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Int. J. Production Economics 112 (2008) 380–390
www.elsevier.com/locate/ijpe
An objective-oriented and product-line-based manufacturing
performance measurement
Chee-Cheng Chen
Department of Business Administration, National Pingtung University of Science and Technology, 1, Hseuh-fu Rd., Nei-pu,
Pingtung 91201, Taiwan, ROC
Received 1 August 2004; accepted 30 March 2007
Available online 1 May 2007
Abstract
Performance-measurement systems (PMSs) that are based on traditional cost-accounting systems do not capture the
relevant performance issues for today’s manufacturing environment. A variety of integrated systems have been proposed
to overcome the limitations of the traditional PMSs, but these systems have been inadequate. This paper presents an
integrated dynamic performance measurement system (IDPMS) that integrates three main areas: company management,
process improvement, and the factory shop floor. To achieve an integrated system, these three areas are linked through
dynamically defined performance measures and performance standards from production planning to customer. The
indicators are transformed into quantitative just-in-time parameters that are utilized with management by objectives
(MBO) principles to develop a manufacturing PMS that satisfies both internal and external customers. An example is given
that illustrates how the IDPMS addresses current PMS requirements.
r 2007 Elsevier B.V. All rights reserved.
Keywords: Performance measurement; Management by objective; Process improvement; Customer satisfaction; Manufacturing
1. Introduction
In today’s highly competitive business environment, product manufacturers need to provide
customized and innovative products. To achieve
this, they require innovative methods of performance measurement. In measuring manufacturing
performance, manufacturers usually compare their
own plants with other manufacturing plants in the
industry in terms of such parameters as customer
satisfaction, product quality, speed in completing
manufacturing orders, productivity, diversity of
Tel.: +886 8 7703202x7679; fax: +886 8 7740367.
E-mail address: carl2004@mail.npust.edu.tw.
product line, and flexibility in manufacturing new
products (Cordero et al., 2005). The essential
function of a performance measure is to assess
how well the activities within a process, or the
outputs of a process, achieve specified goals. This
involves a comparison of actual results with a
predetermined goal and an assessment of the extent
of any deviation from that goal. A target level of
performance is usually expressed as a quantitative
standard, value, or rate (Ahmad et al., 2005).
The selection of a range of performance measures
appropriate to a particular company should be
made in the light of the company’s strategic
intentions, and should suit the competitive environment in which the company operates. For example,
0925-5273/$ - see front matter r 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.ijpe.2007.03.016
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if technical leadership and product innovation
represent the basis of a manufacturing company’s
competitive advantage, performance in these areas
must be measured relative to the competitors.
Similarly, if a service company differentiates itself
in the marketplace on the basis of quality of service,
it should be monitoring and controlling the desired
level of quality (Ahmad and Dhafr, 2002). Whether
the company is in the manufacturing sector or the
service sector, it is necessary to choose an appropriate range of performance measures, and these
measures must be balanced to ensure that one
performance or set of performance dimensions is
not stressed to the detriment of others. The mix that
is chosen will differ from case to case. Incomplete
metrics can lead to inappropriate action (Ahmad
and Dhafr, 2002), and performance areas must
therefore be operationalized (that is, made measurable) in a way that allows performance to be
adequately measured against relevant performance
indicators (Jose et al., 1999). To achieve this, many
companies have adopted modern management
strategies—such as total quality management
(TQM), just-in-time (JIT), computer-integrated
manufacturing (CIM), and optimized production
realization (OPR).
A business can achieve success only by understanding and fulfilling the needs of its customers. A
failure to recognize this can cause huge potential
losses (Ernst and Young, 1992). A customer focus
involves the establishment of links between customer requirements and internal processes (Sousa,
2003; Reiner, 2005), and implies a shifting of
company goals from the maximization of company
profits in one project to the optimization of the
value of the customer’s project by meeting jointly
agreed objectives (Cova and Salle, 2005). However,
having adopted such a customer focus, many
companies use performance measures that are based
on outdated management cost systems that are
incompatible with their new operating philosophies.
Consequently, many researchers are suggesting new
comprehensive approaches to performance measurement—approaches that support day-to-day
operations by providing managers, supervisors,
and operators with timely and relevant information
(Shah and Ward, 2003).
