1) The document proposes an integrated model for performance management of manufacturing units that combines performance measurement and improvement. 2) Performance is measured using an extended Brown-Gibson model considering objective and service quality factors evaluated through analytic hierarchy process. 3) If performance measures do not meet satisfactory levels, quality function deployment is used to redesign manufacturing processes.

1. The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
An integrated
An integrated model model for PM
for performance management
of manufacturing units
261
P. Parthiban
Department of Production Engineering, National Institute of Technology,
Tiruchirappalli, India, and
Mark Goh
NUS 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 performance
measurement by the extended Brown Gibson model by considering the objective and the service
quality factors. The quality factor measure has been evaluated by using the analytic hierarchy
process. On the non-compliance of the performance measures with the satisfactory levels, quality
function deployment is used to redesign the existing manufacturing process.
Findings – This study provides a way to identify the current performance of an organization and a
methodology for further improvement. An important contribution of this model is that it combines
both the qualitative and quantitative dimensions of manufacturing performance measurement. Both
the objective and manufacturing quality factors have been converted into consistent dimensionless
indices to measure system performance.
Practical implications – This study has demonstrated the applicability of the model to support a
manufacturing unit. It has shown how performance measures have been identified and how they can be
used 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 attain
the optimal level of cost and profit, as well as increase customer satisfaction and goodwill, and gain
potential future business. Hence, the process of measuring and redesigning manufacturing performance
measures needs to be monitored and the implementation plans reviewed often, which is successfully
done by this integrated model.
Originality/value – We contend that the integrated model for PM, illustrated with a practical case in
this paper makes a contribution to the never-ending process of performance enhancement for both
theory 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. Introduction Benchmarking: An International
Globalisation and liberalisation create a need for having a manufacturing management Journal
Vol. 18 No. 2, 2011
system to estimate the performance of the manufacturing industry. Amaratunga and pp. 261-281
Baldry (2002) define performance management (PM) as the use of performance q Emerald Group Publishing Limited
1463-5771
measurement information to effect a positive change in the organizational culture, DOI 10.1108/14635771111121702

2. BIJ systems and processes, by helping to set agreed-upon performance goals, allocating and
18,2 prioritizing resources, informing managers to either confirm or change current policy or
programs direction to meet these goals and sharing the results of performance
in 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
262 (2) performance improvement.
Performance measurement is recognized as an important part of the manufacturing
strategy literature. It is a process of quantifying actions, where measurement is the
process of quantification and action correlates with performance. The definitions of
three 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: the
individual performance measures, the performance measurement system as an entity,
the relationship between the performance measurement system and the environment in
which it operates. The key to the evaluation of performance measurement in our view
has to be based first and foremost on identifying the function of the performance
measurement system; and this, again, depends largely on the organizational context and
the organizational culture management intent and strategy. Thus, performance
measurement 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 process
re-engineering, reflecting the concern of the customer. Process improvement identifies
the redundant and missing performance measures, as well as identifies potential
conflicts between performance measures and targets for each performance measure.
Quality function deployment (QFD) has been used to improve the manufacturing
process improvement. This technique converts the “voice of the customer” into design,
engineering, manufacturing and production terms to ensure the product meets the needs
of the customer. Therefore, it is an effective tool to integrate marketing strategy with
product development process. The QFD procedure uses a series of matrices called house
of quality (HOQ), to express the linkages between the inputs and outputs of the different
phases of development (Hauser and Clausing, 1988). The HOQ typically contains
information on the “what to do” (customer requirements), “how to do”
(engineering characteristics), relation measures between customer requirements and
engineering characteristic’s, as well as the correlation measures among the engineering
characteristics 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 the
optimal set of service requirements needs to be determined.
Thus, the objective of this paper is to demonstrate the need for the manufacturing
PM model, integrating performance measurement and performance improvement.
Moreover, it explores the relationship between the measuring variables considering the
objective and service quality factors, which are based not only on costumer focus but
also on the technical parameters based on the operational system.
The paper is organized as follows. A brief theoretical background is presented on
the performance measurement framework, and performance measuring tools are

