Supply chain managment

International Journal of Physical Distribution & Logistics Management Logistics Performance: Definition and Measurement Garland Chow Trevor D. Heaver Lennart E. Henriksson

Article information: To cite this document: Garland Chow Trevor D. Heaver Lennart E. Henriksson, (1994),"Logistics Performance", International Journal of Physical Distribution & Logistics Management, Vol. 24 Iss 1 pp. 17 - 28 Permanent link to this document: http://dx.doi.org/10.1108/09600039410055981

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Introduction Logistics research may be defined as the systematic and objective search for, and analysis of, information relevant to the identification and solution of any problem in the field of logistics[1]. A great deal of logistics research is conducted around the premiss that a relationship exists between a particular course of action and logistics performance (or effectiveness). Unfortunately, drawing broad inferences from the work that has been done is frustrating because of the great variety of ways in which performance has been defined in the literature. The definition of performance is a challenge for researchers in any field of management because organizations have multiple and frequently conflicting goals[2]. Some define goals in terms of profits. Others may choose goals such as customer service or sales maximization. Also difficult are the tasks of selecting and developing adequate measures for the chosen definition. “Hard” measures (such as net income or accounting figures) and “soft” measures (such

as customer satisfaction ratings) each have strengths and weaknesses associated with them.

The purpose of this article is to examine the definition and measurement of performance in logistics research. We begin with a literature review which includes an examination of the various ways in which “performance” has been defined. Data collection methods, sources, and the measures that have been used are also identified. Next, potential sources of performance data are identified and discussed. Recommendations arising from the review complete the article.

Literature Review The contents of five leading logistics journals between 1982 and 1992 (International Journal of Logistics Management, International Journal of Physical Distribution & Logistics Management and its predecessors, Journal of Business Logistics, Logistics and Transportation Review and Transportation Journal) were reviewed for studies addressing performance. A number of studies from other sources were selected for examination on an ad hoc basis.

Summary of the Literature The literature may be divided into six categories. They are:

(1) Conceptual works;

(2) Performance definition;

(3) Performance measurement;

(4) “Leading edge” literature;

(5) Performance as an outcome variable;

(6) Mathematical/economic analyses. The first category is composed of three articles and three volumes. These conceptual works are listed and summarized briefly in Table I. Armitage studied how management accounting techniques could be used to improve productivity analysis in distribution operations[3], while Mentzer and Konrad reviewed performance measurement practices from an efficiency and effectiveness perspective[4]. Rhea and Shrock presented a framework for the development of measures of the effectiveness of distribution customer service programmes[5].

While some empirical work is reported in Byrne and Markham’s volume, a key contribution lies in its conceptual treatment of measurement issues, particularly performance indicators[6]. The volume by La Londe et al. focuses on customer service[7]. This excellent work includes an ambitious survey of shippers, carriers and warehouse executives, and provides several ideas relevant to measuring customer service performance. A

17LOGISTICS PERFORMANCE: DEFINITION AND MEASUREMENT

Data collection methods, sources and the measures used are identified.

Logistics Performance: Definition and Measurement

International Journal of Physical Distribution & Logistics Management, Vol. 24 No. 1, 1994, pp. 17-28 © MCB University Press, 0960-0035

Garland Chow, Trevor D. Heaver and Lennart E. Henriksson

The authors gratefully acknowledge the financial support provided by the Social Sciences and Humanities Research Council of Canada (SSHRC). An earlier version of this article was presented at the 1993 International Logistics Congress in Toronto, Canada.

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particular contribution of the book lies in its emphasis upon the inter-temporal and multi-dimensional nature of customer service. The volume by the Nevem Working Group is a comprehensive description and application of “hard” performance indicators in the logistics setting[8].

A variety of empirical studies comprise the remaining categories. Tables II – VI list the studies by category, and identify the data source and collection method used, how performance is defined, and whether the measures used for performance are soft or hard.

18 IJPD & LM 24,1

Article/book Summary

Armitage[3] Focuses on the use of management accounting techniques to measure (and improve) efficiency and effectiveness in distribution operations

Mentzer and Konrad[4] Reviews measurement practices and suggests methods for improvement

Rhea and Shrock[5] Defines “physical distribution effectiveness” and discusses some implications for measurement

Byrne and Markham[6] Focus is on quality; a key value of this book lies in its treatment of performance indicators for various dimensions of logistics

La Londe, Cooper and Noordewier[7] Focus on customer service and how it may be measured

Nevem Working Group[8] Comprehensive review of performance indicators in logistics

Table I. Conceptual Articles

Data collection Article/book method Data source Measures Definition of performance/remarks

Read and Miller[9] Mail survey Managers Soft Quality: (total customer satisfaction, on-time delivery, zero defects, employee awareness of quality importance, reduction of the cost of quality, best-in-class practices, human resource excellence, satisfaction of industry regulations, employee education, other)

Harrington, Lambert Archival Firm Hard Vendor performance (criteria developed using and Christopher[10] brainstorming method): promised lead-time, lead-

time variability, fill rate, discrepancies, total purchases

Gassenheimer, Mail survey Executives Soft Logistical performance: length of promised order Sterling and cycle times for base-line/in-stock products, Robicheaux[11] manufacturer’s performance in meeting

promised delivery dates, fill rate on base-line/in-stock items, advance notice on shipping delays, accuracy of manufacturer in forecasting and committing to estimated shipping dates on contract/project orders, manufacturer's adherence to special shipping instructions, accuracy in filling orders