To provide a comprehensive overview of company
performance, researchers have tried to combine
more than one performance measurement through
the development of integrated performance-measurement systems (PMSs). These integrated systems
381
address many of the shortcomings of older PMSs;
however, there are still issues associated with today’s
manufacturing environment that must be considered. For example, these systems work primarily
as monitoring-and-controlling tools, and most
have not incorporated an explicit feedback
loop that supports improvement in performance
measurement. In addition, these systems are not
dynamic, and they cannot therefore provide mechanisms for adapting to a changing manufacturing
environment.
To address these shortcomings, this paper presents an integrated dynamic performance-measurement system (IDPMS), which has been developed in
collaboration with an international electronics firm
(‘D company’) in Taiwan. The proposed system
focuses on improving manufacturing competitiveness by overcoming the limitations of existing
PMSs and by facilitating continuous improvement
(Ghalayini et al., 1997). To this end, the system
incorporates interaction among three groups: (i)
management (production planning); (ii) manufacturing; and (iii) customers. Having proposed the
system and having discussed the indicators used in
it, the present paper presents a case simulation to
verify the practical suitability of the proposed
system.
The remainder of this paper is arranged as
follows. Following this Introduction, the next
section of the paper reviews the literature pertaining
to performance measurement. Section 3 then
presents the proposed IDPMS. Section 4 illustrates
how the system would be applied. Sections 5 and
6 provide a result comparison and summarize
the benefits of IDPMS with respect to existing
PMSs.
2. Literature review
2.1. Overview
An effective PMS should include the traditional
financial and cost-accounting criteria used by senior
management and also the tactical-performance
criteria that are used in assessing a firm’s current
level of competitiveness. Such tactical-performance
measures vary according to the needs of the various
management levels and functional areas within the
organization. Each functional area should develop
and utilize a set of performance criteria consistent
with its particular operating characteristics and
strategic objectives. An effective PMS should lead
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to the integration of operations, marketing, finance,
engineering, and accounting to ensure that they act
as a unified and coordinated value-adding system.
The system must have a long-term orientation such
that continual improvement in both product and
process leads to a sustainable competitive advantage
(Wisner and Fawcett, 1991).
In designing a PMS to support strategy implementation, particular challenges in modern manufacturing industries must be addressed. The rapid
escalation in global competition has brought new
doctrines (for example, JIT, TQM, and the ‘flexible
factory’), and this has necessitated new approaches
to performance measurement—including the expansion of traditional efficiency-focused manufacturing
PMSs to embrace new manufacturing-performance
measures (Abernethy and Lillis, 1995; Chenhall,
1997; Perera et al., 1997). However, the widespread
use of multiple measures has raised several implementation issues, including the potential problem
of inadequate managerial commitment (Ittner and
Larcker, 1998; Lillis, 2002). Because companies
operate in dynamic environment, and because
performance measurement is thus a dynamic
process, Suwignjo et al. (2000) developed a quantitative model for performance-measurement system
(QMPMS), which used the analytic hierarchy
process (AHP) to quantify the effects of various
factors on performance. The system allowed for the
fact that performance measures will change over
time and vary between companies. In a similar vein,
Hayes and Wheelwright (1984) noted that management must make decisions in a variety of areas (such
as capacity, facilities, technology, quality, and so
on), each of which presents a variety of choices and
each of which can have an effect on the manufacturing function’s ability to implement the organization’s business strategy. Senior management
therefore needs to consult closely with its manufacturing function to obtain its perspective on the
major issues that are facing the business. In this
regard, Choe (2004) has noted that advanced
manufacturing technology (AMT) can achieve low
cost, improved quality, increased flexibility, and
dependability of supply, and that the measurements
of manufacturing performance in AMT should
reflect these four strategic goals. The works of
Gunasekharan et al. (2005) and Gupta and Galloway (2003) are two examples of scholarly work
(among a plethora of articles) that has focused on
the use of financial and cost-based performance
measures in manufacturing operations.
