Wade D. Cook

• York University

The current research makes three main contributions to the DEA (Data Envelopment Analysis) literature. First, when using DEA to derive an efficiency score for a given DMU, it is normally assumed that each and every DMU has its own unique set of inputs and outputs, there are situations whereby a DMU can have a factor that is shared with other DMUs.....

Data envelopment analysis (DEA) is a methodology for evaluating efficiencies of decision-making units (DMUs) with each unit having its own set of inputs and outputs. However, there are situations where there can be an interdependence among the units. In a previous paper the authors examine efficiency measurement in a situation where university depa...

Sufficient numbers of decision-making units (DMUs) in comparison to the number performance measures that are classified as inputs and outputs has been a concern when applying data envelopment analysis (DEA) in real world settings. As the number of DMUs decreases (or the numbers of inputs and outputs increases) the discrimination power and accuracy...

In recent years, the continuous development of every country's economic activities has generated undesirable impacts on the environment. Common problems are high water and energy consumption rates, jointly with harmful pollution levels. This situation has gained the research community's interest in exploring and analyzing the extent to which initia...

The conventional DEA methodology is generally designed to evaluate the relative efficiencies of a set of comparable decision-making units (DMUs). An appropriate setting is one where all DMUs use the same inputs, produce the same outputs, experience the same operating conditions, and generally operate in similar environments. In many applications, h...

Project portfolio management (PPM) is an important area of interest in many organizations. There is a wide literature on each of many different aspects of PPM. The central purpose of the current paper is to focus on a specific sub-area of PPM, namely the project portfolio selection (PPS) problem. Specifically, we develop a new methodology that will...

Regional Innovation Systems (RISs) literature usually focuses on the comparative performance of different regions and analyzes how each region is utilizing its own dedicated resources. The available resources can be shared by many firms which are grouped by industry, just as universities collaborate with many firms in many industries. This paper st...

Balancing fairness and efficiency has become an emerging issue in today's society. In this paper we propose a balanced benchmarking methodology to address the fairness issue in performance evaluation. The methodology used to create performance measures is data envelopment analysis (DEA), a tool designed to evaluate the relative efficiencies of comp...

The measurement and monitoring of the efficiency of processes in organisations has become an important undertaking in today’s competitive environment. A fundamental tool for this undertaking is data envelopment analysis (DEA). The conventional setting for DEA views the decision-making unit (DMU) (school, hospital etc.) as a black box with inputs en...

In a large number of organizations, there is an ongoing need to evaluate performance. There is also a need to estimate the production frontier of best performers in such environments. Part of the difficulty in constructing this frontier is that massive amounts of data are generated daily with the result being that the set of best performers is cons...

Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of a set of decision-making units (DMUs). It is commonly assumed that the DMUs are independent of one another, in that each has its own quantities of a set of inputs and outputs. In case this assumption of independence of DMUs holds, decreasing the inputs of o...

Network data envelopment analysis (DEA) models the internal structures of decision-making units (DMUs). Unlike the standard DEA model, multiplier-based network DEA models are often highly non-linear and cannot be converted into linear programs. As such, obtaining a non-linear network DEA's global optimal solution is a challenge because it correspon...

Many operational processes that set out to create a specific set of products will often involve the creation of a set of associated co-products. The problem of interest is how to evaluate the efficiencies of a set of comparable such processes in the presence of both products and co-products. In particular, there has been an increasing interest in c...

Data Envelopment Analysis (DEA) is a methodology for evaluating the relative efficiencies of a set of decision-making units (DMUs), based on their multiple inputs and outputs. The original model is based on the assumption that DMUs operate independently of one another. However, this assumption may not apply in some situations, as in the case we pre...

In the traditional Data Envelopment Analysis (DEA) approach for a set of n Decision Making Units (DMUs), a standard DEA model is solved n times, one for each DMU. As the number of DMUs increases, the running-time to solve the standard model sharply rises. In this study, a new framework is proposed to significantly decrease the required DEA calculat...

Incentive plans involve payments for performance relative to some set of goals. In this paper, we extend Data Envelopment Analysis (DEA) to the evaluation of performance in the specific context of pay-for-performance incentive plans. The approach proposed ensures that the evaluation of performance of decision making units (DMUs) that follow the imp...

