Operations homework

Journal of Operations Management 22 (2004) 209–217

Metrics and performance measurement in operations management: dealing with the metrics maze

Steven A. Melnyk∗, Douglas M. Stewart, Morgan Swink Department of Marketing and Supply Chain Management, The Eli Broad Graduate School of Management,

Michigan State University, East Lansing, MI 48824-1039, USA

Accepted 16 January 2004

Abstract

Metrics provide essential links between strategy, execution, and ultimate value creation. Changing competitive dynamics are placing heavy demands on conventional metrics systems, and creating stresses throughout firms and their supply chains. Research has not kept pace with these new demands in an environment where it is no longer sufficient to simply let metrics evolve over time—we must learn how to proactively design and manage them. The intent of this paper is to convey the importance and need for metrics-related research. An outline of the important characteristics of the metrics research topic is provided. Specifically, we address the functions of metrics; their focus and tense; their operational and strategic contexts; as well as discuss the distinction between metrics, metrics sets and metrics systems. Some initial theoretical grounding for the research topic is provided through agency theory. We conclude with a discussion of the intent and process of the special issue, and introduction of the associated articles. © 2004 Elsevier B.V. All rights reserved.

Keywords:Metrics; Performance measurement; Operations management

1. The metrics challenge

One of the most powerful management disciplines, the one that more than any other keeps people fo- cused and pulling in the same direction, is to make an organization’s purposes tangible. Managers do this by translating the organization’s mission—what it, particularly, exists to do—into a set of goals and performance measures that make success concrete for everyone. This is the real bottom line for every organization—whether it’s a business or a school or a hospital. Its executives must answer the question,

∗ Corresponding author. Tel.:+1-517-353-6381. E-mail address:[email protected] (S.A. Melnyk).

“Given our mission, how is our performance going to be defined?” (Magretta and Stone, 2002, p. 129)

The quote fromMagretta and Stone (2002)sug- gests that metrics and performance measurement are the critical elements in translating an organization’s mission, or strategy, into reality. Metrics and strat- egy are tightly and inevitably linked to each other. Strategy without metrics is useless; metrics without a strategy are meaningless. The importance of metrics has been long recognized. Manufacturing and man- agement consultant Oliver Wight almost 30 years ago offered the oft-repeated maxim, “You get what you inspect, not what you expect.” Every firm, every ac- tivity, every worker needs metrics. Metrics fulfill the fundamental activities of measuring (evaluating how

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210 S.A. Melnyk et al. / Journal of Operations Management 22 (2004) 209–217

we arc doing), educating (since what we measure is what is important; what we measure indicates how we intend to deliver value to our customers), and direct- ing (potential problems are flagged by the size of the gaps between the metrics and the standard). Yet, per- formance measurement continues to present a chal- lenge to operations managers as well as researchers of operations management. Operating metrics are of- ten poorly understood and guidelines for the use of metrics arc often poorly articulated.

As a focus of research, little attention has been de- voted to this topic within the field of operations man- agement. A great deal of what we currently know about metrics comes from the managerial literature (e.g.,Brown, 1996; Cooke, 2001; Dixon et al., 1990; Kaydos, 1999; Ling and Goddard, 1988; Lynch and Cross, 1995; Maskell, 1991; Melnyk and Christensen, 2000; Melnyk et al., in press; Smith, 2000; Williams, 2001). While there are numerous examples of the use of various metrics, there are relatively few studies in operations management that have focused on the development, implementation, management, use and effects of metrics within either the operations man- agement system or the supply chain. Nascent exam- ples can be found in the research ofBeaumon (1999), Leong and Ward (1995), Neely (1998), Neely et al. (1994, 1995), andNew and Szwejczewski (1995).

We should point out that topic of metrics as dis- cussed by managers differs from the topic of mea- surement as typically discussed by academics. This is primarily a byproduct of different priorities between these groups. The academic is concerned with defin- ing, adapting and validating measures to address spe- cific research questions. The time required to develop and collect the measures is of less importance than the validity and generalizability of the results beyond the original context. Managers face far greater time pressures, and are less concerned about generalizabil- ity. They are generally more than willing to use a “good enough” measure if it can provide useful in- formation quickly. However, as long as the difference in priorities is recognized there are undoubtedly many lessons academic measurement experts can contribute to managers’ understanding of metrics.

