean manufacturing practices).

Table 13: Analysis of variance

ANOVAb

Model Sum of Squares Df Mean Square F Sig.

1 Regression 687.190 2 343.595 6.497 .267a

Residual 52.884 1 52.884

Total 740.074 3

a. Predictors: (Constant), CI, JIT,TPM,JIDOKA,VSM

b. Dependent Variable: Operational performance

Analysis of variance was done so as to test the reliability of the regression model

used in establish the nature and strength of the relationship between the variables.

From the above ANOVA table the significant value for the model was 0.267 which

means that the model was statistically significant in analyzing variables the since the

significant value was above the threshold of 0.05.

43

CHAPTER FIVE: SUMMARY, CONCLUSIONS AND

RECOMMENDATIONS

5.1 Introduction

This chapter presented the summary of key findings, which were set out in order with

the study objectives. The objectives of the study were: to determine the extent to

which manufacturing firms in Mombasa County have adopted lean manufacturing

practices, to establish the effect of lean manufacturing practices on operational

performance of manufacturing firms in Mombasa County and to find out the

challenges faced by manufacturing firms in adopting lean manufacturing practices. It

also presented the conclusions and recommendations of the study.

5.2 Summary of the findings and Conclusions

44

5.2.1 Effects of lean manufacturing practices on operational performance

The study found that holding just in time, continuous improvement, total productive

maintenance, jidoka and value stream mapping lean manufacturing practices to

constant zero, operational performance of the of the firm would be -9.245.This

observation indicates that the practices play an important role in enhancing the

operational efficiency and performance of the manufacturing firms (Table 11)

Thus the study found that continuous improvement and total productive maintenance

were positively related to operational performance as shown by table11 above. The

study found that just in time, jidoka and value stream mapping had an inverse

relationship to operational performance.

The study found that factor decrease in just in time would lead to an increase in

operational performance. Also a factor decrease of jidoka and value stream mapping

would increase operational performance This information shows that there’s a

positive relationship between continuous improvement, total productive maintenance

and operational performance. It also showed that there was a inverse relationship

between just in time, jidoka ,value stream mapping and operation performance.

4.4: Operational Performance

In this section the operational performance statistics are computed and interpreted.

Table 10: Descriptive statistics Product Quality

45

N Mean

Std.



Improved features in products 42 4.17 .076 .490 .240 2

Quality output without defects 42 4.07 .079 .513 .263 3

Increased product reliability and usability 42 4.31 .080 .517 .268 1

Valid N (listwise) 42


From the table 10, it can be seen that the respondents agreed that increased product

reliability and usability which had a mean of 4.31 is an effect of lean manufacturing

practices on product quality. This was followed by improved features in products

which had a mean of 4.17 and the least was quality output without defects which had

a mean of 4.07.

Table 11: Descriptive statistics of Flexibility

N Mean

Std.

Deviation Variance

Rank


Increased product customization 42 4.14 .080 .521 .272 3

Reduced set-up time of production equipment 42 4.38 .083 .539 .290 2

Faster response to customers and timely delivery 42 4.45 .078 .504 .254 1



46

From table 11, it can be seen that the respondents strongly agreed that faster response

to customers and timely delivery which had a mean of 4.45 is an effect of lean

manufacturing practices on flexibility. This was followed by reduced set-up time of

production equipment which had a mean of 4.38 and the least agreed with was

increased product customization which had a mean of 4.14.

Table 12: Descriptive statistics of Delivery speed

N Mean

Std.

Deviation Variance

Rank


Speedy delivery of customer orders 42 4.36 .082 .533 .284 1

Product availability 42 4.33 .081 .526 .276 2

Raw materials readily available or near firm 42 4.33 .081 .526 .276 2



From table 12, the respondents agreed that the most effect of lean manufacturing

practices on delivery speed was speedy delivery of customer orders which had a mean

of 4.36. This was followed by product availability and raw materials being readily

available or near firm which both had a mean of 4.33.

Table 13: Descriptive statistics of manufacturing costs

47

N Mean

Std.

Deviation Variance

Rank

Statistic Statistic Std. Error Statistic Statistic Statistics

Reduced material costs and stock out costs 42 4.19 .070 .455 .207 3

Reduced manufacturing costs 42 4.24 .075 .484 .235 1

Uninterrupted production thus lower costs 42 4.24 .089 .576 .332 1



From table 13, it can be seen that the respondents most agreed that uninterrupted

production thus lower costs and reduced manufacturing costs which both had a mean

of 4.24 are effects of lean manufacturing practices on manufacturing costs. The least

agreed with was reduced material costs and stock out costs which had a mean of 4.19.

