Benchmarking: An International Journal

Benchmarking: An International JournalEmerald Article: An integrated model for performance management of manufacturing unitsP. Parthiban, Mark Goh

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To cite this document: P. Parthiban, Mark Goh, (2011),"An integrated model for performance management of manufacturing units", Benchmarking: An International Journal, Vol. 18 Iss: 2 pp. 261 - 281

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An integrated modelfor performance management

of manufacturing unitsP. Parthiban

Department of Production Engineering, National Institute of Technology,Tiruchirappalli, India, and

Mark GohNUS Business School, National University of Singapore, Singapore and

School of Management, University of South Australia, Adelaide, Australia

Abstract

Purpose – The objective of this paper is to develop an integrated model for performance management(PM) of manufacturing industries.

Design/methodology/approach – The proposed integrated model consists of performancemeasurement by the extended Brown Gibson model by considering the objective and the servicequality factors. The quality factor measure has been evaluated by using the analytic hierarchyprocess. On the non-compliance of the performance measures with the satisfactory levels, qualityfunction deployment is used to redesign the existing manufacturing process.

Findings – This study provides a way to identify the current performance of an organization and amethodology for further improvement. An important contribution of this model is that it combinesboth the qualitative and quantitative dimensions of manufacturing performance measurement. Boththe objective and manufacturing quality factors have been converted into consistent dimensionlessindices to measure system performance.

Practical implications – This study has demonstrated the applicability of the model to support amanufacturing unit. It has shown how performance measures have been identified and how they can beused to calculate the two different manufacturing units using time, cost and service quality dimensions.Improving performance is a never-ending process and organizations should strive to achieve it to attainthe optimal level of cost and profit, as well as increase customer satisfaction and goodwill, and gainpotential future business. Hence, the process of measuring and redesigning manufacturing performancemeasures needs to be monitored and the implementation plans reviewed often, which is successfullydone by this integrated model.

Originality/value – We contend that the integrated model for PM, illustrated with a practical case inthis paper makes a contribution to the never-ending process of performance enhancement for boththeory and application, and assists in expanding the boundaries of theory and practicality in this area,thus highlighting the novelty of our approach.

Keywords Analytical hierarchy process, House of quality, Quality function deployment,Performance management

Paper type Research paper

1. IntroductionGlobalisation and liberalisation create a need for having a manufacturing managementsystem to estimate the performance of the manufacturing industry. Amaratunga andBaldry (2002) define performance management (PM) as the use of performancemeasurement information to effect a positive change in the organizational culture,

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An integratedmodel for PM

261

Benchmarking: An InternationalJournal

Vol. 18 No. 2, 2011pp. 261-281

q Emerald Group Publishing Limited1463-5771

DOI 10.1108/14635771111121702

systems and processes, by helping to set agreed-upon performance goals, allocating andprioritizing resources, informing managers to either confirm or change current policy orprograms direction to meet these goals and sharing the results of performancein pursuing these goals. PM both precedes and follows performance measurement.The effective conduct of PM is generally divided into two stages:

(1) performance measurement; and

(2) performance improvement.

Performance measurement is recognized as an important part of the manufacturingstrategy literature. It is a process of quantifying actions, where measurement is theprocess of quantification and action correlates with performance. The definitions ofthree important terms in the context of performance measurement as given by Neely et al.(1995) are the process of quantifying the efficiency and effectiveness of action.A performance measurement system can be examined at three different levels: theindividual performance measures, the performance measurement system as an entity,the relationship between the performance measurement system and the environment inwhich it operates. The key to the evaluation of performance measurement in our viewhas to be based first and foremost on identifying the function of the performancemeasurement system; and this, again, depends largely on the organizational context andthe organizational culture management intent and strategy. Thus, performancemeasurement sets the agenda for bringing the more relevant-, integrated-, balanced-,strategic- and improvement-oriented PM (Tangen, 2004).

Performance improvement is the positive change which is brought about by processre-engineering, reflecting the concern of the customer. Process improvement identifiesthe redundant and missing performance measures, as well as identifies potentialconflicts between performance measures and targets for each performance measure.Quality function deployment (QFD) has been used to improve the manufacturingprocess improvement. This technique converts the “voice of the customer” into design,engineering, manufacturing and production terms to ensure the product meets the needsof the customer. Therefore, it is an effective tool to integrate marketing strategy withproduct development process. The QFD procedure uses a series of matrices called houseof quality (HOQ), to express the linkages between the inputs and outputs of the differentphases of development (Hauser and Clausing, 1988). The HOQ typically containsinformation on the “what to do” (customer requirements), “how to do”(engineering characteristics), relation measures between customer requirements andengineering characteristic’s, as well as the correlation measures among the engineeringcharacteristics and benchmarking data compared to competitors (Tan et al., 2004;Tang et al., 2002). In the redesign process, the HOQ needs to be developed and theoptimal set of service requirements needs to be determined.

