International Journal of Operations & Production ManagementPerformance measurement system design: A literature review and research agendaAndy Neely Mike Gregory Ken Platts

Article information:To cite this document:Andy Neely Mike Gregory Ken Platts, (1995),"Performance measurement system design", International Journal ofOperations & Production Management, Vol. 15 Iss 4 pp. 80 - 116Permanent link to this document:http://dx.doi.org/10.1108/01443579510083622

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Performance measurementsystem design

A literature review and research agendaAndy Neely, Mike Gregory and Ken Platts

Manufacturing Engineering Group, University of Cambridge, UK

IntroductionWhen you can measure what you are speaking about, and express it in numbers, you knowsomething about it…[otherwise] your knowledge is of a meagre and unsatisfactory kind; itmay be the beginning of knowledge, but you have scarcely in thought advanced to the stage ofscience. (Lord Kelvin, 1824-1907).

Performance measurement is a topic which is often discussed but rarelydefined. Literally it is the process of quantifying action, where measurement isthe process of quantification and action leads to performance. According to themarketing perspective, organizations achieve their goals, that is they perform,by satisfying their customers with greater efficiency and effectiveness thantheir competitors[1]. The terms efficiency and effectiveness are used precisely inthis context. Effectiveness refers to the extent to which customer requirementsare met, while efficiency is a measure of how economically the firm’s resourcesare utilized when providing a given level of customer satisfaction. This is animportant point because it not only identifies two fundamental dimensions ofperformance, but also highlights the fact that there can be internal as well asexternal reasons for pursuing specific courses of action[2]. Take, for example,one of the quality-related dimensions of performance – product reliability. Interms of effectiveness, achieving a higher level of product reliability might leadto greater customer satisfaction. In terms of efficiency, it might reduce the costsincurred by the business through decreased field failure and warranty claims.Hence the level of performance a business attains is a function of the efficiencyand effectiveness of the actions it undertakes, and thus:

● Performance measurement can be defined as the process of quantifyingthe efficiency and effectiveness of action.

● A performance measure can be defined as a metric used to quantify theefficiency and/or effectiveness of an action[3].

This paper was produced during the research project – Manufacturing Strategy andPerformance Measurement – which was sponsored by the ACME Directorate of SERC undergrant number GR/H 21470.

International Journal of Operations& Production Management, Vol. 15No. 4, 1995, pp. 80-116. © MCBUniversity Press, 0144-3577

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● A performance measurement system can be defined as the set of metricsused to quantify both the efficiency and effectiveness of actions[4,5].

Even with these definitions performance measurement remains a broad topic.Hence this article will focus on the issues associated with the design ofperformance measurement systems, rather than the detail of specific measures.

The remainder of the article has been split into four main sections. It isstructured around the framework shown in Figure 1, which highlights the factthat a performance measurement system can be examined at three differentlevels:

(1) the individual performance measures;

(2) the set of performance measures – the performance measurementsystem as an entity; and

(3) the relationship between the performance measurement system and theenvironment within which it operates.

As an example of this, take the “set of measures” shown in Table I, which areused by a leading US supplier of computer-based information-processingequipment[6]. At the level of the individual measure, this “performancemeasurement system” can be analysed by asking questions such as:

● What performance measures are used?

● What are they used for?

● How much do they cost?

● What benefit do they provide?

Figure 1.A framework for

performancemeasurement system

design

Individualmeasures

Individualmeasures

Individualmeasures

Individualmeasures

Performancemeasurement

system

Theenvironment

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At the next higher level, the system can be analysed by exploring issues suchas:

● Have all the appropriate elements (internal, external, financial, non-financial) been covered?

● Have measures which relate to the rate of improvement been introduced?● Have measures which relate to both the long- and short-term objectives

of the business been introduced?● Have the measures been integrated, both vertically and horizontally?● Do any of the measures conflict with one another?

And at the highest level, the system can be analysed by assessing:● whether the measures reinforce the firm’s strategies;● whether the measures match the organization’s culture;

Table I.Typical monthlyperformance measures

Category of measure Measures used

Shipments ActualPerformance-to-build planCurrent backlog

Inventories Total (weeks and $)ScrapExcessObsolete

Variances Purchase priceProduction burdenMaterials acquisitionMaterials burdenMaterials usageLabour

Labour performance EfficiencyUtilizationProductivityOverhead percentageOvertimeAbsenteeismIndirect: direct radio

Capital AppropriationsExpenditures

Spending Salaries and benefitsControllable expensesNon-controllable expenses

Headcount DirectIndirectTotalBy functional areas

(Adapted from [6])

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● whether the measures are consistent with the existing recognition andreward structure;

● whether some measures focus on customer satisfaction;

● whether some measures focus on what the competition is doing.

In the next three sections the issues surrounding these, and similar questions,will be explored more fully. In the fourth, the implications of this review will besummarized in the form of a research agenda.

Individual measures of performanceAs Figure 1 shows, all performance measurement systems consist of a numberof individual performance measures. There are various ways in which theseperformance measures can be categorized, ranging from Kaplan andNorton’s[7] balanced scorecard through to Fitzgerald et al.’s[8] framework ofresults and determinants. The rationale underlying this article is thatperformance measures need to be positioned in a strategic context, as theyinfluence what people do. Measurement may be the “process of quantification”,but its affect is to stimulate action, and as Mintzberg[9] has pointed out, it isonly through consistency of action that strategies are realized.

Following their review of the manufacturing strategy literature, Leong etal.[10] claim that it is widely accepted that the manufacturing task, and hencethe key dimensions of manufacturing’s performance, can be defined in terms ofquality, delivery speed, delivery reliability, price (cost), and flexibility. Despitethis assertion, however, confusion still exists over what these generic termsactually mean. Wheelwright[11], for example, uses flexibility in the context ofvarying production volumes, while Tunälv[12] uses it to refer to a firm’s abilityto introduce new products rapidly. And, as shown in Table II, other authorssuch as Garvin[13], Schonberger[14], Stalk[15], Gerwin[16], and Slack[17] haveall pointed out that the generic terms quality, time[18] cost and flexibilityencompass a variety of different dimensions[19]. It would be impractical, then,

Table II.The multiple

dimensions of quality,time, cost and

flexibility

Quality Time FlexibilityQ1: Performance T1: Manufacturing lead time F1: Material qualityQ2: Features T2: Rate of production introduction F2: Output qualityQ3: Realiability T3: Deliver lead time F3: New productQ4: Conformance T4: Due-date performance F4: Modify productQ5: Technical durability T5: Frequency of delivery F5: DeliverabilityQ6: Serviceability F6: VolumeQ7: Aesthetics Cost F7: MixQ8: Perceived quality C1: Manufacturing cost F8: Resource mixQ9: Humanity C2: Value addedQ0: Value C3: Selling price

C4: Running costC5: Service cost

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to review all the possible measures of manufacturing’s performance in thisarticle. Hence only a selection of the most important measures relating toquality, time, cost and flexibility will be discussed.

One of the problems with the performance measurement literature is that it isdiverse. This means that individual authors have tended to focus on differentaspects of performance measurement system design. Business strategists andorganizational behaviourists, for example, have explored the rationaleunderlying the use of performance measures more fully than the productionand operations management community, and as this is an important part of theperformance measurement system design process, it will also be explored inthis section.

One of the techniques used to gather data for this review was a survey whichexamined the use of performance measures in UK SMEs – small and medium-sized manufacturing enterprises[20,21]. A key finding of that survey was thatthe cost of measurement is an issue of great concern to managers in SMEs.Indeed in the free-form section of the questionnaire one of the respondentswrote:

For small and medium sized companies often the best justification is “feel”, even when thenumbers don’t add up. Measurement is a luxury – success and failure are obvious.

To date, however, the authors have been unable to identify any studies whichhave explored how the cost-benefit relationship of performance measures canbe analysed.

Performance measures relating to qualityTraditionally quality has been defined in terms of conformance to specificationand hence quality-based measures of performance have focused on issues suchas the number of defects produced and the cost of quality. Feigenbaum[22] wasthe first to suggest that the true cost of quality is a function of the prevention,appraisal and failure costs. Campanella and Corcoran[23] offer the following asdefinitions of these three types of cost:

Prevention costs are those costs expended in an effort to prevent discrepancies, such as thecosts of quality planning, supplier quality surveys, and training programmes;

Appraisal costs are those costs expended in the evaluation of product quality and in thedetection of discrepancies, such as the costs of inspection, test, and calibration control;

Failure costs are those costs expended as a result of discrepancies, and are usually dividedinto two types:

● Internal failure costs are costs resulting from discrepancies found prior to delivery of theproduct to the customer, such as the costs of rework, scrap, and material review;

● External failure costs are costs resulting from discrepancies found after delivery of theproduct to the customer, such as the costs associated with the processing of customercomplaints, customer returns, field services, and warranties.