A ‘customer orientation’ has been variously
defined. For example, it has been defined as ‘‘the
set of beliefs that puts the customer’s interest first’’
(Desphande et al., 1993) or, alternatively, as a
‘‘firm’s ability and will to identify, analyze, understand, and answer user needs’’ (Gatignon and
Xuereb, 1997; Matsuo, 2006). Yet another definition was supplied by Narver and Slater (1990), who
defined ‘customer orientation’ as ‘‘the sufficient
understanding of one’s target buyers to be able to
create superior value for them continuously’’.
According to Liu et al. (2002), a ‘customer
orientation’ is a set of beliefs that places customers’
needs and satisfaction as the priority of an
organization; it focuses on dynamic interactions
among the organization, its customers, its competitors in the market, and its internal stakeholders. In
summary, the term ‘customer orientation’ refers to
an organization (and individuals within the organization) focusing their efforts on understanding and
satisfying customers (Huff and Kelley, 2005).
Given these definitions, a customer-focused manufacturing strategy should include the dimensions of
cost, quality, flexibility, and dependability of supply
(Ahmad and Dhafr, 2002). In this context, it is
important to note that a focus on the customer is
the underpinning principle of the TQM philosophy,
which is concerned with how an organization
designs and introduces products and services,
integrates production-and-delivery requirements,
and manages supplier performance (Evans and
Lindsay, 1999). From a total quality perspective,
all strategic decisions that a company makes should
be ‘customer-driven’ (Evans and Dean, 2000).
Knowing the customer begins with a detailed
evaluation of what is known about the customer,
which is essentially an exercise in measuring
customer satisfaction (Player and Keys, 1999).
Mohr-Jackson (1991) proposed an extended
concept of customer orientation to include internal
customers, noting that this requires additional
activities. These include: (i) understanding internal
customers’ requirements for the effective delivery of
needs and preferences of external customers; (ii)
obtaining information about external customers’
needs and preferences through effective interdepartmental communication; and (iii) creating additional
final buyer value by increasing internal customer
benefits. According to this view, the provision of
superior value to the external customer requires
superior value to be provided at each point in the
value chain. Internal suppliers therefore need to
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demonstrate an internal-customer orientation by
focusing on satisfying the requirements of their
internal customers, thus ensuring that a customer
orientation is developed throughout the organization (Hauser et al., 1996), rather than limiting this
orientation to the point of customer contact
(Conduit and Mavondo, 2001). In this respect,
Breyfogle et al. (2001) noted that a good
PMS addresses both external quality and internal
quality.
Bhote (1991) has asserted: ‘‘If performance
isn’t being measured, it isn’t being managed’’.
In this regard, ‘management by objectives’ (MBO),
which was introduced to industry in the early
1950s by Drucker (1954), has been used effectively
by senior management. The first step is to establish
objective measurements, and the best measures
are customer-focused and goal-oriented. They help
people to learn how to improve their performance
by pointing out where they are deficient and
by establishing achievable timetables to reach
desired levels. In this way, they can be used as a
basis for improved performance (George and
Weimerskirch, 1998).
Ahmad et al. (2005) noted that performance
measurement involves a comparison of actual
operation results with established performance
targets. In this regard, Six-Sigma (Gack and
Robinson, 2003) and Balanced Scorecard (BSC)
(Kaplan and Norton, 2001) offer frameworks for
business improvement. Both approaches begin with
goal definition, and both are based on a quantitative
measurement of performance using pre-defined
business goals and pre-defined metrics (Trienekens
et al., 2005).
2.2. Recent concepts and practices in manufacturing
performance
The manufacturing performance assessment and
analysis introduced in Ahmad and Benson (1999)
covered the areas of quality, delivery reliability, cost
(price minus profit margin), and delivery lead time.
The KPIs within manufacturing strategy are cost,
quality, inventory, flexibility, and delivery (Corbett,
1998). A part of a project survey was carried out to
identify which performance indicators companies’
use and which ones they characterize as important.