Thus, the objective of this paper is to demonstrate the need for the manufacturingPM model, integrating performance measurement and performance improvement.Moreover, it explores the relationship between the measuring variables considering theobjective and service quality factors, which are based not only on costumer focus butalso on the technical parameters based on the operational system.

The paper is organized as follows. A brief theoretical background is presented onthe performance measurement framework, and performance measuring tools are

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discussed followed by the problem identification and research methodology. A casestudy of two manufacturing units to illustrate the applicability of the model follows.

2. Theoretical backgroundThe following reviews serve as the background within which measurement ofmanufacturing performance is undertaken and the subsequent development of theintegrated model is proposed. Next, the framework and dimensions on PM are presented.

2.1 Review on performance measurement frameworkPerformance measurement has not received much attention from researchers andpractitioners, however, organizational performance has always exerted considerableinfluence on the working and decisive actions of companies. Garvin (1993) coined a phrasein theHarvardBusinessReview that has become paradigmatic for this view: “If you cannotmeasure it, you cannot manage it”. Thus, the ways and means of accurately measuring theperformance is perceived as being an increasingly important field of research for bothorganizations and academics alike. Indeed, in the past 15 years, performance measurementhas been seen to make its mark, reflecting its importance in an increasing number of fields(Rouse and Putterill, 2003). The mid to late 1990s seem to have seen the peak of thisactivity. Consequently, numerous frameworks on performance measurement have beendeveloped in many fields (Yeniyurt, 2003). One of the first frameworks put forward for theprocess of performance measurement was by Sink and Tuttle (1989), it describes a six-stepprocedure for performance measurement in the planning phase: effectiveness, efficiency,and quality, and productivity, quality of work life, innovation and profitability/budgetability. Keegan et al. (1989) developed a model which presented the structural performancemeasurement matrix that examined external/internal and cost/non-cost performancemeasures, while the results and determinants framework proposed by Fitzgerald et al.(1991) described the financial performance competitiveness, quality, flexibility, resourceutilization, and innovation as the determinants. Lockamy (1998) has proposed fourtheoretical performance measurement system models for the dimensions of cost, quality,lead time, and delivery based on research into the linkages between the operational andstrategic PM systems in a small number of world-class manufacturing companies. Lynchand Cross (1991) proposed the structural performance pyramid, which highlights ahierarchical view of business performance measurement, and a ten-step procedural modelencompassing vision, market, financial, customer satisfaction, flexibility, productivity,quality, delivery, cycle time, and waste to describe what needs to be done in terms of PM.Both Kaydos (1991) and Wisner and Fawcett (1991) have proposed procedural stepwiseframework models, while the structural-balanced scorecard attempts to introduce theconcept of producing a “balanced” set of measures (i.e. non-financial “balanced” againstfinancial measures). Berrach and Cliville (2007) proposed building performancemeasurement systems by linking an overall performance expression to elementaryones. As global frameworks, the analytic hierarchy process (AHP) or MACBETHmethodologies were suggested. Neely et al. (1997) emphasized the role of performancemeasurement and matrices in setting objectives for evaluating performance, anddetermining future courses of action. They further suggested that “time” can be used as astrategic metric in PM. Folan and Browne (2005) described the evolution of theperformance measurement in four sections: recommendations, frameworks, systems,and inter-organizational performance measurement. A holistic analysis with cost,


263

time, and service quality as the coherent dimensions of performance measure is rarelyfound in the literature.

The next section discusses the literature on the various tools namely AHP, QFD andthe extended Brown-Gibson (EBG) model used in the paper.

2.2 Review on AHPAHP, developed by Saaty (1980), is a decision-making tool that can help to describe thegeneral decision operation by decomposing a complex problem into a multi-levelhierarchical structure of objectives, criteria, sub-criteria, and alternatives. The wideapplicability is due to its simplicity, ease of use and great flexibility. AHP consists ofthree main operations, including hierarchy construction, priority analysis, andconsistency verification. AHP has been designed for situations in which ideas, feelingsand emotions are quantified based on subjective judgement to provide a numeric scalefor prioritizing decision alternatives. The usability of AHP in solving multiple criteriaproblems can be appreciated with its diverse applications in various fields (Vaidya andKumar, 2006). Some applications include education systems (Lam and Zhao, 1998),quality control systems (Badri, 2001), plant layout design (Yang and Kuo, 2003), flexiblemanufacturing systems (Aravindan and Punniyamoorthy, 2002; Punniyamoorthy andRagavan, 2003), material planning and control systems (Razmi et al., 2006); modellingsupply chains (Gunasekaran et al., 2001), manufacturing systems (Harker, 1987),activity-based costing (Schniederjans and Garvin, 1997), and hospitals (Lee and Kwak,1999). More researchers are realizing that AHP is an important generic method and areapplying it to various manufacturing areas (Andijani and Anwarul, 1997). Ghodsypourand O’Brien (1998) adopted the AHP to determine the relative importance weights of thesuppliers with respect to three criteria: cost, quality, and service. Saaty (2003) studied theresource allocation problem in two merging companies.