Crosby’s assertion[24] that “quality is free” is based on the assumption that, formost firms, an increase in prevention costs will be more than offset by adecrease in failure costs. Basically, the logic underlying the cost of quality

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literature is that for a given set of organizational conditions there is an optimallevel of quality. The cost of quality is a measure of the extra cost incurred by theorganization because it is either under- or over-performing. It is commonlyargued that this can be as much as 20 per cent of net sales[23].

Plunkett and Dale[25] point out that, although conceptually appealing, theacademic rigour of the cost of quality model is debatable. It is based onassumptions and estimates, rather than on data. And like the economic orderquantity (EOQ) model, it is questionable whether an optimum level of qualityreally exists. More relevant for performance measurement system design,however, is the point made by Crosby[26]. He says that many companies fail tointegrate the cost of quality model with their management process. That is,although managers estimate the cost of quality they fail to take appropriateactions to reduce it.

With the advent of total quality management (TQM) the emphasis has shiftedaway from “conformance to specification” and towards customer satisfaction.As a result the use of customer opinion surveys and market research hasbecome more widespread. The establishment of the Malcolm Baldrige NationalQuality Award in the USA and the European Quality Award reflects this trend.Table III (adapted from[27])shows how entrants for the 1991 Baldrige Awardwere judged. Note how the strongest weighting was given to customersatisfaction (300 of the 1,000 points available).

Other common measures of quality include statistical process control[28,29]and the Motorola six-sigma concept. Motorola is one of the world’s leadingmanufacturers and suppliers of semiconductors. It set a corporate quality goalof achieving six-sigma capability (3.4 defects per million parts) by 1992 andrecently one 27 person unit reported its 255th consecutive week without a singledefect[30]. These last two measures of quality raise an important issue relevantto performance measurement system design because they focus on themeasurement of the process rather than the output.

Performance measures relating to timeTime has been described as both a source of competitive advantage and thefundamental measure of manufacturing performance[15,31]. Under the just-in-time (JIT) manufacturing philosophy the production or delivery of goods justtoo early or just too late is seen as waste[32]. Similarly, one of the objectives ofoptimized production technology (OPT) is the minimization of throughputtimes[33].

Galloway and Waldron[34-37] have developed a time-based costing systemknown as throughput accounting. It is based on the following threeassumptions[38]:

(1) Manufacturing units are an integrated whole whose operating costs in the short term arelargely predetermined. It is more useful and infinitely simpler to consider the entire cost,excluding material, as fixed and to call the cost the “total factory cost”.

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(2) For all businesses, profit is a function of the time taken to respond to the needs of themarket. This in turn means that profitability is inversely proportional to the level ofinventory in the system, since the response time is itself a function of all inventory.

(3) It is the rate at which a product contributes money that determines relative productprofitability. And it is the rate at which a product contributes money compared to the rateat which the factory spends it that determines absolute profitability[34].

Table III.Scoring the 1991Baldrige award

1.0 Leadership (100 points)1.1 Senior executive leadership (40)1.2 Quality values (15)1.3 Management for quality (25)1.4 Public responsibility (20

2.0 Information and analysis (70 points)2.1 Scope and management of quality data and information (20)2.2 Competitive comparisons and benchmarks (30)2.3 Analysis of quality data and information (20)

3.0 Strategic quality planning (60 points)3.1 Strategic quality planning process (35)3.2 Quality goals and plans (25)

4.0 Human resource utilization (150 points)4.1 Human resource management (20)4.2 Employee involvement (40)4.3 Quality education and training (40)4.4 Employee recognition and performance measurement (25)4.5 Employee well-being and morale (25)

5.0 Quality assurance of products and services (150 points)5.1 Design and introduction of quality products and services (35)5.2 Process quality control (20)5.3 Continuous improvement of processes (20)5.4 Quality assessment (15)5.5 Documentation (10)5.6 Business process and support service quality (20)5.7 Supplier quality (20)

6.0 Quality results (180 points)6.1 Product and service quality results (90)6.2 Business processes, operational, and supportive service quality results (50)6.3 Supplier quality results (40)

7.0 Customer satisfaction (300 points)7.1 Determining customer requirements and expectations (30)7.2 Customer relationship management (50)7.3 Customer service standards (20)7.4 Commitment to customers (15)7.5 Complaint resolution for quality improvement (25)7.6 Determining customer satisfaction (20)7.7 Customer satisfaction results (70)7.8 Customer satisfaction comparison (70)

(Adapted from [27])

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Galloway and Waldron’s philosophy is that contribution should be measured interms of the rate at which money is received rather than as an absolute. Hencethey define the key throughput accounting ratio as return per factory hourdivided by cost per factory hour, where:

Practical examples of the use of throughput accounting are still rare. One of thefirst to be published was the case of Garrett Automotive[39]. In that report itwas claimed that throughput accounting was one of the factors that helpedGarrett Automotive double its profits. Interestingly the authors also identifiedthree problems with the adoption of throughput accounting, namely:

(1) It can be difficult to identify the constraints and the bottlenecks correctly.(2) The reduction in stocks and work-in-progress stemming from the use of

throughput accounting means a short-term profit problem as feweroverheads are carried forward in the stocks. This gives a one-off profitreduction.

(3) Reduced inventories highlight problems which have been hidden foryears.

Of course, one can argue that these problems are not due to throughputaccounting. Indeed they appear to be more akin to the problems (andopportunities) that arise from an OPT implementation.

In a different context House and Price[40] recommend the use of the Hewlett-Packard return map to monitor the effectiveness of the new productdevelopment process. Figure 2 shows a sample return map. Note how itintegrates the dimensions of time and cost. House and Price argue that thereturn map can be used to provide superordinate goals which, given anappropriate organizational culture, will encourage managers from marketing,R&D and manufacturing to work together.

Fooks[41] reports that Westinghouse has used similar cost-time profiles formore than a decade. Basically the idea is that any set of business activities orprocesses can be defined as a collection of costs over time. In Westinghouse:

Vertical lines on the profile represent purchased materials and services. In a factory, they’rethe raw materials; in the office, they’re supplies, outside services, and information. Diagonallines represent work – dollars per hour of cost added over time. The slope of the line dependson the workers’ pay rates and on the duration of the work. Horizontal lines are wait times –when nothing is happening to the process but time is going by. In factories the material sits instorerooms or in the aisles; in offices, the information resides in in-baskets or electronicallyinside computers. Reducing wait times – which can approach 95 per cent of elapsed time –offers the most immediate opportunity for improvement efforts[41].

Return per factory hour = Sale price – material costTime on the key resource

Cost per factory hour =Total factory cost

Total time available on the key resource

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On an alternative tack, an interesting approach to the design of time-basedperformance measures is proposed by Azzone et al.[42]. They suggest thatcompanies that seek to employ time as a means of competitive advantageshould use the generic set of measures shown in Table IV. Note how these reflectthe efficiency (internal configuration) and effectiveness (external configuration)dimensions of performance that were discussed earlier.

Performance measures relating to costThe development of management accounting has been well documented byJohnson[43-49], among others, and his work shows that many of the

Table IV.Measures for time-based competition

Internal configuration External configuration

R&D Number of changes in Development time forEngineering time projects new products

∆ average time between twosubsequent innovations

Operations Adherence to due dates Outgoing qualityThroughput time Incoming quality Manufacturing cost

Distance travelledValue-added time (as apercentage of total time)Schedule attainment

Sales and marketing Complexity of procedures Cycle timeOrder processing lead time Size of batches of information Bid time

(Adapted from [42])

Figure 2.The Hewlett-Packardreturn map

DevelopmentInvestigation Manufacturing sales

Time (months)

Sales

Profit

Investment

Cumulative costs and revenues(millions of pounds)

Source: Adapted from [40]

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management accounting systems used today are based on assumptions thatwere made 60 years ago. Indeed Garner’s[50] review of the accounting literatureindicates that most of the so-called sophisticated cost accounting theories andpractices had been developed by 1925[51]. Take, for example, return oninvestment (ROI). In 1903 the three DuPont cousins decided to adopt adecentralized structure for their diverse business interests and hence had toface the problem of how they could control them.

Pierre DuPont rejected the [then] widely used measure of profits or earnings as a percentageof sales or costs, because it failed to indicate the rate of return on capital invested[51].

Instead, DuPont developed the accounting measure return on investment andused this to assess both the efficiency of each business unit, and DuPont’ssuccess as a whole[44,52]. ROI is still commonly used for the same purposetoday, but it is now widely recognized that it often induces short-termism[53].