Incentive plans involve payments for performance relative to some set of goals. In this paper, we extend Data Envelopment Analysis (DEA) to the evaluation of performance in the specific context of pay-for-performance incentive plans. The approach proposed ensures that the evaluation of performance of decision making units (DMUs) that follow the imp...

Performing a high-quality manufacturing audit can be time consuming and costly given the large number and scale of manufacturing firms and enterprises. Chinese economy has experienced some rapid and significant growth over the past 30 years, which is largely due to contributions from the development of manufacturing industry. Auditing tools are ver...

An important area of research involving the benchmarking methodology data envelopment analysis (DEA), concerns the modeling of multistage situations. In the usual multistage settings, it is generally assumed that all decision-making units (DMUs) have the same number and configuration of stages. However, in many real-world examples, this assumption...

In data envelopment analysis (DEA), it is usually assumed that all data are continuous and not restricted by upper and/or lower bounds. However, there are situations where data are discrete and/or bounded, and where projections arising from DEA models are required to fall within those bounds. Such situations can be found, for example, in cases wher...

Electricity generation currently is the main industrial source of air emissions in the United States. Both researchers and practitioners are interested in conducting studies to evaluate the ecological performance of this industry, in order to propose solutions to curb emissions of air pollutants and to improve the efficiency of converting fossil re...

Data envelopment analysis (DEA) is a methodology used to measure the relative efficiencies of peer decision-making units (DMUs). In the original model, it is assumed that in a multiple input, multiple output setting, all members of the input bundle affect the entire output bundle. There are many situations, however, where this assumption does not h...

A balance between environmental regulation and economic prosperity has become a major issue of concern to attain a sustainable society in China. This study proposes the application of Data Envelopment Analysis (DEA) for measuring the efficiencies of the ecological systems in various regions of that country. The proposed approach differs from most o...

This paper evaluates the relative efficiencies of a set of economic activities in Mexico through time, with the aim of setting targets for their future performance for the year 2018. These evaluations are based upon parameters obtained by the economic census recorded by the INEGI (‘Instituto Nacional de Estad�ıstica y Geograf�ıa INEGI’ for Mexico);...

Cook and Zhu (2007) extend the DEA structure to apply to the more general setting where DMUs fall into distinct groups whose members experience similar circumstances. As these authors state, in those cases the DMUs of a group are to be treated uniformly in terms of multiplier allocation. In the same contextual setting, the present paper proposes a...

In the usual data envelopment analysis (DEA) setting, as pioneered by Charnes et al. (1978), it is assumed that a set of decision making units (DMUs) is to be evaluated in terms of their relative efficiencies in converting a bundle of inputs into a bundle of outputs. The usual assumption in DEA is that each output is impacted by each and every memb...

The data envelopment analysis (DEA) methodology is a benchmarking tool where it is generally assumed that decision making units (DMUs) constitute a homogeneous set; specifically, it is assumed that all DMUs have a common (input, output) bundle. In earlier work by the authors the issue of non-homogeneity on the output side was investigated. There we...

Reallocation of input resources (RIR) is a process by which certain decision making units (DMUs) reallocate resources among themselves; a process that occurs frequently in many enterprises. In this paper, a new data envelopment analysis (DEA) approach is developed to select the best cooperative partner DMU. Context-dependent DEA is used to identify...

This paper investigates efficiency measurement in a two-stage data envelopment analysis (DEA) setting. Since 1978, DEA literature has witnessed the expansion of the original concept to encompass a wide range of theoretical and applied research areas. One such area is network DEA, and in particular two-stage DEA. In the conventional closed serial sy...

In many settings, systems are composed of a group of independent sub-units. Each sub-unit produces the same set of outputs by consuming the same set of inputs. Conventional data envelopment analysis (DEA) views such a system as a "black-box", and uses the sum of the respective inputs and outputs of all relevant component units to calculate the syst...

Data envelopment analysis (DEA) provides a relative efficiency measure for peer decision making units (DMUs) with multiple inputs and outputs. WhileDEA has been proven an effective approach in identifying the best practice frontiers, its flexibility in weighting multiple inputs and outputs and its nature of self-evaluation have been criticized. The...

Data Envelopment Analysis (DEA) is a methodology for evaluating the relative efficiencies of a set of decision-making units (DMUs). The original model is based on the assumption that in a multiple input, multiple output setting, all inputs impact all outputs. In many situations, however, this assumption may not apply, such as would be the case in m...