4.5: Challenges of Adopting Lean Manufacturing

In this section the descriptive statistics of the challenges of adopting lean

manufacturing are computed and analyzed.

Table 14: descriptive statistics of Adopting Lean Manufacturing

48

N Mean

Std.

Deviation Variance

Rank

Statistic Statistic Std. Error Statistic Statistic Statistics

Resistance to change 42 3.83 .090 .581 .337 4

Lack of top management support 42 3.52 .161 1.042 1.085 7

Failure to implement lean into the supply chain 42 4.24 .159 1.031 1.064 2

Wrong implementation sequence 42 3.98 .169 1.093 1.195 3

Customer dissatisfaction 42 3.55 .174 1.131 1.278 6

Plant size issues 42 3.74 .184 1.191 1.418 5

High costs of implementation 42 4.26 .184 1.191 1.418 1

Any other challenge (specify) 42 1.26 .149 .964 .930 8



From table 14, the respondents agreed that high cost of implementation which had a

mean of 4.26 was a challenge of adopting lean manufacturing practices. This was

followed by failure to implement lean into the supply chain which had a mean of 4.24,

then it was followed by wrong implementation sequence which had a mean of 3.98

which was followed by resistance to change which had a mean of 3.83. The least

agreed with as the challenge of adopting lean manufacturing was lack of top

management support which had a mean of 3.52. The respondents were also asked to

indicate if there were any other challenges of adopting lean manufacturing and they

slightly disagreed that there were any other challenges.

4.6: Regression Analysis

49

In this section regression analysis was done to determine if there is a relationship

between lean manufacturing practices and operational performance.

Table 15: Regression model summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square Change F Change df1 df2 Sig. F Change

1 .964a .929 .786 7.27213201 .929 6.497 2 1 .267

a. Predictors: (Constant), Value stream mapping (VSM), just in time, continuous improvement/ kaizen,

automation/jidoka, total product maintenance (TPM)

From data in the above table 15 the adjusted R 2 was a 0.964 which means that there

was 96.4% positive variation in operational performance index due to changes in

Value stream mapping (VSM), just in time, continuous improvement/ kaizen,

automation/jidoka and total product maintenance (TPM). The correlation coefficient

tells us the strength of the relationship between the variables. The study found that the

correlation coefficient was 0.929 thus there was a strong positive relationship between

the lean manufacturing practices and operational performance.

Table 16: Analysis of Variance

50

Model Sum of Squares df Mean Square F Sig.

1 Regression 687.190 5 137.438 6.497 .267a

Residual 52.884 2 26.442

Total 740.074 7

a. Predictors: (Constant), Value stream mapping (VSM), just in time, continuous improvement/ kaizen,

automation/jidoka, total product maintenance (TPM)

b. Dependent Variable: Operational performance index

From the above ANOVA table the significant value for the model was 0.267 which

means that the model was statistically significant since it is higher than 0.05.

Table 17: Regression coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients

T Sig.B Std. Error Beta

1 (Constant) -9.245 211.498 -4.371 .972

Just in time -.857 .616 -.692 -1.392 .397

Continuous improvement

Total productive management

Automation

Value stream mapping

1.249

1.120

-.526

-2.335

2.037

.892

.120

.542

.305

.269

-0.938

-1.338

6.131

1.256

-4.379

-4.311

.650

.428

.143

.145


From the above table the following regression equation was established

Y= - 9.245 - 0.857X1 + 1.249X2 + 1.120X3 - 0.526X4 - 2.335X5 + 211.498

51

From the above equation the study found that holding Value stream mapping (VSM),

just in time, continuous improvement/ kaizen, automation/jidoka and total product

maintenance (TPM) to constant zero, Operational performance index (dependent) of

the petroleum distributing firms would be -9.245.

A factor decrease in just in time would lead to an increase in operational performance

by factor of 0.857, a unit increase in continuous improvement would lead to an

increase in operational performance by 1.249, an increase in a unit of total product

maintenance by a factor of one would lead to an increase of 1.120 in the firm’s

operational performance, a unit decrease in automation or jidoka would lead to an

increase in operational performance by 0.526, a unit decrease in value stream

mapping would lead to a 2.335 increase in operational performance. This information

shows that there’s a positive relationship between, continuous improvement/ kaizen

and total product maintenance (TPM) and operational performance. It also showed

that there was a negative relationship between automation/jidoka, Value stream

mapping (VSM), just in time and operational performance.