Cooper, Browne Personal European-based Soft Study focused upon performance indicators and and Peters[12] interviews companies found significant variation in logistics efficiency

depending upon which performance indicators were used

Table II. Empirical Studies: How Performance May Be Measured

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Four empirical studies focus primarily upon how performance could be measured: their content is summarized in Table II. Read and Miller conducted an exploratory study of quality in logistics, using a sample of firms on a consultant’s mailing list[9]. One of the most important findings of their study is “a clear gap between the importance given to the components of logistics quality, and the measures being used” (p. 36). Harrington et al. developed an interesting model for vendor performance evaluation in a logistics context[10]. A

brainstorming method was used to identify the various dimensions of vendor performance, and to evaluate the priority that each deserved. The model was subsequently tested in a health-care setting. Gassenheimer et al. on the other hand, employed factor analysis to identify the dimensions of “performance”[11]. Their analysis revealed five key dimensions: “logistical”, “boundary personnel”, “product/product support” [sic], “flexibility and innovative” [sic] and “inventory assistance” (p. 20). A principal contribution of the paper by Cooper et al. lies in

19LOGISTICS PERFORMANCE: DEFINITION AND MEASUREMENT

Data collection Article/book method Data source Measures Definition of performance/remarks

Bowersox, Daugherty, Mail survey Firm Soft Common attributes index (performance Dröge, Rogers and and interview antecedents). Appendices include attempts to Wardlow[15] associate firm CAI measures with performance;

results revealed few associations that were statistically significant

Daugherty, Stank and Mail survey Firm Soft Service capabilities of firm (various logistics and Rogers[17] support services)

Daugherty, Sabath and Mail survey Firm Soft Firm’s ability to accommodate special requests: Rogers[19] special service requests, programme support,

customized service, modification while in logistics system

Dröge and Mail survey Firm Soft Firm’s ability to accommodate product Germain[20] introduction, product phaseout, product recall and

customization of service levels to specific markets or customers

Germain[21] Mail survey Firm Soft Ability of firm’s logistics system to accommodate: supply disruption, production schedule changes, in-stream product modification, services level customization, incentive programmes, special customer requests, product introduction, product phase-out, product recall and computer breakdown

Table IV. Empirical Performance Studies: “Leading-edge” Literature

Data collection Article/book method Data source Measures Definition of performance/remarks

Clarke[13] Mail survey Executives Soft Productivity (study sought to determine which measures used)

Yavas, Luqmani Mail survey Managers Soft Efficiency (study provides data on efficiency and Quraeshi[14] measure usage, and attempts to correlate this

with a measure of purchasing sophistication developed by the authors)

Table III. Empirical Studies :Which Performance Measures Are Used

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its use of performance indicators for the purpose of measuring logistics efficiency and effectiveness[12].

Two studies, summarized in Table III, report the results of surveys which sought to identify the measures used by decision-makers in assessing logistics performance. Clarke reports how physical distribution productivity was measured by South Carolina distribution executives[13]. Yavas et al. focused a similar study on the purchasing area. They collected data on the usage of efficiency measures in Saudi Arabian firms and found that utilization of measures was associated with purchasing sophistication[14]. Given the different orientations of the two studies, the findings are understandably quite dissimilar.

In Leading-edge Logistics, Bowersox and his colleagues distinguish between emergent, norm and leading-edge organizations on the basis of common attributes index (CAI) scores [15]. However, few significant relationships were reported between CAI scores and publicly-available financial performance measures [15, Appendix E, 16].

Four performance-relevant studies have been found which build on the Leading-edge material. A summary of these studies (along with the original volume) is presented in Table IV. An important limitation of the CAI

approach is that it measures potential, that is, services offered rather than actual logistics performance. For example, Daugherty et al. suggest that the ability of the logistics system to accommodate the customization of service levels to specific markets or customers is an important dimension of performance[17]. They measured performance by asking respondents to identify which “expanded services” were offered to customers (e.g. customer billing/collection; freight bill audit and payment and so on). The authors conclude that “firms wishing to improve logistical performance are well advised to concentrate on formalizing selected processes” (p. 57). Because the dependent variable is potential rather than actual service delivery, this conclusion is best regarded as tentative and in need of further empirical validation. The limitations of outcome variables which may have little or no direct linkage with actual performance are well- known[18, p. 543]. For, as Cooper and his colleagues noted, “there can be a large gulf between what companies say they do and what they actually do” [12, p. 30].

Daugherty et al. found that firms that could be distinguished as “leading edge” by virtue of several structural characteristics performed better than others[19]. The authors defined performance as “ease of accommodating special requests”, such as sales and marketing incentive programmes. Again, it is potential,

20 IJPD & LM 24,1

Data collection Article/book method Data source Measures Definition of performance/remarks

Fawcett and Closs[22]; Mail survey Managers Soft “Competitive position” of firm or profit centre: Fawcett[23] (rated own five perceptual items rating from nontraditional

organization’s measures of cost and customer service to more performance) traditional measure of growth in sales and growth

in return on assets

Perry[24] Site visits, Managers Soft “Operating performance” of specific logistics questionnaires (rated own dimensions: vendor relations, material and interviews firm’s acquisition lead-time, purchasing method,

performance) material invoice, transport management/ comparison between JIT and non-JIT firms

Rhea and Mail survey Key decision Soft “Distribution effectiveness”: adequacy, Shrock[25, 26] makers consistency, accuracy, timeliness, initiative,

responsiveness

Fawcett[28]; Mail survey Managers Soft A single logistics function, carrier performance: Fawcett and on-time performance, transit time, rates and Vellenga[29] tariffs, accuracy, equipment co-ordination,

documentation, information, loss and damage

Marr[30] Mail survey Executives Soft “Distribution service performance”/one-item performance measure

Table V. Empirical Performance Studies: Performance as an Outcome Variable or Basis for Comparison

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not actual performance that is being measured. Similar outcome variables were used in Dröge and Germain’s study of the effects of formalization[20] and Germain’s study of the effects of customization and standardization[21].