Recent indicators suggest that metrics and perfor- mance measurement are receiving more attention. In 1999, the Education and Research Foundation of APICS commissioned a research program dealing

with measuring supply chain performance. The 2002 POMS National Conference included a special session focusing on performance measurement. In late 2002, KPMG in conjunction with the University of Illinois at Champagne undertook a major research initiative aimed at funding and encouraging research in perfor- mance measurement (to the tune of US$ 2.8 million). Finally, the January 2003Harvard Business Review case study focused on the miscues and disincentives created by poorly thought out performance measure- ment systems (Kerr, 2003). Why the increasing inter- est? We believe the answer is in the business environ- ment faced by today’s operations managers. Today’s environment is characterized by: (1) “never satisfied” customers (McKenna, 1997); (2) the need to manage the “total” supply chain, rather than only internal factors; (3) shrinking product life cycles; (4) more (but not necessarily better) data; and (5) an increasing number of alternatives. These dynamics make static metrics systems obsolete, and call for new perfor- mance measures and metrics approaches that go be- yond simple reporting to create means for identifying improvement opportunities and anticipating potential problems. Further, metrics are now seen as an im- portant means by which priorities are communicated within the firm and across the supply chain. Metrics misalignment is thought to be a primary source of in- efficiency and disruption in supply chain interactions.

Given this environment, the research challenge is to better understand the roles and impacts of metrics in operating systems, and using this knowledge to de- sign metrics systems and guidelines that provide clar- ity of purpose, real-time feedback and predictive data, and insights into opportunities for improvement. In addition, these new metrics systems need to be flex- ible in recognizing and responding to changing de- mands placed on the operating system due to product churn, heterogeneous customer requirements, as well as changes in operating inputs, resources, and perfor- mance over time.

By way of introducing this special issue on perfor- mance measures and operating metrics, in the remain- ing sections of this article we:

• Identify the defining elements and different types of metrics.

• Position metrics within the operations management research environment.

S.A. Melnyk et al. / Journal of Operations Management 22 (2004) 209–217 211

• Identify the special research challenges associated with metrics.

• Introduce the articles that comprise the special is- sue.

Ultimately, the goal of this special issue is to direct, shape, and encourage research into this very important topic area.

1.1. Defining metrics—an overview

A metric is a verifiable measure, stated in either quantitative or qualitative terms and defined with re- spect to a reference point. Ideally, metrics are consis- tent with how the operation delivers value to its cus- tomers as stated in meaningful terms.

This definition identifies several critical elements. First, a metric should be verifiable, that is, it should be based on an agreed upon set of data and a well-understood and well-documented process for converting this data into the measure. Given the data and the process, independent sources should be able to arrive at the same metric value. Second, metrics are measures. They capture characteristics or out- comes in a numerical or nominal form. In order to interpret meaning from a metric, however, it must be compared to a reference point. The reference point acts as a basis of comparison, and can be an absolute standard or an internally or externally developed stan- dard. Standards can be based on past metric values or based on metric values for a comparable process (e.g., a “benchmark”). Zero defects would be an absolute standard, for example, as would be 100% utilization. An operating budget is an internally developed stan- dard, whereas environmental performance might be compared to external standards published by the Envi- ronmental Protection Agency (EPA). Because metrics are expressed relative to some reference point, they encourage comparison by the users of the metric or by external parties (as in the case of ISO 9000 auditors).

It is generally desirable that a metric be expressed in meaningful terms. If metrics are to be effective, they must be understood—they must make sense to the per- son using the metrics. In addition, metrics should be value-based. That is, a metric should be linked to how the operation delivers value to its targeted customers. Naturally, not all metrics will be directly related to customer value. Metrics may also be related to the

values of other stakeholders in the process. For exam- ple, worker safety-oriented metrics are important, but indirectly related to customer value.

2. The fundamental need for metrics

Metrics provide data refinement. As the volume of inputs increases, through greater span of control or growing complexity of an operation, data manage- ment becomes increasingly difficult. Metrics provide a means of distilling the volume of the data while si- multaneously increasing its information richness. Op- erations need these functions in order to operate ef- fectively and efficiently on a day-to-day basis.