Johnson and Kaplan’s[54] thesis is that because the business environment haschanged dramatically in the last 60 years, management accounting is based onassumptions which are no longer valid. One of the most widely criticizedpractices is the allocation of indirect labour and overhead according to the directlabour cost[54]. In the early 1900s direct labour made up the majority of the fullproduct cost. Hence it made sense to allocate overheads to products accordingto their direct labour content. With the increasing use of advancedmanufacturing technologies, however, direct labour cost now typically accountsfor only 10-20 per cent of the full product cost[55], while overhead constitutes30-40 per cent[56]. This leads to overhead burdens of between 400 and 1,000 percent and hence even a relatively small change in a product’s direct labourcontent can have a massive impact on its cost structure. Furthermore, thepractice of allocating overheads according to direct labour hours encouragesmanagers to concentrate on trying to minimize the number of direct labourhours attributed to their cost centre, while ignoring overhead. Johnson andKaplan[54] argue that these problems are likely to become more severe in thefuture as product life cycles become shorter and hence an ever increasingproportion of the full product cost will take the form of research anddevelopment overhead.

It should be noted that there is not universal agreement within the accountingfraternity that Johnson and Kaplan[54] are correct. In 1988 the UK’s CharteredInstitute of Management Accountants (CIMA) instigated a study designed toexplore what criticisms were then being levelled at management accounting inthe USA. Following their investigation, Bromwich and Bhimani[57] reportedthat the perception at the time was that management accounting systems had ashort-term orientation, lacked a strategic focus, relied on redundantassumptions concerning the manufacturing process, and were too often used toprovide data for external financial reporting rather than that which wasnecessary for managing the business. In their conclusion, however, Bromwichand Bhimani contend that contrary to the suggestions of, among others,Johnson and Kaplan:

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The evidence and arguments advanced by advocates of wholesale changes in managementaccounting are not yet sufficient to justify the wholesale revision of management accountingin the UK[57].

Murphy and Braund[56] support this thesis and question the academic rigourof some of the data with which Kaplan supports his position.

As a result of the criticisms levelled at traditional management accountingCooper[58-63] developed an approach known as activity-based costing (ABC).Jeans and Morrow[64] argue that ABC overcomes many of managementaccounting’s traditional problems, such as:

● Management accounting has become distorted by the needs of financialreporting; in particular, costing systems are driven by the need to valuestock, rather than to provide meaningful product costs.

● Direct labour has shrunk as a percentage of total cost for the majority ofmanufacturing companies, yet it is still by far the most common basis ofloading overheads onto products.

● Overhead costs are no longer a mere burden to be minimized. Overheadfunctions such as product design, quality control, customer service,production planning and sales order processing are as important to thecustomer as are the physical processes on the shopfloor.

● Complexity has increased. Production processes are more complex,product ranges wider, product life cycles shorter, quality higher.

● The marketplace is more competitive. Global competition is a reality inmost sectors. Every business should be able to assess the trueprofitability of the sectors it trades in, understand product costs andknow what drives overhead. The cost management systems shouldsupport process improvement and the performance measures should bein line with strategic and commercial objectives.

In 1985 Miller and Vollmann[65] pointed out that while many managers focuson the visible costs, e.g. direct labour, direct material, etc., the majority ofoverheads are caused by the “invisible” transaction costs. Cooper appears tohave based his early work on this paper and the central assumption underlyingABC is that it is activities, and not products, that cause cost:

In an activity-based costing system, the cost of a product is the sum of all activities requiredto manufacture and deliver the product[60].

Troxel and Weber[66] say that ABC is not really a new concept and they suggestthat it has undergone three phases of development. In the first, ABC was notformally recognized. It was merely seen as an advanced version of traditionalcost accounting and indeed there is evidence to suggest that some firms wereusing ABC in the late 1960s. In the second phase of its development, which wasled by academics such as Cooper, ABC became recognized as a costing systemin its own right, but many practitioners were put off by the thought that theymight have to scrap their existing cost accounting system in order to introduce

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it. Troxel and Weber[66] argue that it was not until phase three that it wasfinally recognized that ABC was not an alternative to traditional costaccounting, but that it could be used as part of the strategic decision-makingprocess. Hence they agree with Kaplan’s[67] assertion that there should be adisconnection between external financial reporting and the systems used togather information for strategic decision making.

Despite the fact that ABC is seen by many US firms as a low payoff activityit has been subject to only a small amount of criticism[68]. Piper andWalley[69,70] question whether the assumption that activities cause cost isvalid. They point out that the claim that ABC provides more accurate productcosts has, first, never been proven and second, is based on a comparison withtraditional cost accounting systems, rather than more modern ones such ascontribution costing. Allen[71] pursues a similar theme when he points out thatABC bases its analysis on the existing cost structure of a firm. Hence it does notencourage managers think radically and examine whether their businessprocesses can be redesigned. Other problems with ABC include the fact that itignores opportunity costs[72] and that most of the data required has to beestimated[73]. Currently the view in the ascendancy appears to be that ABC isbest used as a means of analysis on a one-off basis[66]. Indeed, a KPMGconsultant recently told one of the authors that in his experience the greatestbenefit of ABC was that it forced managers to think about how they couldreduce the hidden or “invisible” cost of transaction.

For the purpose of this article, then, it is sufficient to note that ABC wasoriginally driven by the need to generate more accurate product costs. Recently,however, it has become increasingly apparent that ABC’s benefit is largely afunction of process analysis[74]. This links in with the concept of businessprocess redesign which involves looking at information which flows across,rather than down through, an organization. For the interested reader a fullerreview of the ABC literature is provided by Innes and Mitchell[75,76].

Another widely documented cost-based performance measure isproductivity. This is conventionally defined as the ratio of total output to totalinput[77]. Hence:

Productivity is a measure of how well resources are combined and used to accomplish specific,desirable results[78].

Ruch[79] has pointed out that higher productivity can be achieved in a numberof ways, including:

● increasing the level of output faster than that of the input (managedgrowth);

● producing more output with the same level of input (working smarter);

● producing more output with a reduced level of input (the ideal);

● maintaining the level of output while reducing the input (greaterefficiency);

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● decreasing the level of output, but decreasing the level of input more(managed decline).

Problems arise with the measurement of productivity because it is difficult notonly to define inputs and outputs, but also to quantify them[77]. Craig andHarris[80] suggest that firms should seek to measure total, rather than partial,productivity. This point was later picked up by Hayes et al.[81] when theydescribed how firms could measure total factor productivity. For the interestedreader more detail on productivity is available in[82-88].

Performance measures relating to flexibilitySlack[89] identifies range, cost and time as dimensions of flexibility, althoughhe later modifies this model so that it includes only range and response, whererange refers to the issue of how far the manufacturing system can change andresponse focuses on the question of how rapidly and cheaply it can change[17].

Gerwin[16] observes that very little is known about the implications offlexibility for manufacturing management and suggests that “part of theproblem arises from the lack of operational measures of flexibility”. Afteridentifying various dimensions of flexibility he suggests the followingmeasures:

Mix flexibility can be measured by the number of components handled by theequipment…Where this figure is heavily influenced by short term fluctuations in marketdemand an average over a given time period can be used. However, the range of componentcharacteristics handled is probably a more sophisticated measure of mix flexibility. Amanufacturing process may handle a small number of different components but they may bevery different from each other. Another measure to consider is the ratio of the number ofcomponents processed by the equipment to the total number processed by the factory.

One possible raw measure of changeover flexibility is the number of componentsubstitutions made over a given time period. However, a correction also needs to be made herefor the degree to which the new and old component differ from each other. There may be a lowfrequency of changes but they may involve very dissimilar components. An alternativeapproach suggested by Gustavsson[90] is to calculate the ratio of the equipment investmentrelevant for the next product to the total investment.

Modification flexibility can be measured in terms of the number of design changes made ina component per time period.

Rerouting flexibility has a long-term aspect which is salient when machines are taken out ofproduction to accommodate major design changes. There is also a short-term aspect whicharises from the necessity to cope with machine shutdowns due to equipment or qualityproblems. A measure for each aspect should reflect the following mutually exclusivepossibilities: it is possible to reroute components directly affected by the stoppage; reroutingis not possible but production of other components produced by the machine ormanufacturing system continues; and all production stops. Alternatively Buzacott[91]suggested measuring rerouting flexibility by the drop in production rate when a machinestoppage occurs.

Volume flexibility needs to be considered at the aggregate level as well as at the level ofindividual components. Volume changes depend upon how high capacity limits are set andhow rigid are these limits. Flexibility can be measured in terms of the average volumefluctuations that occur over a given time period divided by the capacity limit.

Material flexibility exists when there are adjustment mechanisms at one stage of amanufacturing process which identify and then correct or adapt to variations arising

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previously. For example, in manual systems operators can locate bent metal and adjust it orproperly position it in the machines. Otherwise, quality problems and machine breakdownswill mount. Material flexibility can be measured by the extent of variations in key dimensionaland metallurgical properties handled by the equipment. Sequencing flexibility could bemeasured by the number of different sequences handled by the equipment with the lower limitbeing inviable sequence and the upper limit being random processing.

Cox[92] sees the concept of flexibility as a measure of the efficiency with whichthe manufacturing process can be changed. He focuses on the issues of product-mix and volume flexibility and argues that the measures shown in Table V canbe used to operationalize the first of these concepts.