3. discussed followed by the problem identification and research methodology. A case An integrated
study of two manufacturing units to illustrate the applicability of the model follows. model for PM
2. Theoretical background
The following reviews serve as the background within which measurement of
manufacturing performance is undertaken and the subsequent development of the
integrated model is proposed. Next, the framework and dimensions on PM are presented. 263
2.1 Review on performance measurement framework
Performance measurement has not received much attention from researchers and
practitioners, however, organizational performance has always exerted considerable
influence on the working and decisive actions of companies. Garvin (1993) coined a phrase
in the Harvard Business Review that has become paradigmatic for this view: “If you cannot
measure it, you cannot manage it”. Thus, the ways and means of accurately measuring the
performance is perceived as being an increasingly important field of research for both
organizations and academics alike. Indeed, in the past 15 years, performance measurement
has 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 this
activity. Consequently, numerous frameworks on performance measurement have been
developed in many fields (Yeniyurt, 2003). One of the first frameworks put forward for the
process of performance measurement was by Sink and Tuttle (1989), it describes a six-step
procedure for performance measurement in the planning phase: effectiveness, efficiency,
and quality, and productivity, quality of work life, innovation and profitability/budget
ability. Keegan et al. (1989) developed a model which presented the structural performance
measurement matrix that examined external/internal and cost/non-cost performance
measures, while the results and determinants framework proposed by Fitzgerald et al.
(1991) described the financial performance competitiveness, quality, flexibility, resource
utilization, and innovation as the determinants. Lockamy (1998) has proposed four
theoretical performance measurement system models for the dimensions of cost, quality,
lead time, and delivery based on research into the linkages between the operational and
strategic PM systems in a small number of world-class manufacturing companies. Lynch
and Cross (1991) proposed the structural performance pyramid, which highlights a
hierarchical view of business performance measurement, and a ten-step procedural model
encompassing 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 stepwise
framework models, while the structural-balanced scorecard attempts to introduce the
concept of producing a “balanced” set of measures (i.e. non-financial “balanced” against
financial measures). Berrach and Cliville (2007) proposed building performance
measurement systems by linking an overall performance expression to elementary
ones. As global frameworks, the analytic hierarchy process (AHP) or MACBETH
methodologies were suggested. Neely et al. (1997) emphasized the role of performance
measurement and matrices in setting objectives for evaluating performance, and
determining future courses of action. They further suggested that “time” can be used as a
strategic metric in PM. Folan and Browne (2005) described the evolution of the
performance measurement in four sections: recommendations, frameworks, systems,
and inter-organizational performance measurement. A holistic analysis with cost,