52

CHAPTER FIVE: SUMMARY, CONCLUSION AND

RECOMMENDATIONS

5.1 Introduction

This chapter summarizes the research findings and also presents conclusions and

recommendations of the study. The conclusions are drawn from the findings of the

study which sought to find out the extent to which manufacturers have adopted lean

manufacturing practices, the effect of lean manufacturing practices on operational

performance and the challenges of adopting lean manufacturing.

5.2 Summary of Findings

The objectives of the study were to establish the extent to which manufacturers have

adopted lean manufacturing practices in Mombasa County, the effect of lean

manufacturing practices on operational performance and the challenges of adopting

lean manufacturing

The target respondents were heads of operations departments, general managers,

logistics managers, distribution managers and their supervisors. Most of them have

been in business for over 10years. Most of the respondents had an annual turnover of

over kshs. 2million.

53

5.2.1 To establish the extent to which manufacturers have adopted lean

manufacturing practices

The research outcome provides an insight on the extent to which manufacturers have

adopted lean manufacturing practices. Just in time, continuous improvement/kaizen,

total productive maintenance, automation/jidoka and value stream mapping were the

lean manufacturing practices under research in this section and the respondents agreed

that the firms have extensively implemented these practices.

5.2.2 The effect of lean manufacturing adoption on operational performance

Product quality, flexibility, delivery speed and manufacturing costs were the

performance indicators used. From the results the respondents strongly agreed that

adoption of lean manufacturing practices have a positive effect on operational

performance, through the improved features in product quality, increased product

customization, reduced set-up time, speedy delivery of consumer orders and reduced

manufacturing costs.

From the regression analysis, there’s a positive relationship between Value stream

mapping (VSM), just in time, continuous improvement/ kaizen and total product

maintenance (TPM) and operational performance. It also showed that there was a

negative relationship between automation/jidoka and operational performance.

The study established following regression equation

Y= - 9.245 - 0.857X1 + 1.249X2 + 1.120X3 - 0.526X4 - 2.335X5 + 211.498

From the above equation the study found that holding Value stream mapping (VSM),

just in time, continuous improvement/ kaizen, automation/jidoka and total product

54

maintenance (TPM) to constant zero, Operational performance index (dependent) of

the petroleum distributing firms would be -9.245.

A factor decrease in just in time would lead to an increase in operational performance

by factor of 0.857, a unit increase in continuous improvement would lead to an

increase in operational performance by 1.249, an increase in a unit of total product

maintenance by a factor of one would lead to an increase of 1.120 in the firm’s

operational performance, a unit decrease in automation or jidoka would lead to an

increase in operational performance by 0.526, a unit decrease in value stream

mapping would lead to a 2.335 increase in operational performance. This information

shows that there’s a positive relationship between, continuous improvement/ kaizen

and total product maintenance (TPM) and operational performance. It also showed

that there was a negative relationship between automation/jidoka, Value stream

mapping (VSM), just in time and operational performance.

5.2.3 Challenges of adopting lean manufacturing

High cost of implementation was seen as the most agreed with to be a challenge of

adopting lean manufacturing. This was followed by failure to implement lean into the

supply chain then it was followed by wrong implementation sequence which was

followed by resistance to change. The least agreed with as the challenge of adopting

lean manufacturing was lack of top management support. The respondents were also

asked to indicate if there were any other challenges of adopting lean manufacturing

and they slightly disagreed that there were any other challenges.

55

5.3 Conclusions

From the findings the study there is a strong positive correlation between lean

manufacturing practices and operational performance; Manufacturers have

extensively implemented the lean manufacturing practices. The challenges faced by

manufacturers in adoption of lean manufacturing are resistance to change lack of top

management support failure to implement lean into the supply chain wrong

implementation sequence customer dissatisfaction plant size issues high costs of

implementation.

5.4 Recommendations

From the findings and conclusions of the study, manufacturing firms should adopt

lean manufacturing practices such as just in time, kaizen; jidoka value stream

mapping and total productive maintenance in order positively affect operational

performance.

The manufacturing firms should also be in the front run in addressing the challenges

which affect the implementation of service quality management. These challenges are

resistance to change lack of top management support failure to implement lean into

the supply chain wrong implementation sequence customer dissatisfaction plant size

issues high costs of implementation.