Applications of performance measurement?Managers find it relatively easy to decide what they should be measuring. Asmentioned earlier one of the ways in which data were collected for this reviewwas through a survey of small-to-medium-sized manufacturing enterprises inthe UK. Of the respondents to that survey 69 per cent agreed or strongly agreedwith the statement “we find it easy to decide which of the financial aspects ofmanufacturing we should be measuring”, while 72 per cent agreed or stronglyagreed with the statement “we find it easy to decide which of the non-financialaspects of manufacturing (quality, lead times, etc.) we should be measuring”.Indeed it could be argued that managers find it too easy to decide what theyshould be measuring. When, for example, four senior managers were recentlyasked to identify what they thought should be measured in their firm theyidentified over 100 different measures. The problem facing this particularcompany, then, is not identifying what could be measured, but reducing the listof possible measures to a manageable set. One way of doing this might be to

Table V.Measures of product-

mix

Element Measure(s)

Vendor lead time Percentage which can be obtained in X days or lessLabour: job classes 100 less the number of job classesLabour: cross training Percentage of workforce trained to do two or more jobsLabour: transference Percentage of workforce doing more than one production job in

any given monthSet-up time Percentage of equipment changed over in X minutes or lessCycle time Make time divided by total time in systemProgrammable equipment Percentage of equipment programmableMultipurpose equipment Percentage of equipment with multiple versus single productLot size Percentage of products for which economic lot size is smaller than X“Pull” production Percentage of product made under kanban or a similar systemWIP inventory Work-on-station divided by total work on floor(Adapted from [92])

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explore the rationale underlying the introduction of specific measures ofperformance.

In the manufacturing literature it is frequently argued that performancemeasures should be derived from strategy; that is, they should be used toreinforce the importance of certain strategic variables[93]. And although thisdoes not always appear to happen in reality[21], the link between performancemeasurement and strategy has been extensively explored in the businessstrategy literature. Many of the early writers on strategy, such as Andrews[94]and Ansoff[95], believed that strategies were synonymous with plans. In reality,however, strategies are much more complex because they evolve as decisionsare made and courses of action are pursued[9]. Take, for example, Nissan,where the espoused business strategy is “to build profitably the highest qualitycar sold in Europe”[96]. If Nissan’s purchasing manager were to decideindependently to buy low-cost, low-quality components then Nissan could endup following a strategy radically different to the one it had planned to adopt.

The key theme here is consistency – consistency of both decision making andaction – because a strategy is only realized as decisions are made and coursesof action are pursued. Indeed, it has been argued that a strategy can only besaid to exist when one can identify a consistent pattern of decisions and actionwithin a firm[9]. Hence an important question is how can one induceconsistency of decision making and action within a firm?

Hrebiniak and Joyce[97] argue that as humans are “calculative receptors” astrategic control system can be used to influence their behaviour. The processstarts with the receipt of a stimulus; an external crisis, the normal job/taskdemands, or a request from a supervisor. Calculative receptors interpret thestimulus, assess the perceived costs and benefits of various responses and arelikely to select whichever course of action they believe will maximize their gain.Control, which in this context includes performance measurement andfeedback, follows action. Finally, rewards or sanctions are used to reinforce ormodify behaviour depending on the employee’s performance and on theappropriateness of the course of action pursued. Hence to business strategiststhe rationale underlying the introduction of a performance measure is that it isone element of a strategic control system and can be used to influencebehaviour.

Despite the academic interest in strategic control systems, there has beenrelatively little empirical research on their use[98]. In 1979 Horovitz surveyed 52European companies and found little evidence to suggest that firms actuallyuse strategic controls. He does, however, describe how one firm used theirperformance measurement system to reinforce the importance of customerservice:

In one British electronics company investigated, top management has in fact put emphasis ona critical element in control. Whereas usual performance (i.e. financial results) is only reportedevery quarter, top management closely monitors – daily if needed – customer satisfaction,explicitly defined to be its major strategic strength: no equipment sold to a customer shall bedown for more than 12 hours. To check on this, every morning and afternoon the chiefexecutive is warned when any equipment has been down for more than 12 hours and

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corrective action is immediately taken at the highest level to replace or send a part to thecustomer. A systematic procedure has been set up whereby a service man unable to repairequipment within 2 hours notifies his superior, who in turn notifies his superior after 2 morehours (and so on up to the chief executive) in order to allow close control over what has beendefined as a distinctive competence by the company: no down time whatever the costs[99].

More recently Goold and Quinn[100] surveyed 200 of the largest Britishcompanies and report that only 11 per cent of them claimed to have a strategiccontrol system. Interestingly these findings can be contrasted with those ofDaniel and Reitsperger[101]. They surveyed 26 Japanese automotive andconsumer electronics firms and found that:

Japanese firms have taken to heart the strategic management literature advocating strategiccontrols…Our findings indicate that modifications of management control systems byJapanese manufacturers are applied in Japanese plants as well as in operations abroad. Thesefindings and the success of Japanese manufacturers in penetrating world markets support thenormative theory that management control systems should be modified to fit strategy (p. 616).

There is also some evidence that Japanese firms use their managementaccounting systems to influence behaviour. Hiromoto[102] argues that “high-level Japanese managers seem to worry less about whether an overheadallocation system reflects the precise demands each product makes oncorporate resources than about how the system affects the cost-reductionpriorities of middle managers and shop-floor workers”. Morgan andWeerakoon[103] concur with this view. It should be noted, however, that this isnot a particularly novel idea. Indeed as long ago as 1974 Hopwood[104]suggested that managers should pay more attention to the behaviouralimplications of management accounting.

Data on Japanese management accounting practices are relatively rare.Yoshikawa et al.[105] surveyed 200 Scottish and 500 Japanese manufacturingcompanies and found that the Japanese cost accountants placed more emphasison the generation of financial statements than did the Scottish ones. It isinteresting to compare this finding with Johnson and Kaplan’s[54] assertionthat the fact that cost accounting is driven by the need to generate financialstatements is one of its greatest weaknesses. Yoshikawa et al.[105] also foundthat Japanese companies pay more attention to product costing at the designstage. This is consistent with Sakurai’s[106] paper on target costing. He arguesthat many Japanese firms, especially those in the automotive industry, assumethat selling prices are set by the market and hence if a company knows whatreturn it wants to achieve, a target cost for the product can be established.

Simons[107-109] extends the notion that performance measures can be usedto influence behaviour. He argues that management control systems can also beused as a means of surveillance, motivation, monitoring performance,stimulating learning, sending signals or introducing constraints. These are allthemes that will be picked up again later. Before then, however, we will explorethe concept of a performance measurement system as an entity and also therelationship between the performance measurement system and theenvironment within which it operates.

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The performance measurement system as an entityThe previous section focused on the individual measures which togetherconstitute a performance measurement system. This one will adopt a slightlydifferent slant and examine the performance measurement system as a whole.It will begin by identifying the various dimensions of a performancemeasurement system. Then it will review a specific performance measurementsystem design process which has been proposed.

As discussed earlier Leong, et al.[10] have suggested that the manufacturingtask, and hence the key dimensions of manufacturing performance, can bedefined in terms of quality, time, price (cost), and flexibility. Other authors takea different stance. Following their study of performance measurement in theservice sector, Fitzgerald et al.[8] suggest that there are two basic types ofperformance measure in any organization – those that relate to results(competitiveness, financial performance), and those that focus on thedeterminants of the results (quality, flexibility, resource utilization andinnovation). This suggests that it should be possible to build a performancemeasurement framework around the concepts of results and determinants.

Perhaps the best known performance measurement framework is Kaplanand Norton’s[7] “balanced scorecard” (see Figure 3), which is based on theprinciple that a performance measurement system should provide managerswith sufficient information to address the following questions:

● How do we look to our shareholders (financial perspective)?

● What must we excel at (internal business perspective)?

● How do our customers see us (customer perspective)?

● How can we continue to improve and create value (innovation andlearning perspective)?

Figure 3.The balanced scorecard

Financial perspective

How do we look toour shareholders?

Innovation andlearning perspective

Can we continue toimprove and create value?

Customer perspective

How do ourcustomers see us?

Internal businessperspective

What must we excel at?

Source: Adapted from [7]

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Consultants from KPMG claim to have used the balanced scorecardsuccessfully both internally and with a number of their clients, but while itprovides a useful framework there is little underlying it, in terms of the processof performance measurement system design[110,111]. In addition, the balancedscorecard contains a serious flaw because if a manager were to introduce a setof measures based solely on it, he would not be able to answer one of the mostfundamental questions of all – what are our competitors doing (the competitorperspective)?