4. BIJ time, and service quality as the coherent dimensions of performance measure is rarely
18,2 found in the literature.
The next section discusses the literature on the various tools namely AHP, QFD and
the extended Brown-Gibson (EBG) model used in the paper.
2.2 Review on AHP
264 AHP, developed by Saaty (1980), is a decision-making tool that can help to describe the
general decision operation by decomposing a complex problem into a multi-level
hierarchical structure of objectives, criteria, sub-criteria, and alternatives. The wide
applicability is due to its simplicity, ease of use and great flexibility. AHP consists of
three main operations, including hierarchy construction, priority analysis, and
consistency verification. AHP has been designed for situations in which ideas, feelings
and emotions are quantified based on subjective judgement to provide a numeric scale
for prioritizing decision alternatives. The usability of AHP in solving multiple criteria
problems can be appreciated with its diverse applications in various fields (Vaidya and
Kumar, 2006). Some applications include education systems (Lam and Zhao, 1998),
quality control systems (Badri, 2001), plant layout design (Yang and Kuo, 2003), flexible
manufacturing systems (Aravindan and Punniyamoorthy, 2002; Punniyamoorthy and
Ragavan, 2003), material planning and control systems (Razmi et al., 2006); modelling
supply 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 are
applying it to various manufacturing areas (Andijani and Anwarul, 1997). Ghodsypour
and O’Brien (1998) adopted the AHP to determine the relative importance weights of the
suppliers with respect to three criteria: cost, quality, and service. Saaty (2003) studied the
resource allocation problem in two merging companies.
2.3 EBG model
The Brown-Gibson model (Brown and Gibson, 1972) was developed for evaluating
alternative plant locations using certain objective and subjective factors. This
quantitative 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 a
manufacturing system. This EBG model has been used to assist in the strategic
decision-making process considering both objective and subjective factors influencing the
decision and addresses both time and cost dimensions in its objective factor measure (OFM).
2.4 Quality deployment function
QFD is an overall concept that provides a means of translating customer requirements
into the appropriate technical requirements for each stage of product development and
production (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 product
development process more efficient (Govers, 2000). QFD may be used as a means for
developing new products and to modify existing products (Nilsson, 1990). According to
¨
Bergman and Klevsjo (1994), the aim of QFD is to transfer the wants and needs of the
customers into product and process characteristics by systematically letting the wishes

5. be reflected at every level of the product development process. As a means of An integrated
identifying the customer needs, questionnaire surveys are suggested. The importance model for PM
of the different product characteristics also are analysed and ranked, and the most
important characteristic receives the highest ranking. This ranking often is carried out
by the company staff but should be made by actual customers. Further, the ranking
scales are insufficient due to the fact that the importance of the product characteristics
is estimated individually. The drawback using this method is that the product is not 265
judged 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 identification
Various authors have through their papers explored the application of AHP-QFD
combined tools in a variety of fields that range from education to sports. Koksal and
Egitman (1998) applied the combined AHP-QFD approach to improve the education
quality for a Middle East Technical University. Lam and Zhao (1998) used the combined
AHP-QFD approach to identify appropriate teaching techniques. The AHP was used to
evaluate the relative importance weightings of the students’ requirements with respect
to 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 was
used together with QFD. The customer requirements and technical/design requirements
in 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 project
selection. Partovi and Epperly (1999) used the combined AHP-QFD approach to
determine the composition of the US peacekeeping force deployed in Bosnia. Zakarian
and Kusiak (1999) evaluated and selected the multi-functional teams using the combined
AHP-QFD approach. Badri (1999) and Chuang (2001) applied the combined AHP-QFD
approach in the facility location problem. Kwong and Bai (2003) used the combined
AHP-QFD approach to aid in new product development. Lu et al. (1994) applied the
combined AHP-QFD approach to evaluate and select the functional characteristics of
environmentally friendly products. Partovi and Corredoira (2002) used the combined
AHP-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 a
company producing digital mass measurement weighted products for industrial use.
Hanumaiah et al. (2006) presented the combined AHP-QFD approach to deal with the
rapid tooling process selection.
Our literature review shows that there is a research gap in the application of the
integrated AHP-QFD tool in the PM of the manufacturing sector.
With this background, paper intends to exploit this unexplored area by using this tool
to create an integrated closed loop model for PM in the manufacturing sector. The
research seeks to devise an integrated methodology to evaluate and analyse the PM of
manufacturing organizations through the process of performance measurement aided
by multi-criterion decision-making tools such as AHP and EBG and the subsequent
improvement of the performance measures using QFD by constructing a suitable
HOQ matrix. The EBG model has been used to quantify the performance considering