5.5 Limitations of the study

Firstly, the study was limited in scope by the fact that it only covered manufacturing

firms in Mombasa County. Ideally, there would have been representativeness if it

covered all the manufacturing firms in Kenya. Secondly, the researcher faced some

56

resistance from some of the respondents as they feared that the information they gave

would be used by competitors to fight them business wise. This was however resolved

through the issuance of the introduction letter and explanation that the information

would be confidential.

Thirdly, the researcher also faced challenges in terms of resources such as finances for

commuting to the different firms and time in the sense that, a lot of time was needed

to going to the firms, meeting with managers, convincing them to fill the

questionnaires and finally going back to pick them.

5.6 Suggestions for Future Research

The study only covered manufacturing firms in Mombasa County there is need to

conduct a research on the several manufacturing firms in Kenya as a whole and see if

there will be any variations in the findings.

57

APPENDIX 1

A1.Questionnaire

I am a post graduate student at the University of Nairobi. As part of the requirements

for the Master of Business Administration course, I am required to conduct research

and develop a research project (report) thus this questionnaire is meant to help me in

data collection. Kindly assist by participating in answering the questions. The

information collected will strictly be used for academic purposes. The topic for the

project is: The effect of Lean Manufacturing Practices on operational performance of

manufacturing firms in Mombasa County, Kenya.

SECTION A: GENERAL INFORMATION

Kindly answer all the questions by ticking in the appropriate box or filling in the

spaces provided.

Q1. Name of organization...................................................................................

Q2. Organization address.....................................................................................

Q3. Type of business.............................................................................................

Q4.How long has the firm been in operation?

a) 1-3 years [ ]

b) 3-5 years [ ]

c) 5-10 years [ ]

58

d) Over 10 years [ ]

Q5. Annual turnover

a) Less than Kshs 500,000 [ ]

b) Less than Kshs1million [ ]

c) Less than Kshs 2millon [ ]

d) Over Kshs 2million [ ]

Q6.Has the company been improving in Operational performance? YES [ ] NO [ ]

SECTION B: Part One (1) Lean Manufacturing Practices

Q7.what extent has the Lean manufacturing practices implemented?

Using a five-point Likert scale state the extent of implementation. = no

implementation, 2 = little implementation, 3 = some implementation, 4 = extensive

implementation, and 5 = complete implementation.

Lean Manufacturing Practices Scale

1 2 3 4 5

1. Just in Time

a) Production of Products with same components and spares

b) Reduced inventory levels and space requirements

c) Faster response to customer demands and order

d) Output as per demand (Pull production)

2. Continuous Improvement/ Kaizen

59

a) Incremental improvement of manufacturing process

b) Improved customer service and product quality

c) improving teamwork and innovation

4. Total Productive Maintenance (TPM)

a) Shared responsibility for equipment

b) Operators maintain their own equipment

c) Maximized use of plant equipment

4. Automation/Jidoka

a) Assist workers with masculine requirements of work

b) Workers can monitor multiple stations

c) Quickly identify and resolve manufacturing issues

5. Value Stream Mapping (VSM)

a) Interpreted flow of information and materials

b) Visual picture of current and future objectives

c) Improved communication and teamwork

SECTION B: Part Two (2) Operational Performance

Q8.What is the effect of lean manufacturing practices on operational performance as

per the indicators. State the extent to which you agree with the following lean

manufacturing effects on operational performance.1= Slightly disagree, 2 = Disagree,

3= Slightly Agree, 4 = Agree, 5= Strongly agree.

LM practice

Indicator

1 2 3 4 5

60

A. Product quality

A1. Improved features in products

A2. Quality output without defects

A3. Increased the product reliability and usability

B. Flexibility

B1.Increased Product customization

B2. Reduced set-up time of production equipment

B3. faster response to customers and timely delivery

D. Delivery speed

D1. Speedy delivery of customer orders

D2. Product availability

D3. Raw materials readily available or near firm

E. Manufacturing costs

E1.Reduced material costs and stock out costs

E2. Reduced manufacturing costs

E3. Uninterrupted production thus lower costs

61

SECTION C: Challenges of adopting Lean Manufacturing

Q12. Please indicate the extent to which each of the following factors was a challenge

in the implementation and lean manufacturing performance. Using the following

scale: 1= Slightly disagree, 2 = Disagree, 3= Slightly Agree, 4 = Agree, 5= Strongly

agree.

Challenges 1 2 3 4 5

Resistance to change

Lack of top management support

Failure to implement lean into the supply chain

Wrong implementation sequence

Customer dissatisfaction

Plant size issues

High costs of implementation

Any other challenge (specify)

Thank you for your cooperation.