In many managerial applications, situations frequently occur when a fixed cost is used in constructing the common platform of an organization, and needs to be shared by all related entities, or decision making units (DMUs). It is of vital importance to allocate such a cost across DMUs where there is competition for resources. Data envelopment analy...

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researc...

In this paper, we present a methodology for evaluating competing organizations in order to identify best practices among those organizations. We focus attention specifically on competitiveness in the context of a set of business schools for the purpose of identifying those that appear to be most efficient relative to their peers. One of the most wi...

The aim of the Journal of CENTRUM Cathedra (JCC): The Business and Economics Research Journal is to become an evergreen, favorable journal through disseminating high quality scholarly research articles to the pool of knowledge seekers in the field of business and economics; as well as play a vital role as a medium of exchange for transmitting and s...

The current paper examines the cross-efficiency concept in data envelopment analysis (DEA). While cross-efficiency has appeal as a peer evaluation approach, it is often the subject of criticism, due mainly to the use of DEA weights that are often non-unique. As a result, cross-efficiency scores are routinely viewed as arbitrary in that they depend...

There is a growing need to view performance in organizations in a more disaggregated sense, paying specific attention to different components of the operation. In this chapter we present models for deriving aggregate measures of bank-branch performance, with accompanying component measures that make up that aggregate value. The technical difficulty...

Recently network DEA models have been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together with intermediate measures. In general, there are two types of network DEA models. One is developed under t...

In many efficiency-measurement settings there are identifiable groups or clusters of DMUs whose impacts should be captured in the analysis. In such problem settings at least two issues need to be considered. The first is that there may be both DMU-level and cluster-level factors each of which should be considered in their proper settings. The secon...

The current chapter focuses on how to identify DEA frontier when decision making units (DMUs) are in forms of two-stage network processes. In these two stage network processes, all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. Due to the existence of intermediate measures, the usual procedur...

This chapter presents a Nash bargaining game model to measure the performance of two-stage decision making units (DMUs) in data envelopment analysis (DEA). The two stages are viewed as players to bargain for a better payoff, which is represented by DEA ratio efficiency score. The efficiency model is developed as a cooperative game model. It is show...

Data envelopment analysis (DEA) is a method for identifying best practices among peer decision making units (DMUs). An important area of development in recent years has been that devoted to applications wherein DMUs represent network processes. One particular subset of such processes is those in which all the outputs from the first stage become inp...

In conventional data envelopment analysis (DEA), decision making units (DMUs) are generally treated as a black-box in the sense that internal structures are ignored, and the performance of a DMU is assumed to be a function of a set of chosen inputs and outputs. A significant body of work has been directed at problem settings where the DMU is charac...

Data envelopment analysis (DEA), as originally proposed, is a methodology for evaluating the relative efficiencies of a set of homogeneous decision-making units (DMUs) in the sense that each uses the same input and output measures (in varying amounts from one DMU to another). In some situations, however, the assumption of homogeneity among DMUs may...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently network DEA models been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together w...

Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of peer decision-making units (DMUs), in a multiple input/output setting. Although it is generally assumed that all outputs are impacted by all inputs, there are many situations where this may not be the case. This article extends the conventional DEA methodol...

Data envelopment analysis (DEA), as originally proposed by Charnes et al. (1978) viewed the efficiency measurement problem as one wherein each of a set of DMUs uses the same input and output measures, albeit in amounts that vary from one DMU to another. In some situations, however, the assumption that all DMUs use the same measures may fail. While...

Conventional data envelopment analysis (DEA) methods assume that input and output variables are continuous. However, in many real managerial cases, some inputs and/or outputs can only take integer values. Simply rounding the performance targets to the nearest integers can lead to misleading solutions and efficiency evaluation. Addressing this kind...

The use of assurance region (AR) constraints to restrict multipliers in data envelopment analysis (DEA) is well-established, and has been discussed at length in the literature. The conventional assumption in imposing such restrictions is that they apply universally. Specifically, AR constraints on input multipliers are intended to control the relat...

Data envelopment analysis (DEA) is a methodology used to evaluate the relative efficiencies of peer decision-making units (DMUs) in multiple input, multiple output situations. In the original formulation, and in the vast literature that followed, the assumption was that all members of the input bundle affected the output bundle. However, many poten...

Special Issue Editors: Vincent Charles, Wade D. Cook, and Joe Zhu Foreword This special issue of the Journal of CENTRUM Cathedra (JCC) on data envelopment analysis and its applications to management coincides with the achievement of Triple Crown accreditation by CENTRUM Católica. The Triple Crown denotes business schools that have received accredi...