Finally, metrics exist as tools for people. Ultimately, the actions people take and the decisions they make determine the degree and nature of value that an op- eration creates. These actions and decisions can be greatly influenced by metrics.

Metrics provide the following three basic functions:

• Control: Metrics enable managers and workers to evaluate and control the performance of the re- sources for which they are responsible.

• Communication: Metrics communicate perfor- mance not only to internal workers and managers for purposes of control, but to external stakeholders for other purposes as well (e.g., Wall Street, the EPA or to a bank). Many times stakeholders and users of metrics do not understand the workings and processes of a firm or operation, nor do they need to. Well-designed and communicated metrics provide the user a sense of knowing what needs to be done without necessarily requiring him/her to understand the intricacies of related processes. Poorly developed or implemented metrics can lead to frustration, conflict, and confusion.

• Improvement: Metrics identify gaps (between per- formance and expectation) that ideally point the way for intervention and improvement. The size of then gap and the direction of the gap (positive or nega- tive) provide information and feedback that can be used to identify productive process adjustments or other actions.

There are dynamic tensions inherent in requiring one system to perform multiple functions. One such tension stems from the desire to change metrics in re-

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sponse to new strategic priorities, and the desire to maintain metrics to allow comparison of performance over time. This tension will dictate the metrics life cycle. Moreover, as the metrics reflect underlying pri- orities and decisions, metrics-related stress between various parties is often simply the first indicator of un- observed, unresolved conflicts between the customer, strategy, and operations of the firm.

3. A metrics typology

One source of complexity regarding the study of metrics is the variety of different types of metrics that researchers and managers encounter. We suggest that various metrics can be readily classified according to two primary attributes:metrics focusand metrics tense. Metrics focus pertains to the resource that is the focus of the metric. Generally, metrics report data in either financial (monetary) or operational (e.g., oper- ational details such as lead times, inventory levels or setup times) terms. Financial metrics define the perti- nent elements in terms of monetary resource equiva- lents, whereas operational metrics tend to define ele- ments in terms of other resources (e.g., time, people) or outputs (e.g., physical units, defects). The second attribute, metrics tense, refers the how the metrics are intended to be used. Metrics can be used to both to judge outcome performance and to predict future per- formance. An outcome-oriented use of a metric im- plicitly assumes that the problems and lessons uncov- ered from a study of past outcomes can be applied to current situations. That is, by studying the past, we can improve the present. In general, managers who monitor and reward activities and associated person- nel use metrics in an outcome-based way. Executives

Scheme 1. Metrics typology.

often use metrics in this way. Many of the cost-based metrics encountered in firms belong to this category. Similarly, many of the accounting based information systems observed in many firms typically generate outcome-based metrics.

In contrast, a predictive use of a metric is aimed at increasing the chances of achieving a certain objective or goal. Predictive metrics are associated to aspects of the process that will result in the outcomes of inter- est. If our interest is in reducing lead time, then we might assess metrics such as distance covered by the process, setup times, and number of steps in the pro- cess. Reductions in one or more of these areas should be reflected in reductions in lead time. An emphasis on identifying and using metrics in a predictive way is relatively new. Predictive metrics are appropriate when the interest is in preventing the occurrence of problems, rather than correcting them.

Combining of these two metrics attributes provides four distinct types of metrics: financial/outcome, financial/predictive, operational/outcome, and oper- ational/predictive (Scheme 1). These different cat- egories appeal to different groups within the firm. Top managers, for example, are typically most in- terested in financial/outcome. In contrast, operations managers and workers are most likely interested in operational/predictive or operational/outcome metrics since these two sets pertain to the processes that these managers must manage and change.

4. Levels of metrics

The term, “metrics” is often used to refer to one of three different constructs: (1) theindividual metrics; (2) the metrics sets; and (3) the overallperformance measurement systems. These terms are often used in-

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terchangeably, thus contributing to the confusion. We suggest that recognition of the different levels of met- rics and their interactions is important for the research and design of metrics.