A summary of the studies in which performance was either investigated as an outcome variable or used as a basis for comparison is provided in Table V. Three studies investigated variables associated with performance in a fairly direct fashion. Fawcett and Closs used a promising application of path analysis to demonstrate linkages between “perceived globalization”, “manufacturing”, “logistics”, and the outcome variable, “competitive position”[22]. In another study using the same database, Fawcett found that firms that emphasized logistics issues at a higher level in the early stages of the co-production decision outperformed their counterparts[23, p. 39]. In his study of firm behaviour and operating performance, Perry conducted a qualitative study utilizing site visits, questionnaires and interviews with managers. He found differences between just-in-time (JIT) and non-JIT firms in a number of performance- related areas: vendor relationships, material acquisition leadtimes, purchasing methods and materials inventories[24].

Rhea and Shrock utilized a combination of previous research and interviews to identify six key elements of logistics distribution effectiveness[25,26]. They found that significant differences exist between “effective” and “ineffective” organizations in the predicted direction. A strength of this study lies in its use of “customer” food broker managers to rate the performance of “seller” producers. While the methodology for collecting the data is quite defensible, a limitation arises from the fact that each respondent determined both the overall assessment of four firms as effective or ineffective, and then evaluated

each one on the various elements. The likelihood for a consistency bias is consequently quite high[27].

Fawcett and Vellenga compared the performance of maquiladora and domestic transport operations[28,29]. The survey respondents were managers who rated the performance of transport firms with whom they dealt. The analysis revealed that transport service performance was less in the transnational operating environment. Marr found “management sophistication” to be somewhat helpful in predicting “distribution service performance”, although a limitation in his findings lies in the one-item measure used to measure “level of overall distribution service”[30].

Four studies used mathematical or economic models with hard performance measures, as summarized in Table VI. The papers by Clarke and Gourdin[31] and Kleinsorge et al.[32,33] used data envelopment analysis (DEA) to evaluate the efficiency of logistics practices. Essentially, DEA uses linear programming methods to measure efficient combinations of inputs and outputs for a set of decision-making units (DMUs) and provides relative performance ratings for all the DMUs in the data set[34]. Writers in logistics and other fields have pointed out that the advantages of DEA include the less restrictive requirement for data input in comparison to many other

21LOGISTICS PERFORMANCE: DEFINITION AND MEASUREMENT

Data collection Article/book method Data source Measures Definition of performance/remarks

Clarke and Archival Firm Hard DEA – efficiency/productivity (also included Gourdin[31] evaluation of DEA using questionnaire with soft

measure)

Kleinsorge, Schary Archival Firm Hard DEA – efficiency and Tanner[32, 33]

Diewert and Smith[37] Archival Firm Hard Total factor productivity

Gomes and Mentzer[38] Simulation Previous Hard Profitability: also examined order cycle time, research variance in order cycle time and order fill rate

Table VI. Empirical Performance Studies: Mathematical/Economic Analyses

Qualitative variables such as attitudes or perceptions

can be included

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methods of quantitative analysis. In particular, qualitative variables such as attitudes or perceptions can be included, thereby allowing the injection of essential intangibles into the system analysis. DEA has the ability to isolate variables for measurement while still including the effects of interaction among both input and output variables[33, p. 39; 35, p. 529). This is in marked contrast to “conventional” performance indicator analyses which are only incomplete measures of performance[34]. Disadvantages of DEA include the frequent difficulty of obtaining data [36, p. 443]. It should also be noted that thus far, most applications of DEA have been in the public sector[36, p. 443].

The paper by Diewert and Smith is a promising attempt at applying total factor productivity measurement in a distribution setting[37]. Using data from a large appliance parts distributor in Western Canada, the authors conclude that large productivity gains were possible in the distribution sector of the economy thanks to the computer revolution, which allows a firm to track its purchases and sales of inventory items and to use the latest computer software to minimize inventory holding costs (p. 16). Finally, Gomes and Mentzer employ a simulation to explore the influence of JIT systems on distribution system performance[38]. They found that JIT systems in the physical distribution and materials management contexts were associated with significantly more favourable profit and service results in comparison to non-JIT systems. System-wide JIT, on the other hand, produced favourable service results, but unfavourable profit results (p. 47).

Implications With the exception of the mathematical/economic studies, almost all of the empirical studies utilized soft measures for the outcome variable. Nevertheless, both soft and hard measures are associated with strengths and weaknesses (as a later section of this article discusses in detail). This limits a researcher’s ability to infer the existence of relationships between logistics performance and its antecedents.

One limitation common to several of the studies is that the respondents appraised their own performance. This becomes a particular concern in cases where the performance dimension under consideration is better assessed by another source, such as the customer. Also, very few studies we reviewed adequately captured the multiplicity of goals that must be included in any meaningful evaluation of performance at either the logistics or firm level. To be sure, keeping a study within a feasible domain will often involve limiting the examination to one or more dimensions of “performance”. However, unsupported extrapolations of the findings of such studies to unmeasured dimensions are difficult to justify. For example, a researcher who finds a significant relationship between the utilization of total quality

management programmes and customer satisfaction should indicate that the findings of the study do not necessarily generalize to dimensions of performance that were not included in the study.

