Lawrence M. Seiford

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• Doctor of Philosophy
• University of Michigan–Ann Arbor at University of Michigan

Data Envelopment Analysis (DEA) is a non-parametric, optimisation-based benchmarking technique first introduced by Charnes et al. (European Journal of Operational Research, 2(6), pp. 429–444, 1978), later extended by Banker et al. (Management Science 30(9), pp. 1078–1092, 1984), with many variations of DEA models proposed since. DEA measures the pr...

Congestion is a term that is applicable in a variety of disciplines which range from medical science to traffic engineering. It has also many uses in practical everyday life. This brings with it a certain looseness in usage. We therefore expand (and refine) our discussion of congestion with reference to its use in economics where we have access to...

In about 30 years, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating the performance. DEA has been successfully applied to a host of many different types of entities engaged in a wide variety of activities in many contexts worldwide. This chapter discusses the basic DEA models and s...

This chapter discusses returns to scale (RTS) in data envelopment analysis (DEA). The BCC and CCR models described in Chap. 1 of this handbook are treated in input-oriented forms, while the multiplicative model is treated in output-oriented form. (This distinction is not pertinent for the additive model, which simultaneously maximizes outputs and m...

Most organizations strive for improved performance, yet often these efforts fail to generate the expected results. Rather than focusing on specific tools and techniques for increasing efficiency, this research presents three studies that examine patterns in the way that employees perceive and enact change. Each study highlights a different pattern...

This handbook covers DEA topics that are extensively used and solidly based. The purpose of the handbook is to (1) describe and elucidate the state of the field and (2), where appropriate, extend the frontier of DEA research. It defines the state-of-the-art of DEA methodology and its uses. This handbook is intended to represent a milestone in the p...

This paper provides a sketch of some of the major research thrusts in data envelopment analysis (DEA) over the three decades since the appearance of the seminal work of Charnes et al. (1978) [Charnes, A., Cooper, W.W., Rhodes, E.L., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444]. The fo...

This paper covers some of the past accomplishments of DEA (Data Envelopment Analysis) and some of its future prospects. It starts with the “engineering-science” definitions of efficiency and uses the duality theory of linear programming to show how, in DEA, they can be related to the Pareto–Koopmans definitions used in “welfare economics” as well a...

In this chapter we expanded the ability of DEA to deal with variables that are not under managerial control but nevertheless affect performances in ways that need to be taken into account when effecting evaluations. Non-discretionary and categorical variables represent two of the ways in which conditions beyond managerial control can be taken into...

In this chapter we described the CCR model in some detail in both its input-oriented and output-oriented versions. 1. We also relaxed assumptions of a positive data set to semipositivity. 2. We defined the production possibility set based on the constant returns-to-scale assumption and developed the CCR model under this assumption. 3. The dual prob...

This chapter introduced the concept of super-efficiency and presented two types of approach for measuring super-efficiency: radial and non-radial. Super-efficiency measures are widely utilized in DEA applications for many purposes, e.g., ranking efficient DMUs, evaluating the Malmquist productivity index and comparing performances of two groups (th...

Observed data for use in DEA may suffer from non-controllable environmental effects and statistical noise. Hence, detaching these external effects from data and uncovering the true managerial efficiency are crucial for evaluating DMUs’ performance. In this chapter, we have covered these subjects and demonstrated a case study dealing with Japanese b...

In this chapter, we introduced two methods for measuring efficiency change over time: “window analysis” and “Malmquist index” using non-parametric DEA models.

In this chapter, we have covered two subjects; economies of scope and capacity utilization. 1. Economies of scope concerns the problem whether any significant difference in efficiency exists between specialized and diversified firms. The specialized firms produce different (specialized) products, whereas the diversified firms produce all of the con...

In this chapter we have introduced a consensus-making method in a multiple criteria environment using a combination of DEA and cooperative game theory. It is demonstrated that both DEA max and min games have the same Shapley value. Problems like the one exemplified in this chapter are usually solved by means of (among others) conventional custom a...

Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, And DEA-Solver Software, 2nd Edition is designed to provide a systematic introduction to DEA and its uses as a multifaceted tool for evaluating problems in a variety of contexts. Each chapter accompanies its developments with simple numerical examples and discuss...

This chapter presents some of the recently developed analytical methods for studying the sensitivity of DEA results to variations in the data. The focus is on the stability of classification of DMUs (Decision Making Units) into efficient and inefficient performers. Early work on this topic concentrated on developing algorithms for conducting such a...