2.3 EBG modelThe Brown-Gibson model (Brown and Gibson, 1972) was developed for evaluatingalternative plant locations using certain objective and subjective factors. Thisquantitative model helped in selecting the best location from a given set of alternatives.

Aravindan and Punniyamoorthy (2002) and Punniyamoorthy and Ragavan (2003)used the modified Brown-Gibson model for the justification of technology selection in amanufacturing system. This EBG model has been used to assist in the strategicdecision-making process considering both objective and subjective factors influencing thedecision and addresses both time and cost dimensions in its objective factor measure (OFM).

2.4 Quality deployment functionQFD is an overall concept that provides a means of translating customer requirementsinto the appropriate technical requirements for each stage of product development andproduction (i.e. marketing strategies, planning, product design and engineering,prototype evaluation, production process development, production, and sales).

QFD has been used since the early 1970s with the purpose of making the productdevelopment process more efficient (Govers, 2000). QFD may be used as a means fordeveloping new products and to modify existing products (Nilsson, 1990). According toBergman and Klevsjo (1994), the aim of QFD is to transfer the wants and needs of thecustomers into product and process characteristics by systematically letting the wishes

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be reflected at every level of the product development process. As a means ofidentifying the customer needs, questionnaire surveys are suggested. The importanceof the different product characteristics also are analysed and ranked, and the mostimportant characteristic receives the highest ranking. This ranking often is carried outby the company staff but should be made by actual customers. Further, the rankingscales are insufficient due to the fact that the importance of the product characteristicsis estimated individually. The drawback using this method is that the product is notjudged as a whole. In the QFD analysis, a matrix is used which is called the HOQ,where the analysis is carried out in a number of steps.

3. Problem identificationVarious authors have through their papers explored the application of AHP-QFDcombined tools in a variety of fields that range from education to sports. Koksal andEgitman (1998) applied the combined AHP-QFD approach to improve the educationquality for a Middle East Technical University. Lam and Zhao (1998) used the combinedAHP-QFD approach to identify appropriate teaching techniques. The AHP was used toevaluate the relative importance weightings of the students’ requirements with respectto three criteria: skills development, interest and knowledge, and examination and job.Wang et al. (1998) suggested that the HOQ can be represented as a hierarchy if AHP wasused together with QFD. The customer requirements and technical/design requirementsin QFD can therefore be regarded as the criteria and alternatives in the AHP,respectively. Partovi (1999) applied the combined AHP-QFD approach to aid in projectselection. Partovi and Epperly (1999) used the combined AHP-QFD approach todetermine the composition of the US peacekeeping force deployed in Bosnia. Zakarianand Kusiak (1999) evaluated and selected the multi-functional teams using the combinedAHP-QFD approach. Badri (1999) and Chuang (2001) applied the combined AHP-QFDapproach in the facility location problem. Kwong and Bai (2003) used the combinedAHP-QFD approach to aid in new product development. Lu et al. (1994) applied thecombined AHP-QFD approach to evaluate and select the functional characteristics ofenvironmentally friendly products. Partovi and Corredoira (2002) used the combinedAHP-QFD approach to prioritize and design rule changes for the game of soccer.The objective was to increase the attractiveness to soccer enthusiasts. Myint (2003)proposed the combined AHP-QFD approach to aid in product design. Bhattacharya et al.(2005) applied the combined AHP-QFD approach to aid in robot selection. Partovi (2006)used the combined AHP-QFD approach to evaluate and select facility location for acompany producing digital mass measurement weighted products for industrial use.Hanumaiah et al. (2006) presented the combined AHP-QFD approach to deal with therapid tooling process selection.

Our literature review shows that there is a research gap in the application of theintegrated AHP-QFD tool in the PM of the manufacturing sector.