The predominance of the mail survey as a data collection method in the logistics studies reviewed raises some concern in light of its inherent limitations[39]. To their credit, however, most authors disclosed these limitations to one extent or another. A few offered especially coherent discussions of remedial or assessment measures that had been completed, such as those suggested by Lambert and Harrington[40].

The examination of these studies reveals that an immense variety of operational definitions and measures exists for logistics performance. This is the result of the varying interests of the researchers and the complexity of performance that have been alluded to earlier. Another source of the variety in performance measures is the domain to which the measure is relevant. Several of the studies measure performance at a logistics activity level (e.g. transport or warehousing). Other studies measure performance at the logistics function level and several attempt to measure the firm’s performance.

None of these studies examines logistics performance in the context of supply chain management. This is significant. The supply chain comprises all companies that participate in transforming, selling and distributing the product from raw material to the final consumer. It has two implications for research in logistics performance. First, some research might be oriented towards measurement of supply chain management, that is, performance involving multiple organizations. Second, research oriented to members of a channel should recognize the relationship between channel structure and member functions. For example, distributors in the health-care supply chain are increasingly holding inventory and performing sorting functions traditionally performed by hospitals. This integration has resulted in lower inventory and handling costs in the whole supply chain, although specific members of the chain may exhibit higher inventory and sorting costs than might otherwise be expected.

22 IJPD & LM 24,1

Distributors in the health-care supply chain are increasingly

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Unfortunately, the variety of performance measures make it difficult to draw broad inferences from the literature about the relationship between a given logistics practice and performance. Meta-analysis, or aggregating the findings of several studies[41], is frustrated by the use of diverse measures. Differences in findings between one study and another may be attributable solely to the measures used.

Defining Logistics Performance Conceptually, logistics performance may be viewed as a subset of the larger notion of firm or organizational performance. The latter has attracted a large volume of diverse research over the years[2,35] and illustrates the futile nature of the search for the “one best way” of defining performance. For example, Gleason and Barnum chose to distinguish between effectiveness and efficiency. They defined effectiveness as “the extent to which an objective has been achieved”, while efficiency was defined as “the degree to which resources have been used economically”[42, p. 380]. Simply put, efficiency is “doing things right”, while effectiveness is “doing the right thing”[42, pp. 3,4]. Sink and his colleagues, on the other hand, defined seven dimensions in order to capture their conception of “what performance means”: they are effectiveness, efficiency, quality, productivity, quality of work life, innovation and profitability/budgetability[43, pp. 266-7].

Both of these examples have strengths and weaknesses. A principle strength of Gleason and Barnum’s work is its simplicity. This simplicity may be somewhat deceptive, however: a convincing case could be made about the importance of efficiency as one dimension of effectiveness. As for the dimensions identified by Sink and his colleagues, they are intriguing because they meet the need for a broad, comprehensive framework. However, this comes at the price of some overlap between the various dimensions, as the authors themselves infer[43,p. 268]. In particular, the distinction between performance and effectiveness is somewhat unclear.

Given the lack of any universally-accepted definition for performance in the organizational literature, it should not

be surprising that extant literature offers many ideas about the dimensions that ought to be incorporated into a conceptualization of “logistics performance”. One of the best examples is the framework presented by Rhea and Shrock, where physical distribution effectiveness is defined as “the extent to which distribution programmes satisfy customers”[5, p. 35]. They note, however, that other goals remain important under this definition:

In taking this orientation, decision makers do not ignore the well-documented need to control costs. Rather, they incorporate this objective within a customer-oriented managerial philosophy in which it is believed that the long- run goal of profitability is achieved by providing customer need and want satisfaction[44; 5, p. 35].

The literature review provided by Rhea and Shrock suggests that care must be taken to incorporate multiple goals in defining performance. In order to begin the task of conceptualizing what the various dimensions of logistics effectiveness might look like, we identified a representative set of answers to the question, “what is logistics performance?” The results of this exercise are shown in Figure 1. Logistics performance may be defined as the extent to which goals such as those suggested in Figure 1 are achieved.

Figure 1 incorporates various possible dimensions of performance in a single envelope to help highlight the numerous interdependencies and conflicts between the goals. For example, interdependency is likely to exist between employee satisfaction, the quality of customer service and profitability. A conflict would occur if a pay rise for employees were postponed to achieve a short- term financial improvement – a move which may inhibit the firm’s ability to attract and retain employees who are capable of delivering quality customer service to the benefit of long-run profits. Another example is that a firm may find it advantageous in the short run to make more extensive use of packaging in an environmentally- suboptimal way although it may face more stringent

23LOGISTICS PERFORMANCE: DEFINITION AND MEASUREMENT

Cost-efficiency

Profitability

Social responsibility

On-time delivery

Sales growth

Job security and working conditions

Customer satisfaction

Keeping promises

Flexibility

"Fair" prices for inputs

Low loss and damage

Product availability

Figure 1. What Is Logistics Performance?

Care must be taken to incorporate multiple

goals…

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regulation, increased paper burden and compliance costs and reduced profits in the long run.

Measuring Logistics Performance The preceding discussion argues that performance is multi-dimensional. No one measure will suffice for logistics performance. Instead, the objective for researchers and managers is to find a set of measures which collectively capture most, if not all, of the performance dimensions thought to be important, over both short- and long-term horizons. For example, in a presentation at the Tenth International Logistics Congress in Toronto, in June 1993, Mr D. Eggleton of Rank-Xerox described the criteria on which his performance is evaluated as employee satisfaction, customer satisfaction, and the company’s rate of return.