Recent years have seen a great variety of applications of DEA (Data Envelopment Analysis) for use in evaluating the performances of many different kinds of entities engaged in many different activities in many different contexts in many different countries. One reason is that DEA has opened up possibilities for use in cases which have been resistan...

This paper covers some of the past accomplishments of DEA and its future prospects. It starts with the engineering-science definitions of efficiency and uses the duality theory of linear programming to show how, in DEA, they can be related to the Pareto-Koopmens definitions used in welfare economics. Some of the models that have now been developed...

A Benchmarking technique known as Data Envelopment Analysis (DEA) will be discussed as a jumpstart technique to put energy back into a company's Six Sigma program. A hypothetical example of benchmarking the production lines of a manufacturing company will be discussed using Six Sigma quality measures. Typical DEA software output will be discussed....

Efficiency evaluation proposed by Fare and Grosskopf is discussed. In this method the undesirable measures are treated by distinguishing between weak and strong disposability and using a directional distance function. This model allows to incorporate detailed preference information into efficiency evaluation through the weights. Weak disposability...

The current paper presents mathematical programming models for use in benchmarking where multiple performance measures are needed to examine the performance and productivity changes. The standard data envelopment analysis method is extended to incorporate benchmarks through (i) a variable-benchmark model where a unit under benchmarking selects a po...

This paper discusses returns to scale (RTS) in data envelopment analysis (DEA) for each of the presently available types of models. The BCC and CCR models are treated in input oriented forms while the multiplicative model is treated in output oriented form. (This distinction is not pertinent for the additive model which simultaneously maximizes out...

Congestion is a term that is applicable in a variety of disciplines which range from medical science to traffic engineering. It also has many uses in practical everyday life. This brings with it a certain looseness in usage. We therefore expand (and refine) our discussion of congestion with reference to its use in economics where we have access to...

This chapter discusses returns to scale (RTS) in data envelopment analysis (DEA). The BCC and CCR models are treated in input oriented forms while the multiplicative model is treated in output oriented form. (This distinction is not pertinent for the additive model which simultaneously maximizes outputs and minimizes inputs in the sense of a vector...

Data envelopment analysis (DEA) is a methodology for identifying the efficient frontier of decision making units (DMUs). Context-dependent DEA refers to a DEA approach where a set of DMUs are evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance level....

Invariance property in data envelopment analysis (DEA) allows negative data in efficiency analysis. In general, there are three cases of invariance under data transformation in DEA. The first case is the "classification invariance" where the classifications of efficiencies and inefficiencies are invariant to the data transformation. The second case...

Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple performance factors which are grouped into outputs and inputs. Once the efficient frontier is determined, inefficient DMUs can improve their performance to reach the efficient frontier by either increasing their current output levels or de...

A major objectiveof theNational ScienceFoundation (NSF) is to improve the nation's capacity for intellectual and economic growth. It does this by supporting the discovery of new knowledge and the enhancement of a skilled workforce. Both outcomes are supported by the different mechanisms of industry-university linkages available in NSF's GOALI initi...

Two approaches are presently available for the treatment of congestion in the data envelopment analysis (DEA) literature. Conditions for equivalence of these two approaches as well as conditions under which invalid results would be secured from one of these two approaches are set forth in Cooper, Seiford and Zhu (CSZ) (Färe et al., The Measurement...

This papersurveys recently developed analytical methods for studying thesensitivity of DEA results to variations in the data. The focusis on the stability of classification of DMUs (Decision MakingUnits) into efficient and inefficient performers. Early workon this topic concentrated on developing solution methods andalgorithms for conducting such a...

This paper develops a necessary and sufficient condition for the presence of (input) congestion. Relationships between the two congestion methods presently available are discussed. The equivalence between Färe et al. [12,13] and Brockett et al. [2] hold only when the law of variable proportions is applicable. It is shown that the work of Brockett e...

In a relatively short period of time Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. DEA has been successfully applied to a host of different types of entities engaged in a wide variety of activities in many contexts woridwide. This chapter discusses the fundamental D...

Data envelopment analysis (DEA) is a useful non-parametric method to evaluate a relative efficiency of multi-input and multi-output units based on observed data. In general, observed data have inherent uncertainty, however, it is difficult to treat the stochastic data in the conventional DEA model. It is required the development of stochastic DEA m...

Utilizing recent developments in data envelopment analysis (DEA), this paper examines the performance of the top 55 U.S. commercial banks via a two-stage production process that separates profitability and marketability. Substantial performance inefficiency is uncovered in both dimensions. Relatively large banks exhibit better performance on profit...