With this background, paper intends to exploit this unexplored area by using this toolto create an integrated closed loop model for PM in the manufacturing sector. Theresearch seeks to devise an integrated methodology to evaluate and analyse the PM ofmanufacturing organizations through the process of performance measurement aidedby multi-criterion decision-making tools such as AHP and EBG and the subsequentimprovement of the performance measures using QFD by constructing a suitableHOQ matrix. The EBG model has been used to quantify the performance considering


265

the objective and service quality factors. These factors are further evaluated by AHPrequired for the EBG model. Whenever required, QFD has been used to redesign theexisting processes. The developed model is further exemplified using a case study ontwo similar manufacturing units.

4. Research methodThe proposed model for PM in manufacturing organizations consists of two phases,the fundamental idea is to first measure the performance and if required set a strategyfor performance improvement. The detailed discussion on the methodology is listed ina subsequent section.

Phase 1. This phase is related to the manufacturing performance measurement. Thiswill involve identifying the parameters and classifying them into objective and qualityfactors. The objective factors include the cost and time dimensions which can be furtherclassified into effective or ineffective. A structured survey will then be conducted at theorganizations included in the study. An OFM and quality measure will then be calculated.The EBG model is used to quantify the manufacturing performance measure. AHP is usedto evaluate the manufacturing quality factor measure (QFM) required for the EBG model.

Phase 2. This phase is related to the manufacturing performance improvement.In this phase, QFD is used to improve manufacturing performance. The basic steps formanufacturing performance improvement include:

(1) Identifying the customer requirements.

(2) Identifying the manufacturing design requirements (MDRs).

(3) Relating customer requirements to MDRs.

(4) Conducting an evaluation of competing manufacturers.

(5) Evaluating the MDRs and development of targets.

The processes that have been redesigned need to be implemented in the organizationand need to be periodically reviewed so that the overall improvement of theperformance quality of the organization can take place. The flowchart provided inFigure 1 illustrates the process.

4.1 Manufacturing performance measurementStep 1. The organizations to be studied are decided and the various parameters thatinfluence the performance are identified in this stage. Fundamentally, these are thevariables required to measure the performance of an organization. Gomes et al. (2004)have identified about 65 parameters or measures classified under the following groups:

. financial;

. product quality and customer satisfaction;

. process efficiency;

. product and process innovation;

. competitive environment;

. quality/independence of management;

. human resource management; and

. social responsibility.

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Figure 1.Decision-making process

Classification of the above parameters into:••

Objective factors: concern cost and timeQuality factors: concern customer satisfaction

Is the abovevalue

satisfactory?

Identification of characteristics to meet the new customerrequirements

Preparing a questionnaire incorporating the above factors andconducting a survey in the organization

Identification of manufacturing performance parametersbased on the organization under study

1. Objective factor measure is calculated using the parameters involving cost and time2. Evaluation of quality factors using AHP

Calculation of the system performance measure using EBG

Develop HOQ for the above

Determination, development and deployment of the optimumrequirements and strategies for process redesign

Review of implementation plan

NO

YES

PI

PM


267

Some of the important performance measures that can be used are:. operating cost per employee;. cost of goods sold per inventory;. product development time;. rejection ratio;. actual production per planned production;. capacity utilization;. number of new products (past three years);. percent of products protected by patents;. customer surveys;. customer complaints;. service responsiveness; and. percent of returned orders.

These parameters are the performance indicators of a manufacturing organization andcan be effectively used for PM.

Step 2. The parameters that are identified for the purpose of performancemeasurement are classified into objective and quality factors. These parametersdepend on the overall operational structure of the manufacturing organization.

4.1.1 Objective factors measurement. These factors measure an organization’sperformance in terms of cost and time. The cost factors can be classified as effectiveand ineffective cost. Similarly, the time factors can also be classified as effective andineffective time factors. The explanations of cost and time factors are given below:

. Effective cost (EC). It involves the costs that need to be maximised in order toimprove the performance, e.g. actual production vs planned production.

. Ineffective cost (IEC). It involves the costs that need to be minimised in order toimprove the performance, e.g. operating cost per employee.

. Effective time (ET). All the productive time that goes into improving theperformance of an organization is known as effective time, e.g. productdevelopment time.

. Ineffective time (IET). All the non-productive time is known as ineffective time,e.g. age of plant and equipment.

4.1.2 Quality factors measurement. These are the factors that pertain to the quality ofthe manufactured products and influence the performance of the organization. Thesefactors are also identified by having discussions with certain groups in the organizationinvolved in the process. These factors are then provided ratings on a fixed scale inrelation to the customer’s perspective and the group’s response.

Step 3. A structured survey is then conducted at the organization by preparing asuitable questionnaire for the purpose of incorporating the various parameters identifiedin terms of the respective factors.