Keegan et al.[112] proposed a similar, but lesser known performancemeasurement framework – the performance measurement matrix. As with thebalanced scorecard, its strength lies in the way it seeks to integrate differentdimensions of performance, and the fact that it employs the generic terms“internal”, “external”, “cost” and “non-cost” enhances its flexibility. That is, theperformance measurement matrix should be able to accommodate any measurewhich fits within the framework provided by the balanced scorecard, while theconverse – take, for example, competitor performance – may not be true.

Rather than proposing frameworks, other authors prefer to provide criteriafor performance measurement system design. Globerson[113], for example,suggests that the following guidelines can be used to select a preferred set ofperformance criteria:

● Performance criteria must be chosen from the company’s objectives.

● Performance criteria must make possible the comparison oforganizations which are in the same business.

● The purpose of each performance criterion must be clear.

● Data collection and methods of calculating the performance criterionmust be clearly defined.

● Ratio-based performance criteria are preferred to absolute number.

● Performance criteria should be under control of the evaluatedorganizational unit.

● Performance criteria should be selected through discussions with thepeople involved (customers, employees, managers).

● Objective performance criteria are preferable to subjective ones.

Similarly, Maskell[114] offers seven principles of performance measurementsystem design:

(1) The measures should be directly related to the firm’s manufacturingstrategy.

(2) Non-financial measures should be adopted.

(3) It should be recognized that measures vary between locations – onemeasure is not suitable for all departments or sites.

(4) It should be acknowledged that measures change as circumstances do.

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(5) The measures should be simple and easy to use.

(6) The measures should provide fast feedback.

(7) The measures should be designed so that they stimulate continuousimprovement rather than simply monitor.

In the late 1980s General Motors invested some $20 million defining a set of 62primary measures that could be applied consistently at various organizationallevels. (See Figure 4). They distinguish between measures of results, e.g. qualityand responsiveness, and measures of the process of strategy implementation(Merchent[115] and Fitzgerald et al.[8] make similar distinctions[116].) Therationale underlying this “integrated” performance measurement system is thatit should ensure that GM employees retain their focus on continuousimprovement through teamwork in the key business activities[117].

Dixon et al.[118] present an interesting structured methodology for auditingwhether a firm’s performance measurement system encourages continuousimprovement. They describe a performance measurement questionnaire (PMQ)which consists of three stages. In the first, general data on both the companyand respondent are collected. In the second, the respondent is asked to identifythose areas of improvement that are of long-term importance to the firm and tosay whether the current performance measurement system inhibits or supportsappropriate activity. In the third, the respondent is asked to compare andcontrast what is currently most important for the firm with what themeasurement system emphasizes.

The data are collected using seven-point Likert scales and then four types ofanalysis are conducted. The first is alignment analysis in which the extent ofmatch between the firm’s strategies, actions and measures is assessed. The

Figure 4.General Motors’integrated performancemeasurement system

8

8

12

11

8

5

7

7

13

8

7

9

9

3

–

5

5

6

5

1

9

6

6

1

1

38

32

52

45

30

3

7

12

12

12

Productinitiation Operations

Marketingsales andservice

Retailcustomer

satisfactionShareholdersatisfaction Total

Peopledevelopment/

employeesatisfaction

Corporate

Group

Divisional/SBU

Plant/field

Department/cell

Source: Adapted from [117]

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second is congruence analysis which provides more detail on the extent towhich the strategies, actions and measures are mutually supportive. The thirdis consensus analysis, in which the data are analysed according to managementposition or function. And the fourth is confusion analysis in which the range ofresponses, and hence the level of disagreement, is examined.

Hayes and Abernathy[119] focus on a different dimension of performancemeasurement – namely the fact that many traditional measures of financialperformance encourage managers to adopt a short-term perspective. Theyhypothesize that the use of short-term financial controls may partly be to blamefor the economic decline of the USA:

Although innovation, the lifeblood of any vital enterprise, is best encouraged by anenvironment that does not unduly penalise failure, the predictable result of relying too heavilyon short-term financial measures – a sort of managerial remote control – is an environment inwhich no one feels he or she can afford a failure or even a momentary dip in the bottomline[119].

One of the most comprehensive studies of short-termism is the one reported byBanks and Wheelwright[53]. They conducted a series of in-depth interviewswith managers and planners in six major US firms and found that short-termism encouraged managers to delay capital outlays; postpone operatingexpenses; reduce operating expenses; and make other operating changes suchas varying the product mix, the delivery schedules, or the pricing strategy.Furthermore, they suggest that one of the ways in which short-termism can beminimized is by establishing performance measures which reflect both theshort and long term. This recommendation is supported by Kaplan[120].

Another issue that has to be considered when designing a performancemeasurement system is conflict, as illustrated by Fry and Cox[121]. They citethe case of a firm where the plant manager was primarily concerned with returnon investment, the product group managers were evaluated according to thenumber of orders that were shipped on time, and the supervisors and operativeswere measured according to standard hours produced. This measurementsystem encouraged the supervisors and operatives to save set-up time byproducing batches larger than those scheduled. Hence some orders weredelayed and the product group managers had to sanction overtime to ensuregood due-date performance. This, in turn, had a negative impact on the plant’smanagers performance which was measured by return on investment.

Finally, the perspective adopted by Computer Aided Manufacturing –International (CAM-I) provides an interesting insight. CAM-I is a consortium ofindustrialists and academics which sponsors, among other things, a researchproject on cost management systems (CMS):

The goal of a cost management system is to provide information to help companies useresources profitably to produce services or products that are competitive in terms of cost,quality, functionality, and timing in the world market. Within this context a cost managementsystem can be defined as a management planning and control system with the followingobjectives:

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● To identify the cost of resources consumed in performing significant activities of the firm(accounting models and practices);

● To determine the efficiency and effectiveness of the activities performed (performancemeasurement);

● To identify and evaluate new activities that can improve the future performance of thefirm (investment management).

● To accomplish the three previous objectives in an environment characterised bychanging technology (manufacturing practices)[122].

CAM-I projects tend to adopt an activity-based approach. That is, they focus onthe process of doing something, rather than the output. As discussed earlier,this has implications for the performance measurement system design taskbecause, as Deming[123] and others have argued, gaining control over aprocess, and hence producing things right first time, is probably more costeffective than repairing things after the event.

This section has sought to illustrate the complexity of the performancemeasurement system. To attempt to produce a single unifying framework atthis stage seems unrealistic. Indeed as Blenkinsop and Davis[124] report onehas to consider all of the following when designing a performancemeasurement system:

● Departmental goal-setting without creating inconsistencies in policy orexcessive interdepartmental conflict.

● Whether the measure is a valid indicator of the performance of thegroup.

● An appropriate mix of integration and differentiation (i.e. goals set bothhorizontally and vertically within the framework of the organizationalchart).

● A thorough understanding of the existing measurement systems, bothformal and informal, spoken and unspoken, as they are perceived.

● Management consensus concerning the organization’s objectives and themeans at its disposal for attaining them.

● The corporate culture.

● Long-, short- and medium-term goals (both financial and non-financial),not a fixation with “this month’s” sales figure.

● Part-ownership of problems – so that a solution has to be found acrossfunctional boundaries and the escape route, “it’s somebody else’s fault”(often the ethereal “company’s” fault), no longer has any meaning orvalidation.

● Total commitment from all involved, so that the “end-of-the-month”syndrome – a system driven by sales value – does not rear its ugly headat the end of the first month following implementation and each andevery subsequent month thereafter.

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The authors are currently involved in a project which adopts the view that thebest way to overcome this complexity is by producing a process for designing ameasurement system, rather than a framework. Work on this is rare. Indeed, theonly only examples of processes for the design of performance measurementsystems that the authors have been able to identify consist of a set of steps withlittle underlying content. The work of Wisner and Fawcett[125] is typical. Theypropose the following nine-step “process” for developing a performancemeasurement system, but make no attempt to explain how it can beoperationalized:

(1) Clearly define the firm’s mission statement.

(2) Identify the firm’s strategic objectives using the mission statement as aguide (profitability, market share, quality, cost, flexibility, dependability,and innovation).

(3) Develop an understanding of each functional area’s role in achieving thevarious strategic objectives.

(4) For each functional area, develop global performance measures capableof defining the firm’s overall competitive position to top management.

(5) Communicate strategic objectives and performance goal to lower levelsin the organization. Establish more specific performance criteria at eachlevel.

(6) Assure consistency with strategic objectives among the performancecriteria used at each level.

(7) Assure the compatibility of performance measures used in all functionalareas.

(8) Use the performance measurement system to identify competitiveposition, locate problem areas, assist the firm in updating strategicobjectives and making tactical decisions to achieve these objectives, andsupply feedback after the decisions are implemented.

(9) Periodically re-evaluate the appropriateness of the establishedperformance measurement system in view of the current competitiveenvironment.

The performance measurement system and its environmentOnce a performance measurement system has been developed it has to beimplemented. Among other things this means that the performancemeasurement system will have to interact with a wider environment. There aretwo fundamental dimensions to this environment. The first is the internal one –that is the organization. The second is the external one – that is the marketwithin which the organization competes. These will be discussed in turn in thissection.