Sensitivity of the returns to scale (RTS) classifications in data envelopment analysis is studied by means of linear programming problems. The stability region for an observation preserving its current RTS classification (constant, increasing or decreasing returns to scale) can be easily investigated by the optimal values to a set of particular DEA...

The development of viable and challenging performance measurement systems in multi-unit organisations receives increased attention in recent years. Performance is no longer viewed as a static phenomenon that reflects the past history of operating systems. There is increasing appreciation for the value of performance scenarios which can be used to g...

The paper investigates the infeasibility of super-efficiency data envelopment analysis (DEA) models in which the unit under evaluation is excluded from the reference set. Necessary and sufficient conditions are provided for infeasibility of the super-efficiency DEA measures. By the returns to scale (RTS) classifications obtained from the standard D...

This paper discusses the determination of returns to scale (RTS) in data envelopment analysis (DEA). Three basic RTS methods and their modifications are reviewed and the equivalence between these different RTS methods is presented. The effect of multiple optimal DEA solutions on the RTS estimation is studied. It is shown that possible alternate opt...

This paper briefly traces the evolution of DEA from the initial publication by Chames, Cooper and Rhodes (1978) to the current state-of-the-art (SOA). The state of development of DEA is characterized at four points in time to provide a perspective in both directions—past and future. An evolution map is provided which illustrates DEA growth during t...

Data envelopment analysis(DEA) is a useful non-parametric method to evaluate a relative efficiency of multi-input and multi-output units based on observed data. In general, observed data have inherent uncertainty, however, it is difficult to treat the stochastic data in the conventional DEA model. It is required the development of stochastic DEA mo...

In data envelopment analysis (DEA) efficient decision making units (DMUs) are of primary importance as they define the efficient frontier. The current paper develops a new sensitivity analysis approach for the basic DEA models, such as, those proposed by Charnes, Cooper and Rhodes (CCR), Banker, Charnes and Cooper (BCC) and additive models, when va...

This paper develops a procedure for performing a sensitivity analysis of the efficient decision making units (DMUs) within the Charnes et al. (CCR) [European Journal of Operational Research 2 (1978) 429–444] model of data envelopment analysis (DEA). The procedure yields an exact `input stability region' and `output stability region' within which th...

Chang and Guh (1995) provide a so-called units invariant distance efficiency measure and argue that Sueyoshi and Chang's (1989) remedy for Charnes et al. (1982, 1983) is not units invariant. However, we show that (i) Chang and Guh (1995) misunderstand Sueyoshi and Chang's (1989) remedy, and (ii) the loglinear frontier determined by the Chang and Gu...

This paper further discusses the suggestion proposed by Troutt et al. [1] that data envelopment analysis (DEA) can be used to develop an acceptance boundary. It is shown that the linear programming model with an arbitrary objective function developed by Troutt et al. [1] can, in fact, be directly obtained from their assumptions. We develop a DEA-ty...

As a consequence of the 1978 Chinese economic reforms, there have been a number of changes within China's industry. First, firms have been given limited autonomy in management. Second, firms now have control over the scale of production and production profitability after fulfilling the required plan. Third, some production factors have become firm...

The paper investigates excesses and deficits in Chinese industrial productivity for the years (1953–1990), by combining data envelopment analysis (DEA) with other management science approaches. Improvement factors are examined with the incorporation of a priori information through Delphi, AHP and assurance region (AR) techniques. Various multiple i...

Abstract: Zhu and Shen [European Journal of Operational Research 81 (1995) 5901show that alternative optimal solutions in the estimation of returns to scale (RTS) are caused by a particular linear dependency among a set of extreme efficient DMUs when one employs the concept of most productive scale size [European Journal of Operational Research 17...

Since the original DEA study by Charnes, Cooper and Rhodes (1978), there has been a rapid growth in the field. Due to the interdisciplinary nature of much of the research, there is a need for a single source referencing the wide range of articles appearing in the literature. The author's intention in maintaining a bibliography of DEA-related articl...

This chapter provides the formulations for the basic DEA models and the editors' introduction to the chapters which follow.

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The problem of aggregating a set of ordinal rankings of n alternatives has given rise to a number of consensus models. Among the most common of these models are those due to Borda and Kendall, which amount to using average ranks, and the ℓ1 and ℓ2 distance models. A common criticism of these approaches is their use of ordinal rank position numbers...

The purpose of this paper is to briefly trace the evolution of DEA from the initial publication by Charnes et al. (1978b) to the current state of the art (SOA). The state of development of DEA is characterized at four points in time to provide a perspective in both directions—past and future. An evolution map is provided which illustrates DEA growt...