62

CHAPTER FOUR: DATA ANALYSIS AND PRESENTATION

4.1 Introduction

This chapter presented the main findings of the study based on the research

objectives. The data collected from the field was analyzed and the results of the

findings presented in form of tables, pie charts and bar graphs.

4.2 General Findings

4.2.1 Response rate

63

The number of questionnaires presented to the respondents was 50 a total of 47 were

successfully completed and returned; this gave the study 94% response rate. The table

below shows the study’s response rate.

Table 2: Response rate

Valid responses Respondents Percentage Number

(%)

Expected Responses

Received Responses

Un received Responses

50

43

7

100%

86%

14%


From Table 2 above it can be deduced that the respondents were cooperative and

provided sufficient responses to the stated questions in the questionnaires

administered. The study had a response rate of 86% of properly and correctly filled

questionnaires.

No. Sector

Total

Pop. size

Sample

size

response

1 Building construction and mining 7 4 4

2 Chemical and allied 10 2 2

3 Energy, electrical and electronics 6 2 2

4 Fresh produce 0 0 0

5 Food and beverages 24 10 10

6 Leather and footwear 0 0 0

7 Metal and allied 16 7 7

64

8 Motor vehicle and Accessories 7 3 1

9 Paper and paperboard 4 2 1

10 Pharmaceutical & Med equipment 2 1 1

11 Plastic and Rubber 9 3 2

12 Services and Consultancy 27 10 7

13 Textile and Apparels 18 7 4

14 Timber, wood and furniture 0 0 0

Total 130 50 43

4.2.2 Manufacturing firms’ size and experience in manufacturing.

The study sort to determine the size of the manufacturing plant through annual

turnover in Kenyan shillings. And experience through years of operation. The

findings of the study are as shown below:

Table 3: Years the manufacturing firms have been on Operation.

Duration (Years) Frequency Percentage (%)

1-3 Years 0 0%

3 -5 Years 8 18.6%

5- 10 Years 14 32.6%

Over 10 Years 21 48.8%


65

The study revealed that most of the firms surveyed had been in operation for a period

of 10 years and above with a 48.8% average as illustrated by table 3 above. No firms

had been in operation for a period between 1-3 years. Thus many of the firms had

been in existence for a while and thereby had experience in manufacturing and must

have utilized lean manufacturing practices in one way or the other.

The researcher also wanted to determine the size of the firms using level of annual

turnover of the manufacturing firms under investigation. The study found out that

most of the manufacturing firms were large enough to implement some lean

manufacturing practices as they had an annual turnover of more than two million. As

shown in the table below:

Annual turnover Frequency Percentage (%)

Less than Kshs 500,000 0 0%

Less than Kshs1million 2 4.7%

Less than Kshs 2millon 7 16.3%

Over Kshs 2million 34 79%

4.3 Lean manufacturing practices

The study sought to determine the extent to which lean manufacturing practices were

implemented in the manufacturing firms under study. The findings are as shown

below:

Table 4: Just in Time implementation extent

Extent of Implementation Average

Production of products with same components .214

66

Reduced inventory levels and space requirements .886

Faster response to customer demands and order .847

Production of products as per customer demand(Pull production) .864

Total cumulative variance 2.811


The study found that the cumulative variance for just in time implementation practice

was 2.811. The study also revealed that JIT was mainly implemented where there's

need to reduce inventory levels and creation of space. This need of inventory

reduction and space creation had a variance of .886 as illustrated by table 4. It was

also revealed that JIT was being implemented by the manufacturing firms resulted to

faster response to customer’s demands and orders and increased output per demand.

The two findings had variances of .847 and .864 respectively.

Table 5: Kaizen implementation extent

Extent of implementation Variance

Incremental improvement of Manufacturing process .877

Improved customer services and product quality .828

Improved team work and Innovation .787


67


From table 5 above the total cumulative variance for the extent to which kaizen was

being implemented by the manufacturing firms was found to be 1.892. The study

revealed that Kaizen was implemented continuously so as to improve the

manufacturing process. Kaizen was also put into practice so as to improve product

quality and enhance customer services in return. The impact of Kaizen in improving

team work and innovation as a necessity for its adoption and implementation had a

variance of .787.