The top five were: profitability, conformance to
specifications, customer satisfaction, return on
investment, and materials/overhead cost. When
looking at the performance areas to which the
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specific indicators are related and considering their
relative importance it was also possible to rank the
importance of these performance areas (from top to
bottom): efficiency, quality, competence (technical),
flexibility, innovativeness, speed, and capacity (Jose
et al., 1999). The measured KPIs are normally
split into six sections: (1) safety and environment,
(2) flexibility, (3) innovation, (4) performance,
(5) quality, and (6) dependability. The focus was
on the KPI of the dependability which consist of:
(1) customer complaints, (2) on-time-in-full delivery
to customers (OTIFc), (3) on-time-in-full delivery
from suppliers (OTIFs), and (4) overall equipment
effectiveness (OEE) ¼ product rate quality rateavailability (Ahmad and Dhafr, 2002). A six-item
scale is used to measure the operational performance of a manufacturing plant after different
levels’ lean manufacturing practice. The items
include 5-year changes in scrap and rework costs,
manufacturing cycle time, first pass yield, labor
productivity, unit manufacturing cost, and customer lead time (Shah and Ward, 2003). Global
competition demands the manufacturing organizations improve quality, reduce delivery time, and
minimize costs. In response to this, many manufacturing organizations have implemented different
excellence programs to improve their performance.
Lean manufacturing techniques, performance
measurement, and benchmarking, were included
in many of those excellence programs (Ahmad
et al., 2005).
2.3. Popular model review
D Company was supported to establish the
manufacturing performance evaluation through
integrating ‘quality’, ‘cost’, and ‘delivery’ by the
Corporate Synergy Development center (CSD),
Taiwan. The manufacturing performance (Y) is
measured by
Y ¼ W q Q þ W c C þ W d D,
(1)
where
Q : score of quality ¼
good production
,
good production þ failed QC
(2)
Wq: weight of quality,
C : score of cost
P
ðactual cost target costÞ
P
¼ 0:60,
target cost
ð3Þ
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384
Wc: weight of price,
D : score of delivery ¼ 1 number of delayed lots
,
number of delivered lots
(4)
Wd is the weight of delivery, and Wq+Wc+
Wd ¼ 100.
From the rating system defined by the D Company, we can find three factors are usually considered
when the manufacturer adopts the formula of grading
to measure the manufacturing performance.
(a) Quality (Q)—quality of product,
(b) Cost (C)—price of product,
(c) Delivery (D)—delivery time of product.
The weight of each factor can be adjusted when
we apply the calculations of grading that depend on
the needs of manufacturers. Some specific manufacturers just consider quality, while others put
quality and cost into consideration. Some of them
consider all three, quality, service, and delivery.
Two example equations are provided for the score
calculations of manufacturing performance:
Score of manufacturing performance
¼ 40Q þ 35C þ 25D
¼ 40Q þ 40C þ 20D.
or
ð5Þ
(6)
In practice, these manufacturing PMSs were
unable to quantify the customer orientation and
objective orientation requirement levels in the past
decade. These measurement systems cannot highlight
the quality or business concerns in a JIT manner that
will promote the effectiveness of improvements. The
advantages of establishing a new model is obvious
after analyzing the concerns listed below.
(a) Internal failure: These models do not reflect the
rework, scrap, and sorting that could occur on
the production line due to manufacturing
quality problems.
(b) Customer voice: These models do not help
highlight customer complaints and their possible impact on the organization.
(c) Equipment effectiveness and engineering efficiency: These models do not evaluate the
efficiency of engineering maintenance programs
caused by poor manufacturing management.
Potential programmatic impacts, such as schedule and cost impacts, should also be brought to
management’s attention.
(d) Quality level: The percentage of defects inside
lots from various process stages are sometimes
extremely different and may significantly impact
profits.
(e) Customer orientation: These models do not
involve full satisfaction levels or views from
actual product users.
(f) Objective orientation: If indicators are applied to
manufacturing project performance measures
with different characteristics, arguments will
always ensue. Setting a target for each performance measurement using ‘achievement level’ or
‘AL’ will be one of the solutions.
This study attempts to highlight suitable factors
that if applied to day-to-day, year-to-year, and
product-to-product in industry PMSs that could
help transform these factors into measurable,
quantitative, and JIT parameters. These parameters
could be utilized in planning and establishing a
manufacturing performance rating system based on
our work with an international electronics firm
(D company). The relevance of the proposed system
to a manufacturing team, product line (internal
customer), and external customers is presented. The
system logic is then detailed. In addition to
describing the system, applications and conclusions
are drawn.