This paper presents a framework for incorporating ordinal data factors into the standard ratio DEA model. An application involving the prioritization of R&D projects is presented as a case in point where such ordinal factors appear in a natural way. Two different models for incorporating ordinal data are developed; one in which the ordinal factors...

This paper examines three essential components which comprise efficiency evaluation in data envelopment analysis. The three components are present in each DEA model and determine the implicit evaluation scheme associated with the model. These components provide a framework for classifying the various DEA models with respect to (i) the form of envel...

La très forte augmentation de faillites bancaires au cours de la dernière décennie a mené à la recherche d'indicateurs qui permettraient de prévenir (et éviter) des renflouages coûteux. Alors que la qualité de la gestion des banques est généralement admise comme un facteur clé de ces faillites, elle est le plus souvent exclue des modèles de dépista...

The extensions to DEA described in this chapter provide valuable techniques that not only allow one to fine tune the frontier but also make it possible to formulate DEA models that incorporate the modeling features of parametric analysis techniques. Thus the extensions make it possible to formulate DEA models that incorporate dummy variables and di...

Data Envelopment Analysis (DEA) is a body of concepts and methodologies that have now been incorporated in a collection of models with accompanying interpretive possibilities as follows: 1. the CCR ratio model (1978) (i) yields an objective evaluation of overall efficiency and (ii) identifies the sources and estimates the amounts of the thusidentif...

It is probably a truism that the lack of simple access to reliable DEA software packages has hampered the diffusion and wider application of DEA analyses. Although, in principle, DEA solutions can be obtained with convential linear programming software, in reality this task can be time-consuming. In principle, DEA solutions require the calculation...

Since the original DEA study by Charnes, Cooper, and Rhodes (1978), there has been rapid growth in the field. Due to the interdisciplinary nature of much of the research, there is a need for a single source referencing the wide range of articles appearing in the literature. The author’s intention in maintaining a bibliography of DEA-related article...

As is true with the application of any analytical approach to the “art of reckoning” (Eilon, 1984), the use of data envelopment analysis (DEA) requires knowledge about formulation of models, choice of variables, underlying assumptions, data representation, interpretation of results, and knowledge of limitations. In this chapter, we discuss the proc...

This book represents a milestone in the progression of Data Envelop­ ment Analysis (DEA). It is the first reference text which includes a comprehensive review and comparative discussion of the basic DEA models. The development is anchored in a unified mathematical and graphical treatment and includes the most important modeling ex­ tensions. In add...

The dramatic rise in bank failures over the last decade has led to a search for leading indicators so that costly bailouts might be avoided. While the quality of a bank's management is generally acknowledged to be a key contributor to institutional collapse, it is usually excluded from early warning models for lack of a metric. This paper presents...

The role of the non-Archimedean construct ∈ in the CCR and BCC models is clarified. It is established that the associated dual linear programs can be infeasible (for the multiplier side) and unbounded (for the associated dual envelopment side program). Sufficient conditions are established for feasibility and boundedness. Computational testing indi...

This work focuses on measuring and explaining producer performance. The contributors to this volume view performance as a function of the state of technology and economic efficiency. They show that insights can be gained by allowing for the possibility of a divergence between the economic objective and actual performance, and by associating this in...

In many problems involving efficiency analysis using DEA, certain factors may be measurable only on an ordinal scale. Specifically, it may be possible only to rank order the DMUs according to a factor, rather than being able to assign a specific numerical value of that factor to each DMU. To illustrate this, we examine a problem involving the evalu...

Data Envelopment Analysis (DEA) has received significant attention in recent years as a tool for measuring the relative efficiency of each member of a set of Decision Making Units (DMUs). Typically, a relatively large proportion of the DMUs will be credited with an efficiency score of 1, with no clear means of discriminating among such units. In a...

As a first step in outlining the contribution of Abraham Charnes to the field of statistics, it is necessary to delineate the subject area. Chernoff and Moses [46] in their book on decision theory describe statistics as follows: Years ago a statistician might have claimed that statistics deals with the processing of data…. today’s statistician will...

The transient output process from tandem qucueing systems with no wailing positions is analysed. Our system is subject to blocking; that is, a unit cannot move to the second queue until it completes service at the first queue and the second queue is not busy. Expressions arc derived for the expected output time of the nth unit when the system is in...

Incorporating the decision processes used by people in complex decision tasks is one of the most significant challenges facing designers of information systems. The need to capture and represent those decision processes is a fundamental part of the development of any information system and requires input from users. The purpose of this paper is to...