Step 4. After getting the required data from the organizations, the OFM is calculated.It is obtained in terms of cost and time effectiveness (CTE). The measure of effectiveness

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is tangible and can be measured in terms of cost and time. The OFM of the organizationis calculated using the following equation:

OFM ¼ CTEi ·1Pm

i¼1CTEi

ð1Þ

The summation in equation (1) ranges from 1 to m, where m is the number ofcompetitors.

The CTE of competitor i is obtained from:

CTEi ¼ ECi ·1Pm

i¼1ECi

þ IECi ·1Pm

i¼1IECi

� �21

þETi ·1Pm

i¼1ETi

þ IETi ·1Pm

i¼1IETi

� �21

ð2Þ

where:

m ¼ the number of competitors.

ECi ¼ the effective cost of competitor i.

IECi ¼ the ineffective cost of competitor i.

ETi ¼ the effective time of competitor i.

IETi ¼ the ineffective time of competitor i.

Thus, the OFM is determined by equation (2).Step 5. In this step, the evaluation of the manufacturing QFM takes place. It is

done through AHP. The following steps are involved in the determination of QFMthrough AHP:

(1) Identify the manufacturing QFM that influence decision making in theorganization and have an effect on performance.

(2) Group the service quality factors based on their interdependence, as criteria,sub-criteria and sub-sub-criteria.

(3) Formulate a hierarchical structure, i.e. the objective function is arranged in thetop-level criteria, with sub-criteria and sub-sub-criteria in the intermediate leveland alternatives at the lower levels for constructing a pairwise comparisonmatrix for each level.

(4) Construct a pairwise comparison matrix A for each level. In this matrix, valuesranging from 1 to 9 and their reciprocal values are assigned. The factors in arow are compared with the factors in a column and the comparison value isgiven in the intersecting cell. When the factor in a row is stronger (moresignificant) than the factor in a column, then the crossing cell is strong and itscorresponding cell, which compares the latter with the former, takes a reciprocalvalue and is weak. The service managers of the organizations are involved inevaluating the criteria and the sub-criteria. Saaty’s (1980) nine-point scale isused for pairwise comparison:


269

. 1 – equally preferred;

. 2 – equally to moderately preferred;

. 3 – moderately preferred;

. 4 – moderately to strongly preferred;

. 5 – equally preferred;

. 6 – strongly to very strongly preferred;

. 7 – very strongly preferred;

. 8 – very to extremely strongly preferred; and

. 9 – extremely preferred.

(5) Determine the maximum eigenvalue (lmax) and its corresponding eigenvectorusing the following equation:

A £ W ¼ lmax £ W ð3Þ

where:

A ¼ observed matrix of pairwise comparison.

lmax ¼ largest eigenvalue of A.

W ¼ principal eigenvector (a measure of relative importance weight –age of the criteria or sub-criteria or the alternative).

(6) Determine the consistency ratio (CR), the ratio between consistency index andthe random index using the following equation:

CR ¼CI

RI¼

lmax 2 n

n 2 1ð4Þ

where:

CI ¼ consistency index of A.

RI ¼ random index of A.

n ¼ order of matrix A.

The random index value corresponding to n is determined from Table I.When the CR is less than 0.10, the matrix is accepted as consistent.Another comparison matrix B is constructed by comparing the alternatives with

respect to each of the factors at the lowest level of the hierarchy. Using the surveyresults from the customers, matrix B is found as per Step 4. Steps 5 and 6 are carriedout again in order to check the consistency of matrix B. The service factor measure(SFM) measure is calculated using matrices A and B. The SFM for competitor i withrespect to j service quality factors is evaluated through the following equation:

Order of matrix 1 2 3 4 5 6 7 8 9 10

Random index 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

Table I.Random indexcalculation

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SFMi ¼X

ðlocal weight of competitor w:r:t: criterion j from matrix BÞ

£ ðlocal weight of criterion j from matrix AÞ:ð5Þ

Step 6. The system performance measure (SPM) is calculated in this step. For thecalculation, the EBG approach is used. In the EBG approach, both the objective andquality factors found in the above steps are converted into consistent and dimensionlessindices to measure the SPM. The SPM of competitor i is found from equation (6):

SPMi ¼ aðOFMiÞ þ ð1 2 aÞSFMi ð6Þ

where, 0 , a , 1, a ¼ the objective factor weight and 1–a ¼ the quality factorweight.

Step 7. The SPM is analysed in this step and the decisions about the manufacturingdesign process is made. When the evaluated SPM value is found to be satisfactory,then the organization can strive for the perfection in the quality of the services offered.The measurement process needs to be continuously repeated and further improvementopportunities need to be analysed. When the evaluated SPM value falls below asatisfactory level, the design of the process itself is faulty and the redesigning of theentire process becomes essential.