6. BIJ the objective and service quality factors. These factors are further evaluated by AHP
18,2 required for the EBG model. Whenever required, QFD has been used to redesign the
existing processes. The developed model is further exemplified using a case study on
two similar manufacturing units.
4. Research method
266 The 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 strategy
for performance improvement. The detailed discussion on the methodology is listed in
a subsequent section.
Phase 1. This phase is related to the manufacturing performance measurement. This
will involve identifying the parameters and classifying them into objective and quality
factors. The objective factors include the cost and time dimensions which can be further
classified into effective or ineffective. A structured survey will then be conducted at the
organizations 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 used
to 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 for
manufacturing 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 organization
and need to be periodically reviewed so that the overall improvement of the
performance quality of the organization can take place. The flowchart provided in
Figure 1 illustrates the process.
4.1 Manufacturing performance measurement
Step 1. The organizations to be studied are decided and the various parameters that
influence the performance are identified in this stage. Fundamentally, these are the
variables 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.

7. An integrated
Identification of manufacturing performance parameters model for PM
based on the organization under study
Classification of the above parameters into:
• Objective factors: concern cost and time
267
• Quality factors: concern customer satisfaction
PM
Preparing a questionnaire incorporating the above factors and
conducting a survey in the organization
1. Objective factor measure is calculated using the parameters
involving cost and time
2. Evaluation of quality factors using AHP
Calculation of the system performance measure using EBG
Is the above
value
YES satisfactory?
NO
Identification of characteristics to meet the new customer
requirements
PI
Develop HOQ for the above
Determination, development and deployment of the optimum
requirements and strategies for process redesign
Review of implementation plan Figure 1.
Decision-making process

8. BIJ Some of the important performance measures that can be used are:
18,2 .
operating cost per employee;
.
cost of goods sold per inventory;
.
product development time;
.
rejection ratio;
268 .
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 and
can be effectively used for PM.
Step 2. The parameters that are identified for the purpose of performance
measurement are classified into objective and quality factors. These parameters
depend on the overall operational structure of the manufacturing organization.
4.1.1 Objective factors measurement. These factors measure an organization’s
performance in terms of cost and time. The cost factors can be classified as effective
and ineffective cost. Similarly, the time factors can also be classified as effective and
ineffective 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 to
improve the performance, e.g. actual production vs planned production.
.
Ineffective cost (IEC). It involves the costs that need to be minimised in order to
improve the performance, e.g. operating cost per employee.
.
Effective time (ET). All the productive time that goes into improving the
performance of an organization is known as effective time, e.g. product
development 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 of
the manufactured products and influence the performance of the organization. These
factors are also identified by having discussions with certain groups in the organization
involved in the process. These factors are then provided ratings on a fixed scale in
relation to the customer’s perspective and the group’s response.
Step 3. A structured survey is then conducted at the organization by preparing a
suitable questionnaire for the purpose of incorporating the various parameters identified
in 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

9. is tangible and can be measured in terms of cost and time. The OFM of the organization An integrated
is calculated using the following equation: model for PM
1
OFM ¼ CTE i · Pm ð1Þ
i¼1 CTE i
The summation in equation (1) ranges from 1 to m, where m is the number of 269
competitors.
The CTE of competitor i is obtained from:
21
1 1 1
CTE i ¼ EC i · Pm þ IEC i · Pm þET i · Pm
i¼1 EC i i¼1 IEC i i¼1 ET i
21
1
þ IET i · Pm ð2Þ
i¼1 IET i
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 QFM
through AHP:
(1) Identify the manufacturing QFM that influence decision making in the
organization 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 the
top-level criteria, with sub-criteria and sub-sub-criteria in the intermediate level
and alternatives at the lower levels for constructing a pairwise comparison
matrix for each level.
(4) Construct a pairwise comparison matrix A for each level. In this matrix, values
ranging from 1 to 9 and their reciprocal values are assigned. The factors in a
row are compared with the factors in a column and the comparison value is
given in the intersecting cell. When the factor in a row is stronger (more
significant) than the factor in a column, then the crossing cell is strong and its
corresponding cell, which compares the latter with the former, takes a reciprocal
value and is weak. The service managers of the organizations are involved in
evaluating the criteria and the sub-criteria. Saaty’s (1980) nine-point scale is
used for pairwise comparison:

10. BIJ .
1 – equally preferred;
18,2 .
2 – equally to moderately preferred;
.
3 – moderately preferred;
. 4 – moderately to strongly preferred;
.
5 – equally preferred;
270 .
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 eigenvector
using 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 and
the random index using the following equation:
CI lmax 2 n
CR ¼ ¼ ð4Þ
RI n21
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 survey
results from the customers, matrix B is found as per Step 4. Steps 5 and 6 are carried
out 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 with
respect to j service quality factors is evaluated through the following equation:
Table I. Order of matrix 1 2 3 4 5 6 7 8 9 10
Random index
calculation Random index 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

11. X
SFMi ¼ ðlocal weight of competitor w:r:t: criterion j from matrix BÞ An integrated
ð5Þ
£ ðlocal weight of criterion j from matrix AÞ: model for PM
Step 6. The system performance measure (SPM) is calculated in this step. For the
calculation, the EBG approach is used. In the EBG approach, both the objective and
quality factors found in the above steps are converted into consistent and dimensionless 271
indices to measure the SPM. The SPM of competitor i is found from equation (6):
SPM i ¼ aðOFM i Þ þ ð1 2 aÞSFM i ð6Þ
where, 0 , a , 1, a ¼ the objective factor weight and 1 –a ¼ the quality factor
weight.
Step 7. The SPM is analysed in this step and the decisions about the manufacturing
design 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 improvement
opportunities need to be analysed. When the evaluated SPM value falls below a
satisfactory level, the design of the process itself is faulty and the redesigning of the
entire process becomes essential.
4.2 Manufacturing performance improvement
In the second phase, QFD is used to improve the service performance. The QFD
procedure uses a series of HOQ matrices to express the linkages between the inputs and
outputs of the different phases of development (Hauser and Clausing, 1988). In the
redesigning process, the HOQ needs to be developed and the optimum set of service
requirements 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 be
reviewed periodically.
5. Model validation by case study
To demonstrate the applicability of the model in manufacturing organizations, we rely
on a case study approach on two identical valve manufacturing companies, namely,
Unit A and Unit B, located at the Tiruchirappalli Regional Engineering College Science
and Technology Entrepreneurs Park in Tiruchirappalli, India. The details of the study
are provided below.
5.1 Manufacturing performance measurement
Step 1. The performance measures that influence the performance of the two companies
are identified through discussions with the company managers. About 15 factors were

12. BIJ shortlisted and identified. These factors are grouped according to their respective
18,2 categories as:
(1) Process efficiency:
.
operating cost per employee;
.
cost of goods sold;
272 .
product development time;
.
rejection ratio;
.
actual production against planned production;
.
age of plant and equipment; and
.
capacity utilization.
(2) Product and process innovation:
.
RD 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 as
follows.
Objective factors:
(1) Effective cost:
.
cost of goods sold;
.
actual production against planned production;
.
RD 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 B
using the sample questionnaire as shown in the methodology.
Step 4. The OFM was now calculated in terms of CTE, from the structured survey
conducted in Unit A and Unit B. The data from both manufacturing units are shown in
Tables II and III.