3. A new model with a customer focus
To address the above-mentioned concerns, the
present study set out to develop an integrated and
dynamic manufacturing performance-measurement
model. Two central concerns for all measurement
systems are: (i) what to measure and (ii) how to
weight the various performance categories. Most of
the objective and quantifiable variables in manufacturing performance have been previously specified, but management might also wish to use a
number of qualitative factors in assessing manufacturing performance—including problem-resolution
ability, technical ability, and corrective-action response. Although these factors are somewhat
subjective in nature, management can still assign
each factor a score or rating.
3.1. Satisfaction indicators in manufacturing
An IDPMS (Ghalayini et al., 1997) was developed in conjunction with the ‘D Company’ of
Chungli, Taiwan. The IDPMS integrated three
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main areas of the company: (i) management; (ii) the
process-improvement team; and (iii) the factory
shop floor. To achieve an integrated system, these
three areas were linked through the specification,
reporting, and updating of certain defined performance measures.
The four main indicators established in this study
were adapted from Ahmad and Dhafr (2002):
Cc (customer complaint);
Od (on-time delivery);
Ee (equipment effectiveness); and
Cq (cost of quality).
These performance indicators were selected because: (i) they indicate important manufacturing
performance areas; (ii) provide critical links among
business strategy, internal organization, and technology; and (iii) are fairly easy to measure or
estimate. The company’s relative performance in
each area of the plant (or specific product line) can
be assessed by comparing the relevant performance
indicators with internal goals/standards, competitors, and customer demand. After comparisons are
made for all performance areas, an overview of the
performance gaps can be made.
3.1.1. Customer complaint (Cc)
A ‘customer complaint’ was defined as a quality
or reliability issue occurring with an external
customer and confirmed as being caused by
manufacturing failure. Normally, a formal notification from the customer and a formal ‘corrective
action report’ (CAR) are required to confirm that
an issue exists. These complaints might arise from
any business area.
A measurement can be made to identify operational problems that might be avoided in future.
This measurement is determined by the number
(and nature) of customer’s complaints. Written,
verbal, and anecdotal information are all recorded.
These data are then shared to avoid repeat problems
at other sites. The quality-assurance department is
responsible for the provision of the required
information on a regular basis.
The KPI to be measured is the number of
customer complaints received—expressed as an
absolute number or as a percentage of the dispatches. The goal is to achieve fewer than x%
complaints on dispatches (with an x value of less
than 1 being suggested).
385
3.1.2. On-time delivery (Od)
This indicator measures the delivery of product
on time and in full—with no errors in product,
packaging, transport, or supporting documentation.
The commonly used performance measures in this
area are ‘unit penalty’, ‘mean tardiness’, ‘maximum
lateness’, and ‘minimum lateness’. Tardy jobs can
incur costs, such as contractual penalties, depending
on how late they are (Lengyel et al., 2003).
Such a measure requires a rigorous recording
system by the plant (or by the distribution company
if this aspect is out-sourced). The KPI goal of this
measure is to achieve a value greater than y% (with
a y value greater than 99 being suggested).
3.1.3. Equipment effectiveness (Ee)
This measure is designed to determine the
reliability of assets and their capability to deliver
the desired performance. This measure is assessed
by taking the product of two sub-measures—
‘quality rate’ and ‘availability’. Equipment effectiveness (Ee) is thus obtained as follows:
Ee ¼ quality rate availability:
The quality rate is the amount of product that is
right the first time—without adjustment, recycles,
and so on. To achieve a satisfactory performance in
this regard, it is necessary to achieve a very high
first-time-right rate. Quality rate is calculated as
follows:
Quality rate ¼
good production
.
good production þ failed QC
The availability is defined in terms of the number
of hours the plant actually operates divided by the
number of hours in a month. For example, if a
factory plans to operate for 22 hours per day for 25
days per month, the maximum available number of
hours is 550. If so, availability is calculated as
follows:
Availability
550 ðnumber of hours of total shutdownÞ
¼
.
550
Ee rests on the principle that the best manufacturing performance occurs when a plant operates to full
capacity, always produces a perfect product, and
never breaks down. Data on capacity usage, quality
performance, and equipment breakdown are therefore required to determine the Ee. The manufacturing manager at the site is responsible for providing
the required information on a timely basis.