4.2 Manufacturing performance improvementIn the second phase, QFD is used to improve the service performance. The QFDprocedure uses a series of HOQ matrices to express the linkages between the inputs andoutputs of the different phases of development (Hauser and Clausing, 1988). In theredesigning process, the HOQ needs to be developed and the optimum set of servicerequirements needs to be determined. Building the first HOQ consists of five basic steps:

(1) Identifying the customer requirements.

(2) Identifying the service design requirements.

(3) Relating the customer requirements to the service design requirements.

(4) Conducting an evaluation of competing service providers.

(5) Evaluating the service design requirements and development of targets.

Thus, new strategies have to be deployed and the implementation plans have to bereviewed periodically.

5. Model validation by case studyTo demonstrate the applicability of the model in manufacturing organizations, we relyon a case study approach on two identical valve manufacturing companies, namely,Unit A and Unit B, located at the Tiruchirappalli Regional Engineering College Scienceand Technology Entrepreneurs Park in Tiruchirappalli, India. The details of the studyare provided below.

5.1 Manufacturing performance measurementStep 1. The performance measures that influence the performance of the two companiesare identified through discussions with the company managers. About 15 factors were


271

shortlisted and identified. These factors are grouped according to their respectivecategories as:

(1) Process efficiency:

. operating cost per employee;

. cost of goods sold;

. product development time;

. rejection ratio;

. actual production against planned production;

. age of plant and equipment; and

. capacity utilization.

(2) Product and process innovation:

. R&D expenditure;

. number of new products in the last three years; and

. percent of products protected by patents.

(3) Product quality and customer satisfaction:

. customer surveys and warranty claims;

. customer complaints;

. service responsiveness; and

. percent of returned orders.

Step 2. The performance measures were classified into objective and quality factors asfollows.

Objective factors:

(1) Effective cost:

. cost of goods sold;

. actual production against planned production;

. R&D expenditure; and

. capacity utilization.

(2) Ineffective cost:

. operating cost per employee;

. rejection ratio; and

. age of plant and equipment.

Step 3. A structured survey was conducted at the two organizations, Unit A and Unit Busing the sample questionnaire as shown in the methodology.

Step 4. The OFM was now calculated in terms of CTE, from the structured surveyconducted in Unit A and Unit B. The data from both manufacturing units are shown inTables II and III.

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The above data were collected and calculated from the above questionnaire:

Cost Time Effectiveness ðCTEÞA ¼58; 168

58; 168 þ 52; 800

� �

þ825

{ð1=1; 071Þ þ ð1=825Þ}

� �21

þ90

90 þ 120

� �

þ4:5

{ð1=4:5Þ þ ð1=8:4Þ}

� �21

¼ 0:52142 þ 0:000003 þ 0:42857 þ 0:07584¼ 1:02583

Cost Time Effectiveness ðCTEÞB ¼52; 800

58; 168 þ 52; 800

� �

þ1; 071

{ð1=1; 071Þ þ ð1=825Þ}

� �21

þ120

90 þ 120

� �

þ8:4

{ð1=4:5Þ þ ð1=8:4Þ}

� �21

¼ 0:47581 þ 0:000002 þ 0:57143 þ 0:04063¼ 1:0879

Objective Factor Measure ðOFMÞA ¼1:02583

1:02583 þ 1:08789

� �¼ 0:48532

Objective Factor Measure ðOFMÞB ¼1:08789

1:02583 þ 1:08789

� �¼ 0:51468

Step 5. Prioritization using AHPThe criteria are prioritized in this step. The priorities are set by comparing each set

of elements pairwise with respect to each of the elements on a higher level (Table IV).This yields lmax ¼ 1:3431 þ 1:0485 þ 0:6298 þ 0:4294 þ 0:3168 þ 2:4720 þ

2:8186 þ 0:2142 ¼ 9:2724

Parameter Cost of unit A Cost of unit B

Average effective cost 58,168 52,800Average ineffective cost 825 1,071

Table II.Cost data from

manufacturing units

Parameter Time of unit A (days) Time of unit B (days)

Average effective time 90 120Average ineffective time 4.5 8.4

Table III.Time data


273

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214

Table IV.AHP matrix and itseigenvalues for variouscriteria

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Order of the Matrix A ðnÞ ¼ 8:

Consistency Index ðCIÞ ¼9:2724 2 8

8 2 1¼ 0:181

Consistency Ratio ðCRÞ ¼CI

RI¼

0:181

1:41¼ 0:12

As per the AHP process, if the CR is less than or equal to 10 percent or 0.1, the matrix isdeemed consistent. The QFM is found from the principal eigenvector of the comparisonmatrix A and individual factor comparison matrix B. The calculations are shown below:

QFMA ¼ ð0:1395 £ 0:16Þ þ ð0:1094 £ 0:16Þ þ ð0:0724 £ 0:125Þ þ ð0:0505 £ 0:25Þ

þ ð0:0386 £ 0:25Þ þ ð0:2687 £ 0:125Þ þ ð0:2945 £ 0:75Þ

þ ð0:0252 £ 0:125Þ ¼ 0:329

QFMB ¼ ð0:1395 £ 0:84Þ þ ð0:1094 £ 0:84Þ þ ð0:0724 £ 0:875Þ þ ð0:0505 £ 0:75Þ

þ ð0:0386 £ 0:75Þ þ ð0:2687 £ 0:875Þ þ ð0:2945 £ 0:25Þ

þ ð0:0252 £ 0:875Þ ¼ 0:67

Step 6. For a manufacturing industry, the value of a is taken as 0.4 because moreimportance is given to the QFM than to the OFM. Using the SPM equation, the serviceSPM for both units is calculated as:

SPMA ¼ ð0:4 * 0:48532Þ þ ð1 2 0:4Þ * 0:3290 ¼ 0:391

SPMB ¼ ð0:4 * 0:51468Þ þ ð1 2 0:4Þ * 0:67 ¼ 0:608

Using the EBG model, the performance of the manufacturing unit is measured.From SPM, the performance of Unit A is lowered than Unit B (Table V). Hence, toimprove the performance of Unit A, the manufacturing parameter has to be redesigned.

5.2 Manufacturing performance improvementFrom the system performance measurement values, we conclude that Unit A which has alower SPM value needs to be improved. QFD has been employed to facilitate this process.This is useful in establishing the priority of actions within the overall re-engineeringstrategy. A cascade of charts can be created dealing with the manufacturing processhierarchy. In this way, all manufacturing design processes at whatever level may betraced back to the customer and the effect of changes at any level in the performancechecked against the overall company strategy. Thus, QFD has the voice of the customerin the manufacturing design improvements.

Building of the first HOQ for Unit A consists of the following steps:

(1) Identifying the customer requirements. The basic model of HOQ incorporates thecustomer requirements. This has already been ascertained in the EBG model.From the absolute weight column in the AHP matrix, it is very clear that theprioritized customer requirements are in the following order: process efficiency,work done right the first time, timely delivery, modern equipments, employeemorale, return on sales, cost of goods sold per inventory and warranty claims.


275

(2) Identifying the MDRs. The QFD team identifies the MDRs that are most neededto fulfil the customer requirements. The MDRs identified are as follows: qualitycharacteristic, technical expertise development, strategic investment policy,correct and thorough workmanship development, job scheduling, clarificationwith the customer on the work to be done, an environment of attitudinal change,rewards and recognition rules.

(3) Relating customer requirements to MDRs. The customer requirements are relatedto MDRs through the central matrix construction. The central matrix provides thedegree of influence between each of the MDRs and the customer requirements.The degree of the relationship has been identified and tabulated in Table VI.

(4) Conducting an evaluation of competing manufacturers. The customercompetitive assessment in the HOQ provides a good way to determinewhether the customer requirements have been met. It also indicates areas to beconcentrated upon during the next design review. It contains an appraisal ofwhere an organization stands relative to its major competitors in terms of eachrequirement. The assessment values are obtained from the EBG model.

(5) To meet the customer requirements, the manufacturing organization has toprioritize the MDRs and fix the targets for each MDRs.

After developing the HOQ for Unit A, we obtain the relative absolute weights of thevarious design requirements with respect to the customer requirements. Thus, the designrequirements are prioritized according to their absolute weights in the HOQ matrix.

Quality parameters Unit A Unit B Eigenvector

Timely deliveryUnit A 1 1/5 0.16Unit B 5 1 0.83Employee moraleUnit A 1 1/5 0.16Unit B 5 1 0.83Modern equipmentUnit A 1 1/7 0.125Unit B 7 1 0.875Return on salesUnit A 1 1/3 0.25Unit B 3 1 0.75Cost of goods sold per inventoryUnit A 1 1/3 0.25Unit B 3 1 0.75Work done right the first timeUnit A 1 1/7 0.125Unit B 7 1 0.875Process efficiencyUnit A 1 3 0.75Unit B 1/3 1 0.25Warranty claimsUnit A 1 1/7 0.125Unit B 7 1 0.88

Table V.Comparison matrix

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276

Qu

alit

ych

arac

teri

stic

s

Tec

hn

ical

exp

erti

sed

evel

opm

ent

Str

ateg

icin

ves

tmen

tp

olic

y

Cor

rect

and

thor

oug

hw

ork

man

ship

Job

sch

edu

lin

g

Cla

rifi

cati

onw

ith

the

cust

omer

onth

ew

ork

tob

ed

one

An

env

iron

men

tof

atti

tud

inal

chan

ge

Rew

ard

san

dre

cog

nit

ion

rule

s

Tim

ely

del

iver

yW

††

††

††

0.83

0.13

9M

oder

neq

uip

men

t†

†W

0.83

0.10

9E

mp

loy

eem

oral

eW

W†

††

0.87

50.