At the highest level, theperformance measure- ment systemlevel integrates. That is, it is respon- sible for coordinating metrics across the various functions and for aligning the metrics from the strategic (top management) to the operational (shop floor/purchasing/execution) levels. For every activity, product, function, or relationship, multiple metrics can be developed and implemented. The challenge is to design a structure to the metrics (i.e., grouping them together) and extracting an overall sense of per- formance from them (i.e., being able to address the question of “Overall, how well are we doing?”).

Several different approaches have been proposed for developing such an integrative system. These in- clude: (1) thebalanced scorecard, as presented by Kaplan and Norton (1992, 1996, 2001)and elaborated on by others (e.g.,Ittner and Larcker, 1998); (2) the strategic profit impact model(otherwise known as the Dupont model (Lambert and Burduroglu, 2000); and (3) thetheory of constraints(TOC) measurement sys- tem (Lockamy III and Spencer, 1998; Smith, 2000). Each of these major systems has strengths and weak- nesses. For example, the balanced scorecard excels at its ability to force top management to recognize that multiple activities must be carried out for corporate success and the management and monitoring of these activities must be balanced. The strategic profit im- pact model provides the operations manager with a mechanism, whereby operational improvements such as reductions in inventory—changes of interest to the operating personnel—can be translated into its impact on financial performance, changes of interest primar- ily to top management. Finally, the TOC approach is attractive because of its ability to simplify the perfor- mance measurement problem and reduce the number of measures and areas actively and continuously mon- itored by top management.

Theperformance measurement systemis ultimately responsible for maintainingalignment and coordi- nation. Alignment deals with the maintenance of consistency between the strategic goals and metrics as plans are implemented and restated as they move from the strategic through the tactical and operational stages of the planning process. Alignment attempts to

ensure that at every stage that the objectives set at the higher levels are consistent with and supported by the metrics and activities of the lower levels. In contrast, coordination recognizes the presence of interdepen- dency between processes, activities or functions. Coordination deals with the degree to which the met- rics in various related areas are consistent with each other and are supportive of each other. Coordination strives to reduce potential conflict that can occur when one area focuses on maximizing uptime (by avoiding setup and running large batches) and another focuses on quality and flexibility. Coordination tries to maintain an equivalence of activities, goals, and purpose across departments, groups, activities and processes.

To date, most research and managerial attention has focused onperformance measurement systemsor on individual metrics. Melnyk et al. (in press)suggest that these two levels are not sufficient by themselves. There exists another metrics construct—themetrics set. The metric set consists of the metrics assigned by a higher level of management to direct, motivate and evaluate a single person in charge of a specific activity, process, area, or function. The metrics set is critical because it is often the relevant unit of analysis, and because the scope and complexity of an individual’s metrics set can be viewed as a load imposed upon that person’s finite mental capacity.

These three levels of metrics are linked. At the base is theindividual metric, the building block.Individual metrics are aggregated to form variousmetrics sets. Each set directs, guides, and regulates an individual’s activities in support of strategic objectives. Coordinat- ing and managing the development of the variousindi- vidual metricsand themetrics setsis theperformance measurement system.

4.1. Positioning metrics within the research environment

There has long been recognition of metrics and its importance within the operations management field. Wickham Skinner in 1974 identified simplistic perfor- mance evaluation as being one of the major causes for factories getting into trouble (Skinner, 1974). Subse- quently, Hill (1999) recognized the role and impact of performance measures and performance measure- ment systems in his studies of manufacturing strategy.

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In these and other studies, metrics are often viewed as being part of the infrastructure or environment in which manufacturing must operate.

However, while we have recognized the role of met- rics as an influencing factor, there is still a need to posi- tion the topic of metrics within a theoretical context—a framework that gives metrics a central role. One such theoretical framework isagency theory.

Agency theory applies to the study of problems aris- ing when one party, the principal, delegates work to another party, the agent (Eisenhardt, 1989a; Lassar and Kerr, 1996). The unit of analysis is the metaphor of a contract between the agent and the principal. Prior studies using agency theory as the theoretical frame- work have used “coordination efforts” (Celly and Frazier, 1996), “control” (Anderson and Oliver, 1987), and “management” (McMillan, 1990) as the unit of analysis.

There are numerous factors and variables that in- fluence the most efficient “contract” in the dyadic relationship between a principal and agent. These include the information systems (Eisenhardt, 1989a), outcome uncertainty (Eisenhardt, 1989a), risk aver- sion (Anderson and Oliver, 1987; Celly and Frazier, 1996), programmability (Eisenhardt, 1989a), and the relationship length (Eisenhardt, 1989a; Celly and Frazier, 1996).