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It is suggested that an Ee of 99.5% on critical
equipment should be the target. This can be
achieved if quality rate is greater than 99.9%, and
availability is greater than 99.6%.
To achieve this goal, the following practices
should be implemented: six-sigma performance;
fully automatic start-up; shutdown and fail-safe;
intelligent measurement; total accurate dynamic
models; multivariate statistical process control;
design for success not failure; and predictive
maintenance.
3.1.4. Cost of quality (Cq)
Cost of quality (or, more accurately, cost of poor
quality) is invisible and seldom pursued; it is often
referred to as a ‘hidden’ quality cost (Dahlgaard
et al., 1998; Krishnan et al., 2000). It is effectively an
‘opportunity cost of sales lost’ brought about by a
customer’s experience of a poor-quality product or
poor delivery service.
The consequential costs of such failure include
engineering time, management time, shop and field
downtime, delivery problems, lost orders, lost market
share, customer dissatisfaction, and decreased capacity generally. Some companies have found a ‘multiplier effect’ between the measured costs and the ‘true’
failure costs (Chen and Yang, 2003).
The measure is defined in terms of a comparison
between actual cost and target cost in completing a
specific manufacturing task. The goal is to achieve a
measure of z% consequential loss on sales revenue
as a result of failure (with a z value less than 3 being
suggested).
3.2. Target values for manufacturing performance
At the end of each year, senior management in a
manufacturing enterprise measures the performance
of each manufacturing team or product line and sets
target values for the following year. These targets
are announced as a management ‘commitment’ or
‘pledge’ for all customers of various product lines.
An example case study will be discussed. This
case study examines a manufacturer whose actual
values for the year 2004 and target values for the
year 2005 for each performance indicator are given
in Tables 1 and 2.
3.3. New performance measurement model
The present study developed a new model
through the integration of the four indicators
discussed above (Cc, Od, Ee, and Cq), with
appropriate weights of r1, r2, r3, and r4, respectively.
Performance (P) was obtained by matching AL
through comparisons of a range of percentages from
actual values and target values for each indicator.
That is,
Actual Cc is compared with target Cc to get an
AL, which is used to obtain a PCc, Cc
performance value through matching.
(1) The performance of Od, Ee, and Cq (‘POd’,
‘PEe’, and ‘PCq’, respectively) can be measured in
the same way.
Formula example 1: PCc—Cc performance
depends on the AL of target Cc.
Formula: PCc ¼ achievement level of target Cc,
ALn
1.00 ¼ AL1: Cc lower than target above 60%
0.95 ¼ AL2: Cc lower than target 41–60%
0.90 ¼ AL3: Cc lower than target 21–40%
0.85 ¼ AL4: Cc lower than target 6–20%
0.80 ¼ AL5: Cc equivalent to target 75%
0.75 ¼ AL6: Cc higher than target 6–20%
0.70 ¼ AL7: Cc higher than target 21–40%
0.65 ¼ AL8: Cc higher than target 41–60%
0.60 ¼ AL9: Cc higher than target above 60%.
Table 1
The actual values for various power supply product business units (B.U.), year 2004
B.U.
Indicator
Desktop power
Adaptor power
Telecom. power
Server power
Cc—customer complaint
Od—on-time delivery
Ee—equipment effectiveness
Quality rate
Availability
Cq—cost of quality
3.6% on dispatches
96.6%
94.6%
97.6%
96.9%
8.1% of sales
2.2% on dispatches
97.8%
97.1%
99.1%
98.0%
4.7% of sales
7.3% on dispatches
94.2%
81.7%
93.2%
87.7%
13.6% of sales
3.8% on dispatches
97.1%
95.9%
98.5%
97.4%
8.5% of sales
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387
Table 2
The target value for various power supply product business units (B.U.), year 2005
B.U.