Table 6: Total productive Maintenance implementation extent


Shared responsibility for equipments .884

Operators Maintain their own equipments .816

Maximized use of plant equipments .897



The study also sought to determine the extent of TPM implementation within the

manufacturing firms under study. The study found out that the firms maximized use

of plant so as to ensure total productive maintenance. Maximized plant usage had a

variance of .897.The study also revealed that there was shared responsibility for

equipments with a variance of .884. It was also deduced that operators did maintain

their own equipments to some extent. The total variance value for productive

maintenance implementation was 2.597.

68

Table 7: Jidoka implementation extent


Assist workers with masculine requirements .268

Workers monitors multiple stations . 689

Quickly identify and resolve manufacturing issues .946



Table 7 above revealed that jidoka was implemented so as to help in quickly

identification and resolving of manufacturing issues. Jidoka was also being

implemented to ease workers monitoring of multiple stations. According to the study

resolving manufacturing process had the highest variance of .946 while monitoring

had a variance of .689.The total variance for jidoka was found to be 1.903

Table 8: Value Stream mapping (VPM)


Interpreted flow of information and material .563

Visual picture of current and future objectives .795

Improved communication and team work .678

69



It was found out that the Value stream mapping practice was implemented to some

extent. Its implementation had a total variance of 2.036.The practice was mainly

implemented to enable visualization of current and future objectives. It was also

implemented to improve communication and team work.

4.4 Effects of lean practices on operational performance indicators

The study sought to determine the effects of lean manufacturing practices on

operational performance. The findings of the study were as follows.

Operational metric

LM Result

Quality Speed Cost Flexibility

Product performance .877 .108 .110 .175

Product features .840 .071 .194 .128

Perceived Overall product quality .828 .249 .094 .119

Order fulfillment speed .187 .884 .145 .264

Delivery speed .173 .874 .171 .273

Total product costs .153 .142 .907 .158

Direct Manufacturing costs .169 .145 .906 .142

Flexibility to change product mix .203 .215 .171 .843

Flexibility to change output volume .156 .317 .149 .643

Percentage of variance (%) 47.649 15.468 13.431 23.452

Cumulative percentage of variance (%) 47.649 63.117 76.548 100

70

It was deduced that lean manufacturing practices had different effects on operational

performance. The operational performance variables had different variances with

respect to the lean practices. Quality had a total variance of 47.649%, speed had a

variance value 15.468%, and cost had 13.431% while flexibility had 23.452%. It can

thus be deduced that quality and flexibility are greatly affected by lean manufacturing

practices

4.5 Correlation Analysis

Correlation is a single number that describes the degree of the relationship between

two variables. A Pearson correlation indicated the direction, strength and significance

of the bivariate relationships for all variables in this study. According to Sekeran

(2003) theoretically there could be a perfect positive correlation between two

variables which is represented by 1.0 or a perfect negative correlation which is

represented by -1.0

The study sought to determine the direction, strength and significance of the effects of

lean manufacturing practices on operational performance. The findings are as shown

below by table

Table 9: correlation analysis of variable

Correlations

Operational

performance JIT CI TPM JIDOKA VSM

Operational

performance

Pearson

Correlation

1 -.959* .889 .275 -.939 -.834

71

Sig. (2-tailed) .041 .111 .725 .061 .166

N 4 4 4 4 4 4

JIT Pearson

Correlation

-.959* 1 -.879 -.259 .838 .839

Sig. (2-tailed) .041 .121 .741 .162 .161

N 4 4 4 4 4 4

CI Pearson

Correlation

.889 -.879 1 -.188 -.934 -.515

Sig. (2-tailed) .111 .121 .812 .066 .485

N 4 4 4 4 4 4

TPM Pearson

Correlation

.275 -.259 -.188 1 -.006 -.740

Sig. (2-tailed) .725 .741 .812 .994 .260

N 4 4 4 4 4 4

JIDOKA Pearson

Correlation

-.939 .838 -.934 -.006 1 .610

Sig. (2-tailed) .061 .162 .066 .994 .390

N 4 4 4 4 4 4

VSM Pearson

Correlation

-.834 .839 -.515 -.740 .610 1

Sig. (2-tailed) .166 .161 .485 .260 .390

N 4 4 4 4 4 4

72

*. Correlation is significant at the 0.05 level (2-tailed).


From the correlation table above it can be deduced that correlation of one variable to

itself equals to 1

What is clear, lean production is not a guarantee for business success. It might be

a necessary but definitely not sufficient condition. However, we expect that firms with

better (and continually improving) operational performance sustain a moderate business

position. Advanced lean manufacturers likely belong to this group of companies. T.C.

Papadopoulou and M. O ¨ zbayrak

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