13. The above data were collected and calculated from the above questionnaire:
An integrated
58; 168 model for PM
Cost Time Effectiveness ðCTEÞA ¼
58; 168 þ 52; 800
21
825 90
þ þ
{ð1=1; 071Þ þ ð1=825Þ} 90 þ 120 273
21
4:5
þ
{ð1=4:5Þ þ ð1=8:4Þ}
¼ 0:52142 þ 0:000003 þ 0:42857 þ 0:07584
¼ 1:02583
52; 800
Cost Time Effectiveness ðCTEÞB ¼
58; 168 þ 52; 800
21
1; 071 120
þ þ
{ð1=1; 071Þ þ ð1=825Þ} 90 þ 120
21
8:4
þ
{ð1=4:5Þ þ ð1=8:4Þ}
¼ 0:47581 þ 0:000002 þ 0:57143 þ 0:04063
¼ 1:0879
1:02583
Objective Factor Measure ðOFMÞA ¼ ¼ 0:48532
1:02583 þ 1:08789
1:08789
Objective Factor Measure ðOFMÞB ¼ ¼ 0:51468
1:02583 þ 1:08789
Step 5. Prioritization using AHP
The 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
Table II.
Average effective cost 58,168 52,800 Cost data from
Average ineffective cost 825 1,071 manufacturing units
Parameter Time of unit A (days) Time of unit B (days)
Average effective time 90 120 Table III.
Average ineffective time 4.5 8.4 Time data

14. BIJ
18,2
274
criteria
Table IV.
AHP matrix and its
eigenvalues for various
Timely Employee Modern Return on Cost of goods sold/ Work done right the Process Warranty Eigen
delivery morale equipment sales inventory first time efficiency claims vector
Timely delivery 1 3 3 3 5 1/3 1/5 5 1.343
Employee morale 1/3 1 5 3 3 1/3 1/3 3 1.049
Modern equipment 1/3 1/5 1 3 3 1/5 1/7 5 0.629
Return on sales 1/3 1/3 1/3 1 3 1/7 1/7 3 0.429
Cost of goods sold/
inventory 1/5 1/3 1/3 1/3 1 1/7 1/5 3 0.317
Work done right the
first time 3 3 5 7 7 1 1 7 2.472
Process efficiency 5 3 7 7 5 1 1 7 2.819
Warranty claims 1/5 1/3 1/5 1/3 1/3 1/7 1/7 1 0.214

15. Order of the Matrix A ðnÞ ¼ 8: An integrated
9:2724 2 8 model for PM
Consistency Index ðCIÞ ¼ ¼ 0:181
821
CI 0:181
Consistency Ratio ðCRÞ ¼ ¼ ¼ 0:12
RI 1:41 275
As per the AHP process, if the CR is less than or equal to 10 percent or 0.1, the matrix is
deemed consistent. The QFM is found from the principal eigenvector of the comparison
matrix 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 more
importance is given to the QFM than to the OFM. Using the SPM equation, the service
SPM 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, to
improve the performance of Unit A, the manufacturing parameter has to be redesigned.
5.2 Manufacturing performance improvement
From the system performance measurement values, we conclude that Unit A which has a
lower 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-engineering
strategy. A cascade of charts can be created dealing with the manufacturing process
hierarchy. In this way, all manufacturing design processes at whatever level may be
traced back to the customer and the effect of changes at any level in the performance
checked against the overall company strategy. Thus, QFD has the voice of the customer
in 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 the
customer requirements. This has already been ascertained in the EBG model.
From the absolute weight column in the AHP matrix, it is very clear that the
prioritized customer requirements are in the following order: process efficiency,
work done right the first time, timely delivery, modern equipments, employee
morale, return on sales, cost of goods sold per inventory and warranty claims.