724

Ret

urn

onsa

les

††

W†

0.75

0.05

05C

ost

ofg

ood

sso

ld/

inv

ento

ryW

††

0.75

0.03

86W

ork

don

eri

gh

tth

efi

rst

tim

eW

††

WD

D0.

880.

2687

Pro

cess

effi

cien

cy†

WD

D†

0.25

0.29

45W

arra

nty

clai

ms

DD

0.87

50.

0252

Ab

solu

tew

eig

hts

5.67

4.77

2.07

4.75

4.3

1.52

2.17

2.63

Tar

get

sP

roce

ssre

liab

ilit

yan

dim

pro

vem

ent

On

etr

ain

ing

intw

om

onth

s

Pol

icy

rev

iew

once

intw

om

onth

s

Con

tin

uou

str

ain

ing

Bet

ter

oper

atio

ns

man

agem

ent

Org

aniz

atio

nal

acce

ssib

ilit

yR

egu

lar

mot

ivat

ion

alp

rog

ram

s

Ince

nti

ves

and

bon

us

sch

emes

Tar

get

val

ue

Ab

solu

tew

eig

ht

from

ahp

mat

rix

(w)

Notes:W

–M

oder

atel

yre

late

d(t

hre

ep

oin

ts);†

–st

ron

gly

rela

ted

(nin

ep

oin

ts);D

–w

eak

lyre

late

d(o

ne

poi

nt)

Table VI.House of quality


277

Further, in accordance with these prioritized MDRs, the targets to meet these MDRs havebeen identified and employed by the QFD team. The prioritized orders of the MDRs andthe targets to achieve are listed below.

The priority orders of the MDRs are: quality characteristics, technical expertisedevelopment, correct and thorough workmanship, job scheduling, rewards andrecognition rules, an environment for attitudinal change, strategic investment policy,and clarification with the customer on the work to be done. The targets are as follows:

. Quality characteristics – process reliability improvement.

. Technical expertise development – training once in two months.

. Correct and thorough workmanship – continuous training.

. Job scheduling – better operations management.

. Rewards and recognition rules – company policy of bonuses and incentives.

. An environment of attitudinal change – motivational programs every month.

. Strategy investment policy – policy review once in two months.

. Clarification with the customer on the work to be done – organizationalaccessibility.

6. ConclusionThis paper demonstrates the need for a manufacturing PM model. A literature survey hasbeen carried out on the various frameworks and tools used. An integrated closed model toenhance PM has been proposed. It also provides a means to identifying the currentperformance of an organization and a methodology to improve it further. An importantcontribution of this model is that it combines both qualitative and quantitativedimensions of manufacturing performance measurement. The proposed model providesan opportunity to operationalise the relationship among the cost, time, and servicequality dimensions. Both objective and manufacturing quality factors have beenconverted into consistent dimensionless indices to measure the system performance.

The case study presented in this paper has demonstrated the applicability of themodel to support a manufacturing unit. It has shown how performance measures areidentified and how they can be calculated for two different units using time, cost, andservice quality dimensions. The case study proves the usability of the EBG model forthe PM process. From the SPM, the performance of the manufacturing organizations isanalysed and manufacturing parameters is redesigned using QFD wherever necessary.

Improving performance is a never-ending process and organizations should striveto achieve it for attaining the optimal level of cost and profit, as well as increasecustomer satisfaction and goodwill, and gain potential future business. Hence, theprocess of measuring and redesigning the manufacturing performance measures needsto be monitored and the implementation plans reviewed often. Finally, this modelfor PM can be extended to the service sector.

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Further reading

Madu, C.N. (2000), House of Quality in a Minute, CHI, Fairfield, CT.

Tyagi, R. and Das, C. (1997), “A methodology for cost versus service tradeoffs in whole salelocation-distribution using mathematical programming and analytic hierarchy process”,Journal of Business Logistics, Vol. 18 No. 2, pp. 77-99.

Corresponding authorMark Goh can be contacted at: [email protected]

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