Within operations management, agency theory has been used to study such topics as decentralized cross-functional decision-making (Kouvelis, 2000), group technology (Beh-Arieh, 1999), international manufacturing (Change, 1999), scheduling (Gu, 1997; Kim, 1996), and inventory management (Allen, 1997). Yet, what makes agency theory so attractive is that the recognition that in most organizations the concept of a contract as a motivating and control mechanism is not really appropriate. Rather, the contract is re- placed by the metric (Austin, 1996). It is the metric that motivates and directs; it is the metric that en- ables principals to manage and direct the activities of their various agents. The development, selection, use, and refinement of metrics becomes a major con- cern of both principals and agents. Consequently, agency theory provides a potentially interesting and useful theoretical context for operations management researchers to analyze this critical topic.

Dependency theory (Pfeffer and Salancik, 1978) might also be seen as a potentially fruitful lens through

which to view the role of metrics in operations man- agement. This theory states that the degree of interde- pendence and the nature of interactions among func- tional specialists within an organization are influenced by the nature of the collective task they seek to accom- plish. In dynamic environments involving rapid prod- uct change and high degrees of heterogeneity in cus- tomer requests, agents responsible for different func- tional aspects of order taking, processing, and fulfill- ment become more and more dependent on each other for information necessary to complete their respec- tive tasks. This theory has implications for the design of metrics systems. For example, questions such as “How should metrics reflect the interdependencies of different functional areas?” could be posed. And fur- ther, “How should the rotation or change in metrics be associated to the dynamics of demands placed on the operating system?” These types of research questions start to get at the coordination attributes of higher or- der performance measurement systems raised earlier in this article.

We have in several places noted that metrics provide a vital linkage between intended strategies and actual execution. This notion harkens to theories of strategic fit. Skinner (1969)offered one of the earliest opera- tions management perspectives on strategic fit as he argued the need for strategic fit between manufactur- ing goals and decisions and other functional and cor- porate strategies.Wheelwright (1984)further defined the notion of strategic fit, stating the need for consis- tency between operations strategy and business strat- egy, other functional strategies, and the competitive environment, respectively. While the need for strate- gic fit is recognized, how to go about achieving it has received less attention in the operations strategy liter- ature. Taking strategy fit theory as the frame of ref- erence for the study of metrics might lead to insights into the role of metrics in achieving fit, and again, the implications of strategy fit for the design of metrics systems.

An information processing perspective (Galbraith, 1973) offers yet another potentially rewarding way to look at metrics. Presumably, a richer “metrics set” cre- ates the basis for richer communications among de- cision makers, workers, strategy representatives, and customers of a process. However, there may be limits to the organization’s (as well as individuals’) ability to process larger sets of metrics, and increasing num-

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bers of metrics could lead to greater conflict in the implied priorities, as well as greater equivocality re- garding future actions. Given this apparent trade-off between metrics set richness and complexity, an in- formation processing theoretical view could stimu- late research into questions regarding the optimal size of a metrics set, or perhaps the optimal combination of outcome and predictive metrics included in the set.

Linkage research in services, beginning with the work of Schneider et al. (1980)and more recently re- viewed in Pugh et al. (2002)has focused on validat- ing and quantifying perceived relationships between internal actions of a firm (such as particular human resources practices) and important strategic outcomes (such as customer satisfaction and profitability). This research may be particularly applicable to addressing such questions as “How does one derive a predictive metric?” and “Is a perceived predictive metric truly predictive?” It may also help in illuminating the dif- ferences between the metrics system as conceived by management and its actual structure. This will be par- ticularly relevant in understanding the relationships between metrics that are not mathematically derived, such as when metrics interact through a lens of altered behavior.

4.2. Overview of the special issue

As can be seen from the preceding discussion, metrics and performance measurement is inherently a complex topic. There is a need for research intended to better structure and to appropriately simplify the analysis of this topic. These and other factors formed the motivation for this special issue.