Many dimensions of logistics performance lend themselves well to hard performance measures. A representative collection of these, along with key advantages and disadvantages, is shown in Table VII. Hard performance measures such as net income or order fill rate are typically impersonal, accurate and easy and inexpensive to collect. Measures such as net income, and accounting ratios such as return on investment (ROI) are useful ways of capturing profitability, and will often be easy and inexpensive to collect, particularly where logistics is treated as a profit centre. Profitability is a particularly useful goal because it directly reflects the goals of all of the organization’s internal constituent groups to one extent or another, although it may not be a good indicator of the viability of the firm in the long run.

Cost accounting measures may also be useful, particularly in evaluating several dimensions of efficiency. The data are often highly accurate and, in many cases, available over long periods of time. However, these measures are not always comparable between one organization and another. Changes in accounting practices may even inhibit valid comparisons within the same organization over time.

There are some difficulties which are common to both raw financial measures and cost accounting data. Because they are often considered confidential, many firms are reluctant to release information to outsiders. Also, in making comparisons between organizations or time periods, variations in standards or accounting methods are a frequent threat[45]. A particular problem to logistics researchers is that in many cases, the level of aggregation is so high that it is difficult to utilize for evaluating sub- functions of the firm.

The use of input-output ratios (also known as productivity or performance indicators) is common in logistics, and has received extensive treatments in textbooks and other literature[8, 46]. Many goals lend themselves well to evaluation using these measures. For example, productivity may be measured utilizing ratios such as number of shipments per vehicle-mile, while a ratio such as percentage orders delivered on time will be helpful in evaluating the quality of service rendered. As the concern of society over environmental issues grows, some firms may find it useful to calculate ratios such as “tons of packaging used/total tonnage shipped”. Again, the limited ability of the researcher to gain access to these data because of their confidential nature is a potential disadvantage. Also, variations in definitions and data

24 IJPD & LM 24,1

Measure type Advantages/disadvantages

Raw financial statistics (e.g. net income, Advantages: Often easy and inexpensive to collect, and is likely to be comparable gross sales) between organizations. Can capture several important dimensions of

performance, often with impressive accuracy Cost statistics (e.g. transport cost, standard labour costs) Disadvantages: May not be comparable between one time interval and another.

Accounting methods may limit comparability between organizations. Level of aggregation may be so large that it is difficult to assign responsibility. Firms may be unwilling to divulge information

Input/output measures or “performance Advantages: Can be used to evaluate goal attainment in many areas, particularly indicators” (e.g. number of shipments/ efficiency and effectiveness vehicle hour)

Quality measures (e.g. order cycle time) Disadvantages: Narrow focus upon individual performance indicators may easily cause faulty analysis or decision-making. Researchers may have trouble getting data. May be incomparable across organizations

Table VII. Alternative “Hard” Measures for Logistics Performance

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collection procedures will often make it difficult to compare performance indicators between one organization and another.

For service measures such as order cycle time or lead time variability, the advantages and disadvantages are essentially the same as those of performance indicators. One limitation common to both is that there are many dimensions of performance which they cannot capture, particularly the extent to which customers are satisfied.

The difficulty in capturing customer satisfaction is the underlying reason that hard measures should be supplemented with “soft”, perceptual ones. Although there are several dimensions of logistics performance which hard measures cannot capture in a meaningful way, customer satisfaction is perhaps the most critical. A set of soft measures, collected using techniques such as the mail survey, telephone interview, or similar method are needed. Besides their usefulness in identifying problems, soft measures may also be called for where available hard measures are not comparable between one organization and another because of differences in accounting standards or similar problems. These measures are subject to the limitations inherent in any self-report, such as consistency bias, and the social desirability problem[27,39]. The difficulties are especially serious for one-item measures[27].

Soft measures may also raise comparability problems. Suppose that a handful of manufacturers in the same industry report (on a scale of 1 to 5) how well their logistics function achieves on-time delivery goals. The responses may be difficult to interpret if (as will likely be the case), there is variation in the competitive strategy or goals of the firms. This may well influence what is reported by the respondent. As an example, consider two firms with an actual on-time delivery level of 80 per cent. Each firm’s manager is asked to rate performance. If Firm A’s goal is 100 per cent and Firm B’s goal is 80 per cent, it is likely that Firm A’s manager will provide a lower performance rating than Firm B’s. In other contexts, low scores on “quality of service” may reflect the presence of a cost-minimization strategy which are not meaningfully comparable to those of a firm seeking to differentiate itself by providing superior service. Soft

measures are associated with a host of other limitations, the most notable being self-report defects[27] or other forms of bias[39].

An excellent way to improve the validity of soft measures may be found in Churchill’s paradigm for developing better measures of marketing constructs[47]. Essentially, the paradigm comprises a number of carefully-ordered steps by which literature searches, surveys and analysis procedures are linked together in a coherent and justified sequence. First, the domain of the construct is specified. Next, a sample of items is generated. Third, the measure is purified. Reliability and validity of the construct are then assessed. The Churchill paradigm has been employed by a handful of logistics scholars[22], with promising results.

The discussion presented here suggests that although the optimal set of performance measures will depend on the purpose of the research, it will often include a collection of both hard and soft measures. One important criterion to consider in choosing the set is “representativeness”. That is, the set of measures should meaningfully capture those dimensions of performance in which the researcher is interested. The use of only one or two measures of performance is justified for the researcher whose study addresses only customer satisfaction or cost efficiency. However, findings from such studies should not be carelessly extrapolated to include the larger variable, logistics performance.

Recommendations This article argues that defining and measuring performance in logistics is a difficult enterprise, for both researchers and managers. The literature review reveals the nature and limitations of the various designs and measures which have been used thus far and, along with subsequent sections, suggests that there are no “easy” ways to address the issues raised. In light of this review, we offer five recommendations which should help improve the quality of future research.