16. BIJ
Quality parameters Unit A Unit B Eigenvector
18,2
Timely delivery
Unit A 1 1/5 0.16
Unit B 5 1 0.83
Employee morale
276 Unit A 1 1/5 0.16
Unit B 5 1 0.83
Modern equipment
Unit A 1 1/7 0.125
Unit B 7 1 0.875
Return on sales
Unit A 1 1/3 0.25
Unit B 3 1 0.75
Cost of goods sold per inventory
Unit A 1 1/3 0.25
Unit B 3 1 0.75
Work done right the first time
Unit A 1 1/7 0.125
Unit B 7 1 0.875
Process efficiency
Unit A 1 3 0.75
Unit B 1/3 1 0.25
Warranty claims
Table V. Unit A 1 1/7 0.125
Comparison matrix Unit B 7 1 0.88
(2) Identifying the MDRs. The QFD team identifies the MDRs that are most needed
to fulfil the customer requirements. The MDRs identified are as follows: quality
characteristic, technical expertise development, strategic investment policy,
correct and thorough workmanship development, job scheduling, clarification
with 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 related
to MDRs through the central matrix construction. The central matrix provides the
degree 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 customer
competitive assessment in the HOQ provides a good way to determine
whether the customer requirements have been met. It also indicates areas to be
concentrated upon during the next design review. It contains an appraisal of
where an organization stands relative to its major competitors in terms of each
requirement. The assessment values are obtained from the EBG model.
(5) To meet the customer requirements, the manufacturing organization has to
prioritize the MDRs and fix the targets for each MDRs.
After developing the HOQ for Unit A, we obtain the relative absolute weights of the
various design requirements with respect to the customer requirements. Thus, the design
requirements are prioritized according to their absolute weights in the HOQ matrix.

17. An
Clarification environment Rewards
Technical Strategic Correct and with the of and
Quality expertise investment thorough Job customer on the attitudinal recognition
characteristics development policy workmanship scheduling work to be done change rules
Timely
delivery W † † † † † † 0.83 0.139
Modern
equipment † † W 0.83 0.109
Employee
morale W W † † † 0.875 0.724
Return on
sales † † W † 0.75 0.0505
Cost of
goods
sold/
inventory W † † 0.75 0.0386
Work
done right
the first
time W † † W D D 0.88 0.2687
Process
efficiency † W D D † 0.25 0.2945
Warranty
claims D D 0.875 0.0252
Absolute
weights 5.67 4.77 2.07 4.75 4.3 1.52 2.17 2.63
Targets Process One training Policy Continuous Better Organizational Regular Incentives Target Absolute
reliability and in two review training operations accessibility motivational and bonus value weight
improvement months once in management programs schemes from ahp
two matrix (w)
months
Notes: W – Moderately related (three points); † – strongly related (nine points); D – weakly related (one point)
An integrated
House of quality
277
model for PM
Table VI.

18. BIJ Further, in accordance with these prioritized MDRs, the targets to meet these MDRs have
18,2 been identified and employed by the QFD team. The prioritized orders of the MDRs and
the targets to achieve are listed below.
The priority orders of the MDRs are: quality characteristics, technical expertise
development, correct and thorough workmanship, job scheduling, rewards and
recognition rules, an environment for attitudinal change, strategic investment policy,
278 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 – organizational
accessibility.
6. Conclusion
This paper demonstrates the need for a manufacturing PM model. A literature survey has
been carried out on the various frameworks and tools used. An integrated closed model to
enhance PM has been proposed. It also provides a means to identifying the current
performance of an organization and a methodology to improve it further. An important
contribution of this model is that it combines both qualitative and quantitative
dimensions of manufacturing performance measurement. The proposed model provides
an opportunity to operationalise the relationship among the cost, time, and service
quality dimensions. Both objective and manufacturing quality factors have been
converted into consistent dimensionless indices to measure the system performance.
The case study presented in this paper has demonstrated the applicability of the
model to support a manufacturing unit. It has shown how performance measures are
identified and how they can be calculated for two different units using time, cost, and
service quality dimensions. The case study proves the usability of the EBG model for
the PM process. From the SPM, the performance of the manufacturing organizations is
analysed and manufacturing parameters is redesigned using QFD wherever necessary.
Improving performance is a never-ending process and organizations should strive
to achieve it for attaining the optimal level of cost and profit, as well as increase
customer satisfaction and goodwill, and gain potential future business. Hence, the
process of measuring and redesigning the manufacturing performance measures needs
to be monitored and the implementation plans reviewed often. Finally, this model
for PM can be extended to the service sector.
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Corresponding author
Mark Goh can be contacted at: bizgohkh@nus.edu.sg
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