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The internal environmentEarlier the concept of a strategic control system was introduced. There theperformance measurement system is seen as but a part of a wider systemwhich includes goal setting, feedback, and reward or sanction. Businessstrategists argue that the wider system has to match the businessstrategy[97,126]. Organizational culturists, such as Weick[127] suggest thatstrategies and cultures are synonymous. Hence one can argue that theperformance measurement system has to be consistent with the organization’sculture. Indeed it is easy to imagine what would happen in a culture based onblame if, for example, one were to introduce a measure of the number of defectsproduced by each operative. The culture of the organization would encourageeveryone to lie. Hence one can question whether there would be any point inintroducing such a measure.

Another dimension to the problem arises because of the functional structureadopted by many organizations. Shapiro[128] discusses the conflict betweenmarketing and manufacturing and suggests that it is partly a result of theevaluation and reward systems used in many firms.

One prime reason for the marketing/manufacturing conflict is that the two functions areevaluated on the basis of different criteria and receive rewards for different activities. On theone hand, the marketing people are judged on the basis of profitable growth of the company interms of sales, market share, and new markets entered. Unfortunately, the marketers aresometimes more sales-oriented than profit-oriented. On the other hand, the manufacturingpeople are often evaluated on running a smooth operation at minimum cost. Similarlyunfortunately, they are sometimes more cost-oriented than profit-oriented.

The system of evaluation and reward means that the marketers are encouraged to generatechange, which is one hallmark of the competitive marketplace. To be rewarded, they mustgenerate new products, enter new markets, and develop new programmes. But themanufacturing people are clearly rewarded for accepting change only when it significantlylowers their costs.

Because the marketers and manufacturers both want to be evaluated positively andrewarded well, each function responds as the system asks it to in order to protect its self-interest[128, p. 108].

At a higher level, KPMG, a UK management consultancy, reports that“increasing numbers of executive directors of KPMG client companies expressconcern that the information they receive neither enables them to measureperformance against their chosen strategy and objectives, nor helps them intheir strategic-decision making process. The common complaints are of toomuch data and too little analysis”. More specifically, and following a survey of150 of The Times 1,000 companies, excluding the top 200, KPMG found that:

● Most companies used targets and performance indicators based oninternal financial standards. External comparisons and non-financialtargets were not widely used.

● Information used to monitor performance was rated poor or average byjust under half the companies contacted in terms of its relevance,accuracy, timeliness, completeness, cost-effectiveness and presentation.Dissatisfaction appeared to be most marked in the cost-effectiveness andpresentation of information.

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● A majority of the respondents rated the information available toformulate and review strategy as poor or average, while 42 per cent saidthat at times it failed to highlight critical issues. Strategic planners wereless satisfied than their accounting colleagues with the relevance,timeliness and completeness of the information that was presented.

● The primary objective of most of the companies surveyed was industryleadership, achievement of target earnings per share, or a certain marketshare. Achievement of these objectives was measured by financialcriteria in three-quarters of the organizations.

● Internal information such as cost profiles, product profitability and pastfinancial performance appeared to dominate the information set.External information, such as domestic and overseas macroeconomicdata, demographic projections, EC policies and impending legislationwas not reported as being widely used in strategy formulation ormonitoring[129].

In one of the more academic studies of performance measurement Richardsonand Gordon[130] adopt an entirely different perspective and explore whetherperformance measures change as products move through their life cycle andhence begin to compete on different factors. At the outset of their study theyhypothesized that:

● as products move through their life cycle, the appropriate performancemeasures will change;

● performance measures will be easier to develop for products late in theirlife cycle as these tend to compete on cost rather than innovativeness;

● dysfunctional consequences will result if measures are not appropriate;● in multi-product facilities “traditional” measures will inhibit innovation;● manufacturing managers will respond to their perceived measures of

performance.Having collected data during interviews with the chief executive andmanufacturing managers of 15 Canadian electronics companies, Richardsonand Gordon[130] observed that:

● the performance measures used did not change as products movedthrough their life cycle;

● the measures did not become better defined later in the products’ lifecycle, primarily because the performance measures used tended to focuson the plant as a whole, rather than individual products;

● inappropriate measures did introduce dsyfunctionality;● traditional measures did inhibit innovation;● managers did respond to their perceived measures of performance.

Yet another perspective is adopted by Crawford and Cox[131]. They suggestthat instead of trying to link measures to the manufacturing strategy, the

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organization’s culture or the product life cycle, one should seek to integrate themeasures and the manufacturing system. They therefore conducted a series ofcase studies and suggest that the following guidelines can be used to design aperformance measurement system suitable for a just-in-time (JIT)manufacturing environment:

● Performance to schedule criteria should evaluate group, not individual,work.

● Specific numeric standards, or goals, should be established for theperformance to schedule criteria and these goals should be revised oncethey have been met.

● Specific numeric standards are not required for inventory and qualitycriteria; improving trends are needed.

● Performance criteria should be measured in ways that are easilyunderstood by those whose performance is being evaluated.

● Performance data should be collected, where possible, by those whoseperformance is being evaluated.

● Graphs should be the primary method of reporting performance data.

● Performance data should be available for constant review.

● Schedule performance should be reported daily or weekly.

● A monthly reporting cycle for inventory performance and qualityperformance is sufficient.

● The reporting system should not replace frequently held performancereview meetings.

The external environmentFor the purposes of this review the external environment is assumed to consistof two distinct elements – customers and competitors. As discussed earlier atruly balanced performance measurement system would provide managerswith information relating to both of these. Measures of customer satisfactionhave already been discussed (see performance measures relating to quality),hence this section will focus on the measurement of competitor performance.One technique that can be used to do this is benchmarking, althoughbenchmarking does not have to focus on competitors. Indeed there are fourbasic types of benchmarking:

(1) Internal. Internal to a corporation, but perhaps external to a plant or aparticular business unit. One of the major advantages of internalbenchmarking is that it minimises problems of access and dataconfidentiality.

(2) Competitive. This is probably the most beneficial form of benchmarking,but the collection of data which is directly comparable is very difficult.

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(3) Functional. This involves functional comparison with companies whichare similar, but not direct competitors.

(4) Generic. The study and comparison of truly generic business process,e.g. order entry, invoicing.

Benchmarking is proving to be a topic of interest to both academics andconsultants. One of the first benchmarking studies to be documented was theone carried out by Garvin[132]. He collected data on the performance of USAand Japanese air conditioning manufacturers in the early 1980s. More recentlyWomack et al.[88] have completed a study of the automotive industry. Over afive-year period they collected detailed performance data, such as that shown inTable VI, on most of the world’s major automotive plants and from these wereable to identify a two to one performance gap along a number of dimensions. In1993 Andersen Consulting[133] reported a similar finding, following their studyof automotive components suppliers. While in the USA the MIT/SloanFoundation is continuing to fund a number of industry specific benchmarkingprojects, including ones which focus on semi-conductors; textiles; computers;steel; pharmaceutical; chemical process; process equipment.

In terms of academic research Voss et al.[134-137] have produced a series ofworkbooks which discuss the process of benchmarking innovation. Theyidentify four dimensions to innovation:

(1) product innovation;(2) product development;(3) process innovation;(4) technology acquisition.

They argue that firms should seek to benchmark themselves along each ofthese dimensions.

Industrial interest in benchmarking is also high. The Britain’s Best FactoriesAward, run by Management Today, was based on a benchmarking exercise forthe first time in 1992. Each entrant filled in a self-assessment questionnairewhich covered issues such as the plant profile; the nature of the manufacturingoperations; the cost structure; the inventory profile; the employee profile;product innovation; management information; and market positioning. The

Table VI.Comparative assembly

performance data

GM Framingham Toyota Takaoka

Gross assembly hours per car 40.7 18.0Adjusted assembly hours per car 31 16Assembly defects per 100 cars 130 45Assembly space per car 8.1 4.8Inventory of parts (average) 2 weeks 2 hoursSource: [88]

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judges, all based at Cranfield School of Management, UK, analysed the data andthen visited the best plants before making a final decision. This innovationlends greater credibility to the Best Factories Award as previously thejudgement process was based on consultants’ opinions rather than on data.

Some authors see benchmarking as a means of identifying improvementopportunities as well as monitoring the performance of competitors.Young[138], for example, argues that benchmarking is being used in this wayby many large companies. He proposes that as most of the “low hanging fruithas been picked” the identification of improvement opportunities is becomingincreasingly difficult. Hence managers are adopting benchmarking as a meansof searching for best practice and new ideas. He identifies four steps in thebenchmarking process:

(1) planning;(2) analysis;(3) integration;(4) action.

Of course one danger with this approach is that the company that is searchingfor best practice will always be following rather than leading.