More Efforts to Develop Performance Measures In the short run, we urge researchers to give more detailed information about how they have defined and measured performance, and about the limitations of their study and its findings. Inadequate reporting of the appropriate caveats serves only to mislead readers and frustrate logistical excellence.

Special issues of logistics journals devoted to measurement and methodological issues would be most useful. Their leading role would be very likely to generate added interest in logistics research methodology among researchers and students. A more coherent picture of the various dimensions of performance would make it much

25LOGISTICS PERFORMANCE: DEFINITION AND MEASUREMENT

Factor analysis will be an especially

useful tool

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easier to address subsequent questions such as, “how” should each dimension be measured?”, and “who should provide data for each measurement?”

Increased attention to the development of valid measures is warranted, particularly the soft ones which capture dimensions of performance which are otherwise left out. The encouraging results of the study by Gassenheimer and his colleagues, in which factor analysis is used, suggest that this will be an especially useful tool for developing valid perceptual or self-report scales[11]. As noted earlier, expanded use of the paradigm suggested by Churchill[47] is very likely to enhance the validity of studies that are conducted, and also facilitate aggregation and comparison among them.

Encouragement of More Innovative Research Designs Studies in which more than one constituency provides data in evaluating performance should be encouraged[48]. Although “rate-your-own-company” studies have a legitimate place in research, they need to be supplemented by data from other constituent groups, especially customers.

While we suspect that the mail survey will continue to be an important data-collection method, journal editors should encourage studies in which alternative methods are used. Also, in the short run, journal reviewers and editors should insist that submissions articulate and justify how performance has been defined, and describe the measures that have been used. In particular, questionnaires should be reproduced verbatim in an appendix, or an address provided where interested researchers may obtain a copy. In this way, a body of time-tested items can be built up in short order, meta- analyses are facilitated, and needless “re-invent the wheel” exercises are avoided.

Quantitative techniques such as data envelopment analysis and total factor productivity both represent potentially valuable ways to evaluate economic performance, and are potentially important tools in logistics research. However, these should not be permitted to obscure the potential gains in knowledge that may be facilitated by qualitative studies, most notably the “case study” approach. One particular form of

case study that has proven invaluable for many firms is the “logistics performance audit” in which a team systematically evaluates a firm’s logistics function.

Development of Contingency Models of Logistics Performance Researchers might do well to explore contingency models of logistics performance[49,50]. To date, their utilization has been sparse, despite their intuitive appeal. Rather than the “one-best-way” paradigm which is common in so many discussions of logistics research, a contingency model would be based on the supposition that the fit between logistics organization and strategy and the organization’s environment, product line, production technology and size will influence performance outcome variables. The development and testing of such a model might well stimulate research on the question of the primacy of various performance dimensions. For example, the firm which has adopted a cost-minimization strategy may be better served by focusing on cost dimensions of performance than the firm which has chosen to differentiate itself on quality of service. While this assertion may have intuitive appeal, it has little in the way of empirical support thus far.

Recognition of the Implications of Supply Chain Management The shift to supply chain management has two implications for logistics performance. First, the measurement of performance must recognize the particular role of an organization in a supply chain. Comparable stages in different channels may be associated with different functions. Second, consideration should be given to assessing the performance of the supply chain, not just that of individual participants.

More Bridge-building between Theory and Practice Is Needed Researchers can and should play a role in making practitioners more familiar with the importance, nature and limitations of research. Practitioners, on the other hand, can offer needed insights into the types of logistics issues that merit greater study and thought, and more generally, what our overall “focus” should be. Taking advantage of whatever opportunities we receive to engage in dialogue will help ensure that the research we conduct fulfils pragmatic purposes in the long run.

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26 IJPD & LM 24,1

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28 IJPD & LM 24,1

Garland Chow is an Associate Professor, Trevor D. Heaver a Professor and Lennart E. Henriksson a Research Associate, all in the Faculty of Commerce and Business Administration, University of British Columbia, Vancouver, Canada.

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55. Dr Nicholas BeaumontAristides MatopoulosDepartment of Marketing and Operations Management, University of Macedonia, Thessaloniki, Greece Maro VlachopoulouDepartment of Applied Informatics, University of Macedonia, Thessaloniki, Greece Vicky ManthouDepartment of Applied Informatics, University of Macedonia, Thessaloniki, Greece. 2009. Understanding the factors affecting e‐business adoption and impact on logistics processes. Journal of Manufacturing Technology Management 20:6, 853-865. [Abstract] [Full Text] [PDF]

56. Dr R. Glenn Richey JrRichard OloruntobaFaculty of Business and Law, Newcastle Business School, University of Newcastle, Callaghan, Australia Richard GrayFaculty of Social Science and Business, Plymouth Business School, Centre for International Shipping and Logistics, University of Plymouth, Plymouth, UK. 2009. Customer service in emergency relief chains. International Journal of Physical Distribution & Logistics Management 39:6, 486-505. [Abstract] [Full Text] [PDF]

57. Ola JohanssonPackaging Logistics, Lund University, Lund, Sweden Henrik PålssonPackaging Logistics, Lund University, Lund, Sweden. 2009. The impact of Auto‐ID on logistics performance. Benchmarking: An International Journal 16:4, 504-522. [Abstract] [Full Text] [PDF]

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58. Kuo‐Chung Shang, Chin‐Shan Lu. 2009. Effects of Safety Climate on Perceptions of Safety Performance in Container Terminal Operations. Transport Reviews 29:1, 1-19. [CrossRef]