Indicator
Desktop power
Adaptor power
Telecom. power
Server power
Cc—customer complaint
Less than 2% on
dispatches
Higher than 98.5%
Higher than 98.0%
Less than 1% on
dispatches
Higher than 99.5%
Higher than 99.0%
Less than 5% on
dispatches
Higher than 96%
Higher than 85.0%
Less than 2% on
dispatches
Higher than 99%
Higher than 98.0%
Od—on-time delivery
Ee—equipment
effectiveness
Quality rate
Availability
Cq—cost of quality
Higher than 99.0%
Higher than 99.8%
Higher than 95.0%
Higher than 99.0%
Higher than 99.0%
Higher than 99.2%
Higher than 90.0%
Higher than 99.0%
Lower than 6.0% of sales Lower than 3.0% of sales Lower than 10.0% of sales Lower than 6.0% of sales
Or we may have many kinds of values set either in
the number of achievement levels or in the actual
output vs. target degree range depending on the
organizational requirements. Two additional valuesetting examples are shown as below:
Formula example 2: PCc—Performance of Cc.
Formula: PCc ¼ achievement level of target Cc,
ALn
1.00 ¼ AL1: Cc lower than target above 80%
0.90 ¼ AL2: Cc lower than target 61–80%
0.80 ¼ AL3: Cc lower than target 41–60%
0.70 ¼ AL4: Cc lower than target 21–40%
0.60 ¼ AL5: Cc equivalent to target 720%
0.50 ¼ AL6: Cc higher than target 21–40%
0.40 ¼ AL7: Cc higher than target 41–60%
0.30 ¼ AL8: Cc higher than target 61–80%
0.20 ¼ AL9: Cc higher than target above 80%.
Formula example 3: PCc—Performance of Cc.
Formula: PCc ¼ achievement level for target Cc,
ALn
1.00 ¼ AL1: Cc lower than target above 80%
0.80 ¼ AL2: Cc lower than target 31–80%
0.60 ¼ AL3: Cc equivalent to target 730%
0.40 ¼ AL4: Cc higher than target 31–80%
0.20 ¼ AL5: Cc higher than target above 80%.
Similarly, value-setting flexibility can be applied
for all other indicators (Od, Ee, and Cq), or only
some of them. The maintenance of performancemeasurement consistency, continuity, and flexibility
is necessary. The weights r1, r2, r3, and r4, can be
set by constructing a scale—rating these performance indicators/objectives in terms of their relative
importance. It is strongly recommended that
decision-makers approximate the importance of
each indicator using AHP and pairwise comparisons (Saaty, 1980).
(2) Manufacturing performance measurement
formula
(a) If a manufacturing team organizes just one
product group, the manufacturing performance
value (Pm) will be the same as the product
performance value (Pp). This value is given by
Pm ¼ Pp ¼ 100 ðr1 PCc þ r2 POd
þ r3 PEe þ r4 PCqÞ,
ð7Þ
where r1 þ r2 þ r3 þ r4 ¼ 1; ri X0; 1pip4.
(b) If a manufacturing team organizes several
groups of products (more than one type), the
Pm is obtained using:
Pm ¼
Pp1 þ Pp2 þ þ Ppn
n
where n41. (8)
(c) The parameters in this formula are applied
flexibly to cover all kinds of manufacturing
projects with different characteristics. For instance,
(1) If a manufacturing project is not suitable for
the Ee measurement, the parameter weights
can be
r1 þ r2 þ r4 ¼ 1.
(2) If another manufacturing project has been
adopted for a program-support role, and is
not suitable for Cq’s control, the formula
weights can be expressed using
r1 þ r2 þ r3 ¼ 1.
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388
4. Results comparison
An example is presented to demonstrate how the
proposed model of formula (7) can be applied to
performance measurement of a manufacturing
team, and to compare it with the popular model
of formula (5). The proposed method can be
compared with different AL and weight values for
each performance indicator. Formula example 1 is
used in this comparison.
The basic manufacturing data from two product
groups (Desktop power and Telecom. power) are
given in Tables 3 and 4. The results from the
proposed model of formula (7) and the popular
model of formula (5) are shown in Tables 3 and 4,
respectively.
Compared with formula (5), the application of
the new model of formula (7) has produced
sensitive, accurate, and effective manufacturing
performance rating results for different achievement
levels.
5. Conclusions
The proposed manufacturing performance-measurement model with a customer-satisfaction orientation could be applied by firms in different
industries to address all kinds of manufacturing
management situations. The proposed model can
assist firms in selecting and rewarding the best
manufacturing teams and integrating their capabilities to develop an appropriate profit-improvement
program for meeting and exceeding specific customer requirements.