Perhaps the most comprehensive description of benchmarking, to date, hasbeen provided by Camp[139]. He defines benchmarking as the search forindustry best practices that lead to superior performance and bases his book onthe nine-step benchmarking process shown in Figure 5 (adapted to the nine-stepbenchmarking process for this article). In terms of performance measurementsystem design, however, the work of Oge and Dickinson[140] is perhaps morerelevant. They suggest that firms should adopt closed loop performancemanagement systems which combine periodic benchmarking with ongoingmonitoring/measurement (see Figure 6).

Implications – performance measurement research agendaThere appears to be a growing recognition that the measures of performancethat companies have traditionally used are inappropriate for manufacturingbusinesses[51,120,141,142], not least because they:

● encourage short-termism, for example the delay of capitalinvestment[53,143];

● lack strategic focus and do not provide data on quality, responsivenessand flexibility[144];

● encourage local optimization, for example manufacturing inventory tokeep people and machines busy[145];

● encourage managers to minimize the variances from standard ratherthan seek to improve continually[146,147];

● fail to provide information on what their customers want and what theircompetitors are doing[7,139].

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Figure 5.The nine-step

benchmarking process

Identify what is to be benchmarked

Identify comparative companies

Determine data collection method and collect data

Determine current performance "gap"

Project future performance levels

Communicate findings and gain acceptance

Establish functional goals

Implement specific actions and monitor progress

Recalibrate benchmarks

Source: Adapted from [139]

Figure 6.Closed loop

performancemanagement

Periodic benchmarking

Compare processesto best practices

InternalExternalAssess progress

Structure programmes to close gaps

ImplementationBest practiceelementsContinuousimprovement

Activity

Standard processEventsDeliverablesProgrammes

Process

CostQualityTime

Product

PerformanceCustomersatisfactionMarket share

Ongoing monitoring/measurement

Performance management

Source: Adapted from [140]

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Performance measurement system design, or re-design, is firmly on theindustrial agenda[148,149]. In terms of issues that need researching this reviewhas identified the following as key.

Issues associated with individual measures of performanceIssues associated with individual measures of performance include:

● Is performance measurement a luxury for the SMME (small or mediummanufacturing enterprise)?

– Which performance measures are of greatest value to SMMEs?

– Do the costs of some measures outweigh their benefit?

– Is this only an issue for SMMEs or does it apply to all sizes oforganization?

● Should measures focus on processes, the outputs of processes, or both?

● Is time the fundamental measure of manufacturing performance?

● How can the measurement of total factor productivity be simplified?

● How can flexibility, which is often simply a property of the “system”, bemeasured?

● How can performance measures be designed so that they encourageinter-functional co-operation?

● How can measures which do not encourage short-termism be designed?

● How can performance measures be designed so that they encourageappropriate behaviour?

● Can “flexible” measures which take account of the changing businessenvironment be defined?

● How should the data generated as a result of a particular measure bedisplayed?

● How can one ensure that the management loop is closed – that correctiveaction follows measurement?

Issues associated with the performance measurement system as an entityThese issues ask the questions:

● What are the “definitive” principles of performance measurementsystem design?

● How can measures be integrated both across an organization’s functionsand through its hierarchy?

● How can conflicts between performance measures be eliminated?

● What techniques can managers use to reduce their list of “possible”measures to a meaningful set?

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– Would a “generic” performance measurement system facilitate thisprocess?

– Would a performance measurement framework facilitate thisprocess?

– Or is a process-based approach required?– What are the relative advantages and disadvantages of the above?

● Do “generic” performance measurement systems actually exist?● What does a truly “balanced scorecard” constitute?● Can a practicable performance measurement system design process be

specified?● Can a “flexible” performance measurement system which takes account

of the changing business environment be defined?● How can the cost-benefit of a performance measurement system be

analysed?

Issues associated with the system and its environmentIssues associated with the system and its environment raise the followingpoints:

● What implications do emerging concepts, such as ABC, BPR or thenotion of core competencies, have for performance measurement?

● Why do firms fail to integrate their performance measures into theirstrategic control systems?

● How can we ensure that the performance measurement system matchesthe firm’s strategy and culture?

● To which dimensions of the internal and external environment does theperformance measurement system have to be matched?

The final issue, and one which has not yet been touched, is that of predictiveperformance measurement. The assumption underpinning this review, andindeed much of the work on performance measurement to date, is thatmanagers use measures both to monitor past performance and stimulate futureaction. Increasingly, however, people are beginning to look for “predictive”measures. Measures, such as statistical process control (SPC), which show thatsomething is going out of control, before too much damage has been done. Akey item on the performance measurement research agenda must therefore bethe identification, and/or development, of “predictive performance measures”.

Notes and references1. Kotler, P., Marketing Management Analysis, Planning and Control, Prentice-Hall,

Englewood Cliffs, NJ, 1984.2. Slack, N., The Manufacturing Advantage: Achieving Competitive Manufacturing

Operations, Mercury, London, 1991.

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3. This can be expressed either in terms of the actual efficiency and/or effectiveness of anaction, or in terms of the end result of that action.

4. In this context the term metric refers to more than simply the “formula” used to calculatethe measure. For a given performance measure to be specified it is necessary to define,among other things, the title of the measure, how it will be calculated (the formula), whowill be carrying out the calculation, and from where they will get the data. See Neely[5] fora fuller explanation.

5. Neely, A.D., “Performance measurement system design – third phase”, draft of the fourthsection of the “Performance Measurement System Design Workbook”, April 1994.

6. Kaplan, R.S. (Ed.), Measures for Manufacturing Excellence, Harvard Business SchoolPress, Boston, MA, 1990.

7. Kaplan, R.S. and Norton, D.P., “The balanced scorecard – measures that driveperformance”, Harvard Business Review, January-February 1992, pp. 71-9.

8. Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R. and Voss, C., PerformanceMeasurement in Service Business, CIMA, London, 1991.

9. Mintzberg, H., “Patterns in strategy formulation”, Management Science, Vol. 24 No. 9,1978, pp. 934-48.

10. Leong, G.K., Snyder, D.L. and Ward, P.T., “Research in the process and content ofmanufacturing strategy”, OMEGA International Journal of Management Science, Vol. 18No. 2, 1990, pp. 109-22.

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18. The two factors delivery speed and delivery reliability are both assumed to fall into thegeneric category time for the purpose of Figure 3. They are shown as factors T3 and T4respectively.

19. Neely, A.D. and Wilson, J.R., “Measuring product goal congruence: an exploratory study”,International Journal of Operations & Production Management, Vol. 12 No. 4, 1992, pp. 45-52.

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248. Adem Göleç, Harun Taşkın. 2007. Novel methodologies and a comparative study for manufacturing systems performanceevaluations. Information Sciences 177, 5253-5274. [CrossRef]

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258. T.F. Burgess, T.S. Ong, N.E. Shaw. 2007. Traditional or contemporary? The prevalence of performance measurement systemtypes. International Journal of Productivity and Performance Management 56:7, 583-602. [Abstract] [Full Text] [PDF]

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261. Bob Lillis, Robin Lane. 2007. Auditing the strategic role of operations. International Journal of Management Reviews9:10.1111/ijmr.2007.9.issue-3, 191-210. [CrossRef]

262. Vittorio Chiesa, Federico Frattini. 2007. Exploring the differences in performance measurement between research anddevelopment: evidence from a multiple case study. R&D Management 37:10.1111/radm.2007.37.issue-4, 283-301. [CrossRef]

263. Paul D. Cousins, Benn Lawson. 2007. The Effect of Socialization Mechanisms and Performance Measurement on SupplierIntegration in New Product Development. British Journal of Management 18:10.1111/bjom.2007.18.issue-3, 311-326.[CrossRef]

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268. Mark Stevenson, Martin Spring. 2007. Flexibility from a supply chain perspective: definition and review. International Journalof Operations & Production Management 27:7, 685-713. [Abstract] [Full Text] [PDF]

269. Veronica Martinez, Zoe Radnor, Patrizia Garengo, Giovanni Bernardi. 2007. Organizational capability in SMEs. InternationalJournal of Productivity and Performance Management 56:5/6, 518-532. [Abstract] [Full Text] [PDF]

270. Veronica Martinez, Zoe Radnor, Zoe J. Radnor, David Barnes. 2007. Historical analysis of performance measurement andmanagement in operations management. International Journal of Productivity and Performance Management 56:5/6, 384-396.[Abstract] [Full Text] [PDF]

271. Angappa Gunasekaran, Bulent Kobu. 2007. Performance measures and metrics in logistics and supply chain management:a review of recent literature (1995–2004) for research and applications. International Journal of Production Research 45,2819-2840. [CrossRef]

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274. Ana Maria Garcia Perez, Marian Garcia Martinez. 2007. The agrifood cooperative netchain. A theoretical framework to studyits configuration. Food Economics - Acta Agriculturae Scandinavica, Section C 4, 31-39. [CrossRef]