59. Stephan Schmidberger, Lydia Bals, Evi Hartmann, Christopher Jahns. 2009. Ground handling services at European hub airports: Development of a performance measurement system for benchmarking. International Journal of Production Economics 117:1, 104-116. [CrossRef]

60. Thomas Landers, John English, Alejandro MendozaLogistics Metrics 4-1-4-20. [CrossRef] 61. Kim,Tae-Hyun, Seongam Moon, Kim, Wonso. 2008. Contract Attributes and the Mediation Effect of Partnership on 3PL

Performance. Korean Journal of Logistics 16:2, 5-26. [CrossRef] 62. Matthew Waller ‐ one of the usual editorsNathalie Fabbe‐CostesCentre de Recherche sur le Transport et la LOGistigue,

Universite de la Mediterranne‐Aix‐Marseille II, Aix‐en‐Provence, France Marianne JahreDepartment of Strategy and Logistics, BI Norwegian School of Management, Oslo, Norway. 2008. Supply chain integration and performance: a review of the evidence. The International Journal of Logistics Management 19:2, 130-154. [Abstract] [Full Text] [PDF]

63. Jesper AastrupDepartment of Marketing, Copenhagen Business School, Center for Retail Studies, Frederiksberg, Denmark Herbert KotzabDepartment of Operations Management, Copenhagen Business School, Frederiksberg, Denmark David B. GrantSchool of Management and Languages, Logistics Research Centre, Heriot‐Watt University, Edinburgh, UK Christoph TellerInstitute of Retailing and Marketing, Vienna University of Economics and Business Administration, Vienna, Austria Mogens BjerreDepartment of Marketing, Copenhagen Business School, Center for Retail Studies, Frederiksberg, Denmark. 2008. A model for structuring efficient consumer response measures. International Journal of Retail & Distribution Management 36:8, 590-606. [Abstract] [Full Text] [PDF]

64. Árni Halldórsson and Gunnar StefánssonJuuso TöyliDepartment of Business Technology, Helsinki School of Economics, Helsinki, Finland Lotta HäkkinenTurku School of Economics, Business Research and Development Centre, Turku, Finland Lauri OjalaDepartment of Marketing, Turku School of Economics, Turku, Finland Tapio NaulaDepartment of Marketing, Turku School of Economics, Turku, Finland. 2008. Logistics and financial performance. International Journal of Physical Distribution & Logistics Management 38:1, 57-80. [Abstract] [Full Text] [PDF]

65. Benita M. BeamonUniversity of Washington, Seattle, Washington, USA Burcu BalcikUniversity of Washington, Seattle, Washington, USA. 2008. Performance measurement in humanitarian relief chains. International Journal of Public Sector Management 21:1, 4-25. [Abstract] [Full Text] [PDF]

66. Árni Halldórsson and Gunnar StefánssonNathalie Fabbe‐CostesCentre de REcherche sur le Transport et la LOGistique, Université de la Méditerranée‐Aix‐Marseille II, Aix‐en‐Provence Cedex, France Marianne JahreDepartment of Strategy and Logistics, Norwegian School of Management (BI), Oslo, Norway. 2007. Supply chain integration improves performance: the Emperor's new suit?. International Journal of Physical Distribution & Logistics Management 37:10, 835-855. [Abstract] [Full Text] [PDF]

67. Chin‐Shan Lu. 2007. Evaluating Key Resources and Capabilities for Liner Shipping Services. Transport Reviews 27:3, 285-310. [CrossRef]

68. Yen‐Chun Jim WuDepartment of Logistics, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, Republic of China. 2006. Assessment of technological innovations in patenting for 3rd party logistics providers. Journal of Enterprise Information Management 19:5, 504-524. [Abstract] [Full Text] [PDF]

69. Helena ForslundDepartment of Logistics and Supply Chain Management, School of Management and Economics, Växjö University, Växjö, Sweden. 2006. Performance gaps in the dyadic order fulfillment process. International Journal of Physical Distribution & Logistics Management 36:8, 580-595. [Abstract] [Full Text] [PDF]

70. B.S. SahayNitin SethInstitute of Engineering and Technology, Indore, India S.G. DeshmukhIndian Institute of Technology Delhi, New Delhi, India Prem VratIndian Institute of Technology Roorkee, India. 2006. A conceptual model for quality of service in the supply chain. International Journal of Physical Distribution & Logistics Management 36:7, 547-575. [Abstract] [Full Text] [PDF]

71. Lori N.K. LeonardUniversity of Tulsa, Tulsa, Oklahoma, USA Christine Clemons DavisUniversity of Arkansas, Fayetteville, Arkansas, USA. 2006. Supply chain replenishment: before‐and‐after EDI implementation. Supply Chain Management: An International Journal 11:3, 225-232. [Abstract] [Full Text] [PDF]

72. Nitin SethDepartment of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, India, and S.G. DeshmukhIndian Institute of Technology Roorkee, Roorkee, India Prem VratIndian Institute of Technology Roorkee, Roorkee, India. 2006. A framework for measurement of quality of service in supply chains. Supply Chain Management: An International Journal 11:1, 82-94. [Abstract] [Full Text] [PDF]

73. Ronald H. BallouWeatherhead School of Management, Case Western Reserve University, Cleveland, Ohio, USA. 2006. Revenue estimation for logistics customer service offerings. The International Journal of Logistics Management 17:1, 21-37. [Abstract] [Full Text] [PDF]

74. Marc WoutersSchool of Business, Public Administration and Technology, University of Twente, Twente, The Netherlands Mark SportelAccenture Technology Solutions, Twente, The Netherlands. 2005. The role of existing measures in developing