The merits of the proposed model are that it is
complete, flexible, and effective.
In terms of completeness, the model integrates
four performance indicators and covers three stages
(planning, production, and customer). The main
manufacturing activities in industry are horizontally
involved in this system. The system also extends the
customer-satisfaction concept vertically from manufacturing to include the user and customer.
In terms of flexibility, the model provides four
flexible weights to combine performance parameters
linearly in a manner that assists management in
formulating the most suitable measurements for
several kinds of manufacturing projects/teams in
different industries. To ensure accuracy and effectiveness, and different weight combinations (r1, r2,
r3, and r4) can be set in this system.
In terms of effectiveness, the proposed model
addresses the same manufacturing performance
requirements as are found in the ISO-9000 and
QS-9000 quality-management systems. The main
purpose of these requirements is to generate
effective quality improvement. The system presented in this study provides an effective method
for measuring manufacturing quality improvement
(PCc, PCq, and PEe) with measurable, JIT aspects.
Grades for manufacturing performance can be
developed after the performance measurement. This
supports senior management in adopting suitable
strategic actions to promote manufacturing-team
Table 3
Comparison in desktop power performance measurements using two different formulas, (5) and (7), time: January–March, year: 2005
Indicator
Target
(%)
Actual value (%)
Weight of formula
(7), r1, r2,y,r4
Score using
formula example
1
Weight of formula (5), Wc,
Wq, and Wd
Score using
formula (5)
PCc
POd
PEe
Quality rate
Availability
PCq
p2.0
X98.5
X98.0
X99.0
X99.0
p6.0
2.3
98.3
98.5
99.4
99.1
9.1
0.3
0.3
0.2
—
—
0.2
0.75
0.75a
0.90b
—
—
0.65c
—
0.35
—
0.4
—
0.25
—
0.983
—
0.994
—
0.569
Total
76.0%d
88.39%e
a
On-time delivery rate ^98.5% means delinquency of delivery against customer requirements o1.5%. The calculation of achievement
level (AL) should be (98.3%98.5%)/(198.5%) 100¼13.3%—higher delinquency than target value 13.3%.
b
Similarly, Ee’s achievement level is (98.5.0%98.0%)/(198.0%) 100¼25.0%—better than target value 25.0%.
c
The calculation of Cc’s achievement level is as same as Cq’s which is (9.1%6.0%)/6.0% 100¼51.7%—worse than target value
51.7%.
d
[(0.75 0.3)+(0.75 0.3)+(0.90 0.2)+(0.65 0.2)] 100%¼76.0%.
e
[(0.983 0.35)+(0.994 0.4)+(0.569 0.25)] 100%¼88.39%.
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389
Table 4
Comparison of Telecom. power performance measurements using two different formulas, (5) and (7), time: January–March, year: 2005
Weight of formula (5), WC,
Wq, and Wd
Score using
formula (5)
0.95a
0.95b
0.90c
—
—
0.35
—
0.4
—
0.983
—
0.967
—
0.85
—
0.25
—
0.609
Indicator
Target
(%)
Actual value Weight of formula (7), r1, Score using formula
r2,y,r4
example 1
(%)
PCc
POd
PEe
Quality
rate
Availability
PCq
p5.0a
X96.0
X85.0
X95.0
2.3
98.3
91.0
96.7
0.3
0.3
0.2
—
X90.0
p10.0
94.1
9.1
—
0.2
Total
92.0%
88.31%
a
(2.3%5.0%)/5.0% 100¼54.0%.
(98.3%96.0%)/(196.0%) 100¼57.5%.
c
(91.0%85.0%)/(185.0%) 100 ¼ 40.0%.
b
performance. Applicable strategic actions can be
considered—including profit-sharing, internal promotion programs, organizational expansion projects, and so on. This depends on the willingness and
capabilities of the management and manufacturing
teams.
Acknowledgments
The authors thank the manufacturing management team from D Company for their valuable
assistance with this research project, and Prof. Sean
A. Day for proofreading the article. This study was
partly supported by the National Science Council,
Taiwan (project number: NSC 95-2622-E-238-020001-CC3).
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