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276. Shankar Purbey, Kampan Mukherjee, Chandan Bhar. 2007. Performance measurement system for healthcare processes.International Journal of Productivity and Performance Management 56:3, 241-251. [Abstract] [Full Text] [PDF]

277. Carlos F. Gomes, Mahmoud M. Yasin, João V. Lisboa. 2007. An empirical investigation of manufacturing performancemeasures utilization. International Journal of Productivity and Performance Management 56:3, 187-204. [Abstract] [Full Text][PDF]

278. Li‐cheng Chang. 2007. The NHS performance assessment framework as a balanced scorecard approach. International Journalof Public Sector Management 20:2, 101-117. [Abstract] [Full Text] [PDF]

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280. Suzanne Bergin-Seers, Leo Jago. 2007. Performance measurement in small motels in Australia. Tourism and HospitalityResearch 7, 144-155. [CrossRef]

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283. Nizamettin Bayyurt. 2007. The Performance of Turkish Manufacturing Firms in Stable And Unstable Economic Periods.South East European Journal of Economics and Business 2. . [CrossRef]

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287. Helen Atkinson. 2006. Strategy implementation: a role for the balanced scorecard?. Management Decision 44:10, 1441-1460.[Abstract] [Full Text] [PDF]

288. Alan Pilkington, Robert Fitzgerald. 2006. Operations management themes, concepts and relationships: a forward retrospectiveof IJOPM. International Journal of Operations & Production Management 26:11, 1255-1275. [Abstract] [Full Text] [PDF]

289. Beata Kollberg, Mattias Elg. 2006. Challenges Experienced in the Development of Performance Measurement Systems inSwedish Health Care. Quality Management in Health Care 15, 244-256. [CrossRef]

290. L. Berrah, G. Mauris, F. Vernadat. 2006. Industrial performance measurement: an approach based on the aggregation ofunipolar or bipolar expressions. International Journal of Production Research 44, 4145-4158. [CrossRef]

291. Prasanta Kumar Dey, Seetharaman Hariharan, Benjamin Thomas Clegg. 2006. Measuring the operational performanceof intensive care units using the analytic hierarchy process approach. International Journal of Operations & ProductionManagement 26:8, 849-865. [Abstract] [Full Text] [PDF]

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296. Benita M. Beamon, Stephen A. Kotleba. 2006. Inventory management support systems for emergency humanitarian reliefoperations in South Sudan. The International Journal of Logistics Management 17:2, 187-212. [Abstract] [Full Text] [PDF]

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298. Jillian MacBryde, Zoe Radnor, Jairo R. Montoya‐Torres. 2006. Manufacturing performance evaluation in wafer semiconductorfactories. International Journal of Productivity and Performance Management 55:3/4, 300-310. [Abstract] [Full Text] [PDF]

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302. Andy Neely. 2005. The evolution of performance measurement research. International Journal of Operations & ProductionManagement 25:12, 1264-1277. [Abstract] [Full Text] [PDF]

303. Marc Wouters, Mark Sportel. 2005. The role of existing measures in developing and implementing performance measurementsystems. International Journal of Operations & Production Management 25:11, 1062-1082. [Abstract] [Full Text] [PDF]

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308. Krishan M. Gupta, A. Gunasekaran. 2005. Costing in new enterprise environment. Managerial Auditing Journal 20:4, 337-353.[Abstract] [Full Text] [PDF]

309. Karen Anderson, Rodney McAdam. 2005. An empirical analysis of lead benchmarking and performance measurement.International Journal of Quality & Reliability Management 22:4, 354-375. [Abstract] [Full Text] [PDF]

310. Beata Kollberg, Mattias Elg, Jan Lindmark. 2005. Design and Implementation of a Performance Measurement System inSwedish Health Care Services. Quality Management in Health Care 14, 95-111. [CrossRef]

311. I Egan, J M Ritchie, P D Gardiner. 2005. Measuring performance change in the mechanical design process arena. Proceedingsof the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 219, 851-863. [CrossRef]

312. Patrizia Garengo, Stefano Biazzo, Umit S. Bititci. 2005. Performance measurement systems in SMEs: A review for a researchagenda. International Journal of Management Reviews 7:10.1111/ijmr.2005.7.issue-1, 25-47. [CrossRef]

313. Sérgio D. Sousa, Elaine Aspinwall, Paulo A. Sampaio, A. Guimarães Rodrigues. 2005. Performance measures and quality toolsin Portuguese small and medium enterprises: survey results. Total Quality Management & Business Excellence 16, 277-307.[CrossRef]

314. Kit Fai Pun, Anthony Sydney White. 2005. A performance measurement paradigm for integrating strategy formulation:A review of systems and frameworks. International Journal of Management Reviews 7:10.1111/ijmr.2005.7.issue-1, 49-71.[CrossRef]

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316. Monica Franco-Santos *, Mike Bourne. 2005. An examination of the literature relating to issues affecting how companiesmanage through measures. Production Planning & Control 16, 114-124. [CrossRef]

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318. Shamsuddin Ahmed ., Masjuki Hj. Hassan ., Yap Hui Fen .. 2005. Performance Measurement and Evaluation in an InnovativeModern Manufacturing System. Journal of Applied Sciences 5, 385-401. [CrossRef]

319. Annabel Jackson. 2005. Falling from a Great Height: Principles of Good Practice in Performance Measurement and the Perilsof Top Down Determination of Performance Indicators. Local Government Studies 31, 21-38. [CrossRef]

320. Stefan Tangen. 2005. Demystifying productivity and performance. International Journal of Productivity and PerformanceManagement 54:1, 34-46. [Abstract] [Full Text] [PDF]

321. Petri SuomalaLIFE CYCLE PERSPECTIVE IN THE MEASUREMENT OF NEW PRODUCT DEVELOPMENTPERFORMANCE 523-700. [Abstract] [Full Text] [PDF] [PDF]

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323. Stefan Tangen. 2004. Performance measurement: from philosophy to practice. International Journal of Productivity andPerformance Management 53:8, 726-737. [Abstract] [Full Text] [PDF]

324. L. Berrah, G. Mauris *, F. Vernadat. 2004. Information aggregation in industrial performance measurement: rationales, issuesand definitions. International Journal of Production Research 42, 4271-4293. [CrossRef]

325. H'ng Gaik Chin, Muhamad Zameri Mat Saman. 2004. Proposed analysis of performance measurement for a productionsystem. Business Process Management Journal 10:5, 570-583. [Abstract] [Full Text] [PDF]

326. Chris Morgan. 2004. Structure, speed and salience: performance measurement in the supply chain. Business ProcessManagement Journal 10:5, 522-536. [Abstract] [Full Text] [PDF]

327. Denis Borenstein, João Luiz Becker, Vaner José do Prado. 2004. Measuring the efficiency of Brazilian post office stores usingdata envelopment analysis. International Journal of Operations & Production Management 24:10, 1055-1078. [Abstract] [FullText] [PDF]

328. Karen Anderson, Rodney McAdam. 2004. A critique of benchmarking and performance measurement. Benchmarking: AnInternational Journal 11:5, 465-483. [Abstract] [Full Text] [PDF]

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335. Jean‐François Henri. 2004. Performance measurement and organizational effectiveness: bridging the gap. Managerial Finance30:6, 93-123. [Abstract] [PDF]

336. Steven A Melnyk, Douglas M Stewart, Morgan Swink. 2004. Metrics and performance measurement in operationsmanagement: dealing with the metrics maze. Journal of Operations Management 22, 209-218. [CrossRef]

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338. J. Schmitz, K.W. Platts. 2004. Supplier logistics performance measurement: Indications from a study in the automotiveindustry. International Journal of Production Economics 89, 231-243. [CrossRef]

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352. Felix T.S. Chan, H.J. Qi. 2003. Feasibility of performance measurement system for supply chain: a process‐based approachand measures. Integrated Manufacturing Systems 14:3, 179-190. [Abstract] [Full Text] [PDF]

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356. Md. Mostaque Hussain, A. Gunasekaran. 2002. An institutional perspective of non‐financial management accountingmeasures: a review of the financial services industry. Managerial Auditing Journal 17:9, 518-536. [Abstract] [Full Text] [PDF]

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359. F.J. O’Donnell, A.H.B. Duffy. 2002. Modelling design development performance. International Journal of Operations &Production Management 22:11, 1198-1221. [Abstract] [Full Text] [PDF]

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365. F. T. S. Chan, H. J. Qi. 2002. A fuzzy basis channel-spanning performance measurement method for supply chainmanagement. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 216, 1155-1167.[CrossRef]

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390. CONCHA ÁLVAREZ‐DARDET, GLORIA CUEVAS‐RODRÍGUEZ, RAMÓN VALLE‐CABRERA. 2000. Value‐BasedManagement: Performance Measurement Systems for Human Resources. Journal of Human Resource Costing & Accounting5:1, 9-26. [Abstract] [PDF]

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