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and implementing performance measurement systems. International Journal of Operations & Production Management 25:11, 1062-1082. [Abstract] [Full Text] [PDF]

75. Bülent SezenGebze Institute of Technology, Faculty of Business Administration, Cayirova Fabrikalar Yolu, Gebze, Kocaeli, Turkey. 2005. The role of logistics in linking operations and marketing and influences on business performance. Journal of Enterprise Information Management 18:3, 350-356. [Abstract] [Full Text] [PDF]

76. Zaili Yang, Steve Bonsall, Alan Wall, Jin Wang. 2005. Reliable container line supply chains. WMU Journal of Maritime Affairs 4:1, 105-120. [CrossRef]

77. K.K.B. Hon. 2005. Performance and Evaluation of Manufacturing Systems. CIRP Annals - Manufacturing Technology 54:2, 139-154. [CrossRef]

78. Clemens Lohman, Leonard Fortuin, Marc Wouters. 2004. Designing a performance measurement system: A case study. European Journal of Operational Research 156:2, 267-286. [CrossRef]

79. K. S. Chen, K. L. Chen, R. K. Li. 2004. Measuring service quality based on number of customer complains. Journal of Interdisciplinary Mathematics 7:1, 113-124. [CrossRef]

80. Árni Halldórsson, Jesper Aastrup. 2003. Quality criteria for qualitative inquiries in logistics. European Journal of Operational Research 144:2, 321-332. [CrossRef]

81. Peter B Marlow, Ana C Paixão Casaca. 2003. Measuring lean ports performance. International Journal of Transport Management 1:4, 189-202. [CrossRef]

82. Derrien R. Jansen, Arjen van Weert, Adrie J.M. Beulens, Ruud B.M. Huirne. 2001. Simulation model of multi-compartment distribution in the catering supply chain. European Journal of Operational Research 133:1, 210-224. [CrossRef]

83. Hsu‐Hao YangNational Chinyi Institute of Technology, Taiping, Taiwan.K.S. ChenNational Chinyi Institute of Technology, Taiping, Taiwan.. 2000. A performance index approach to managing service quality. Managing Service Quality: An International Journal 10:5, 273-278. [Abstract] [Full Text] [PDF]

84. K.S. ChenNational Chin‐Yi Institute of Technology, Taichung, Taiwan H.H. YangNational Chin‐Yi Institute of Technology, Taichung, Taiwan. 2000. A new decision‐making tool: the service performance index. International Journal of Quality & Reliability Management 17:6, 671-678. [Abstract] [Full Text] [PDF]

85. Cristina MasellaPolitecnico di Milano, Milan, ItalyAndrea RangonePolitecnico di Milano, Milan, Italy. 2000. A contingent approach to the design of vendor selection systems for different types of co‐operative customer/supplier relationships. International Journal of Operations & Production Management 20:1, 70-84. [Abstract] [Full Text] [PDF]

86. Rohit BhatnagarNanyang Business School, Nanyang Technological University, Singapore, Amrik S. SohalDepartment of Management, Monash University, Victoria, Australia, and Robert MillenCollege of Business Administration, Northeastern University, Boston, Massachusetts, USA. 1999. Third party logistics services: a Singapore perspective. International Journal of Physical Distribution & Logistics Management 29:9, 569-587. [Abstract] [Full Text] [PDF]

87. K. L. Moon. 1999. Supply-side Logistics: Managing of In-coming Materials by Hong Kong Clothing Manufacturers. Journal of the Textile Institute 90:2, 163-175. [CrossRef]

88. P. Gary JarrettKaiser Permanente, Altadena, California, USA. 1998. Logistics in the health care industry. International Journal of Physical Distribution & Logistics Management 28:9/10, 741-772. [Abstract] [Full Text] [PDF]

89. Karel van DonselaarEindhoven University of Technology, The Netherlands Kees KokkeEindhoven University of Technology, The Netherlands Martijn AllessieEindhoven University of Technology, The Netherlands. 1998. Performance measurement in the transportation and distribution sector. International Journal of Physical Distribution & Logistics Management 28:6, 434-450. [Abstract] [Full Text] [PDF]

90. Sergio OlavarrietaUniversity of Chile Business School, Santiago, Chile Alexander E. EllingerVillanova Univer sity, Villanova, Pennsylvania, USA. 1997. Resource‐based theory and strategic logistics research. International Journal of Physical Distribution & Logistics Management 27:9/10, 559-587. [Abstract] [Full Text] [PDF]

91. Alexandeer E Ellinger, Patricia J Daugherty, Craig M Gustin. 1997. The relationship between integrated logistics and customer service. Transportation Research Part E: Logistics and Transportation Review 33:2, 129-138. [CrossRef]

92. Patricia J. DaughertyAssociate Professor of Marketing and Distribution, University of Georgia, Athens, Georgia, USA Alexander E. EllingerDoctoral Candidate, University of Georgia, Athens, Georgia, USA Craig M. GustinExecutive Consultant, Ernst & Young. 1996. Integrated logistics: achieving logistics performance improvements. Supply Chain Management: An International Journal 1:3, 25-33. [Abstract] [Full Text] [PDF]

93. Pietro EvangelistaAdding Value to Logistics Services using ICT 98-122. [CrossRef] 94. Edward SweeneySupply Chain Integration 1-26. [CrossRef] 95. Asad Ullah, Mohd. AdilService Quality in Supply Chain: 173-186. [CrossRef] 96. Edward SweeneySupply Chain Integration 221-246. [CrossRef] 97. Pietro EvangelistaAdding Value to Logistics Services using ICT 924-948. [CrossRef]

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