The announcement for this special was framed in very broad terms. Both theoretical and empirical pa- pers, as well as rigorous case studies, were invited. Cross-functional studies were particularly encouraged. Suggested topics for the special issue included:

• Assessing the impact of operating/predictive met- rics on system performance.

• Evaluating the relationship between financial and operating metrics.

• Measuring performance within the supply chain environment—practices, challenges, problems and opportunities.

• Assessing consistency of metrics, both within the set of metrics being used and between the metrics and corporate strategy (and the potential effects at- tributable to consistency or the lack of consistency within the metrics).

• Implementing performance measurement systems. • Changing performance measurement system or

metrics over time. • Measuring performance of product/process design

processes. • Integrating environmental issues into the perfor-

mance measurement systems. • Integrating metrics with the real or perceived reward

structure. • Assessing performance measurement and metrics

within services and manufacturing settings.

Papers that were submitted for the special issue were subjected to an initial review by the co-editors to assess the compatibility of the topic addressed by the paper with the theme and focus of the special issue. Papers that did not pass this initial screen were, at the authors’ discretion, forwarded to the editor of theJournal of Operations Managementto be included in the normal review process for the journal. Of 10 papers submitted, 8 passed this initial review. These eight papers were then subject to a normal double-blind review process with three being accepted for publication.

The first paper is “An exploratory study of perfor- mance measurement systems and relationships with performance results” by James Evans. Evans uses the metrics framework provided by the Malcolm Baldrige Award, which is similar to that of the balanced score- card to conduct an empirical investigation of the re- lationship between the scope or types of metrics used and customer, financial and market performance. The paper addresses issues relating to both metrics and the metrics system.

The second paper by Shinn Sun is “Assessing joint maintenance shops in the Taiwanese army using data envelopment analysis”. This paper is application ori- ented, and in it Sun develops a DEA-based perfor- mance assessment tool to evaluate the performance of multiple similar functional units, and identify op- portunities for improvement. The tool is applied to a collection of maintenance shops to demonstrate its usefulness. Sun’s tool directly addresses two of the functions of metrics: control through setting objective

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standards and assuring comparability of the results; and improvement through identifying areas of relative inefficiency. Indirectly, the model supports the third function of communication in that it identifies simi- lar but more efficient functional units to communicate with.

The final paper “Perceptual measures of perfor- mance: fact or fiction?” is from Mikko Ketokivi and Roger Schroeder. In it the authors investigate the reli- ability and validity of perceptual measures of perfor- mance. They introduce and empirically demonstrate a method for using multi-informant survey data, and conclude that the reliability and validity of such an ap- proach is satisfactory. This has implications for prac- tice in situations where perceptual data is the best or only source of assessment, and perhaps more impor- tantly for future research on the linkage between met- rics attributes and firm performance.

4.3. Concluding comments

The intent of our discussion has been to first con- vince the reader of the importance of metrics as a topic and the need for an increased understanding of metrics and their role in the firm, and to provide some orga- nization to our current understanding of the topic. We have suggested an outline of what we see as important characteristics by which the research space can be or- ganized, and provided some initial theoretical ground- ing for this research in agency theory, dependency the- ory, strategic fit theory, information processing theory, and linkage research. Taken in the greater context of the special issue, it should suggest many profitable av- enues of inquiry to follow. It is our hope that it will serve to inspire other researchers to contribute to our understanding of this very important topic.

Acknowledgements

All papers contained in this special issue were sub- ject to an exhaustive review process. Critical to the success of this review process were the various re- viewers who gave extensively of their time and in- sights. We would like to acknowledge the following re- viewers: Joe Biggs, California Polytechnic State Uni- versity; Ken Boyer, Michigan State University; David Collier, Ohio State University; Kevin J. Dooley, Ari-

zona State University; Janet L. Hartley, Bowling Green State University; Nancy Lea Hyer, Vanderbilt Univer- sity; Jay Jayaram, University of Oregon; Chris Mc- Dermott, Rensselaer Polytechnic Institute; Linda G. Sprague, CEIBS; Srinivas Talluri, Michigan State Uni- versity; Shawnee Vickery, Michigan State University; D.B. Waggoner, Cambridge University; Ravi Behara, Florida Atlantic University; Rohit Verma, University of Utah; Anthony Ross, Michigan State University; Daniel Krause, Arizona State University.

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