Data envelopment analysis (DEA) provides an optimization methodology for deriving an efficiency score for each member of a set of peer decision-making units. Under the original DEA model it was assumed that there is constant returns to scale (CRS). This idea was later extended to the more general case that allowed for variable returns to scale (VRS...

An important area of development in recent years in data envelopment analysis has been the applications wherein internal structures of DMUs are considered. For example, DMUs may consist of subunits or represent two-stage processes. One particular subset of such processes is those in which all the outputs from the first stage are the only inputs to...

Conventional applications of data envelopment analysis generally treat the Decision-Making Unit (DMU) as a black box in that the internal processes are not examined in detail. In some situations, such as the measurement of performance of a set of supply chains, the DMU can be viewed as exhibiting a network structure. A significant body of recent li...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs), where the internal structures of DMUs are treated as a black-box. Recently DEA has been extended to examine the efficiency of DMUs that have two-stage network structures or processes, where all the outputs from the first stage are intermed...

In conventional DEA analysis, DMUs are generally treated as a black-box in the sense that internal structures are ignored, and the performance of a DMU is assumed to be a function of a set of chosen inputs and outputs. A significant body of work has been directed at problem settings where the DMU is characterized by a multistage process; supply cha...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). An important area of development in recent years has been devoted to applications wherein DMUs represent two-stage or network processes. One particular subset of such processes is those in which all the outputs from the first stage are the...

We introduce a branch-and-cut algorithm to aggregate published journal rankings based on subsets of the accounting literature in order to create a consensus ranking. The aggregate ranking allows specialist and regional journals, which may only be ranked in a limited number of studies, to be placed with respect to each other and with respect to the...

A great deal of research in the information systems field has focused on the link between IT and firm-level outputs, like productivity and performance. This paper critically examines the economic assumptions and methodological approaches that underlie much of this work. Three important issues and gaps are identified. First, the functional form of t...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently DEA has been extended to examine the efficiency of two-stage processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model provides n...

Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision making units (DMUs). It is particularly useful where no a priori information on the tradeoffs or relations among various performance measures is available. However, it is very desirable if “evaluation standards,” when they can be establi...

The conventional model structures presented in the data envelopment analysis (DEA) literature view all variables as behaving in a linear fashion, meaning that regardless of the amounts, large or small, of a variable held by the set of decision-making units, we apply the same multiplier to those various amounts. In certain situations this linearity...

It is assumed in the standard DEA model that the aggregate output (input) is a pure linear function of each output (input). This means, for example, that if DMU j1 generates twice as much of an output as does another DMU j2, then the former is credited with having created twice as much value. In many situations, however, linear pricing (μryrj) may...

Kao and Hwang (2008) [Kao, C., Hwang, S.-N., 2008. Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research 185 (1), 418-429] develop a data envelopment analysis (DEA) approach for measuring efficiency of decision processes which can be divide...

The super-efficiency data envelopment analysis (DEA) model is obtained when a decision making unit (DMU) under evaluation is excluded from the reference set. This model provides for a measure of stability of the “efficient” status for frontier DMUs. Under the assumption of variable returns to scale (VRS), the super efficiency model can be infeasibl...

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...

We examine the cross-efficiency concept in data envelopment analysis (DEA). Cross efficiency links one decision-making unit’s (DMU) performance with others and has the appeal that scores arise from peer evaluation. However, a number of the current cross-efficiency approaches are flawed because they use scores that are arbitrary in that they depend...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). This tool has been utilized by a number of authors to examine two-stage processes, where all the outputs from the first stage are the only inputs to the second stage. The current article examines and extends these models using game theory...

As an extension to data envelopment analysis (DEA), cross-efficiency evaluation not only provides a ranking among the decision-making units (DMUs) but also eliminates unrealistic DEA weighting schemes without requiring a priori information on weight restrictions. A factor that possibly reduces the usefulness of the cross-efficiency evaluation metho...

Assurance region (AR) restrictions on multipliers in data envelopment analysis (DEA) have been applied extensively in many performance measurement settings. They facilitate the derivation of multiplier values that reflect the reality of the problem situation under study. In measuring the operational efficiency of bank branches, for example, output...

In standard data envelopment analysis (DEA), it is assumed that the input versus output status of each of the chosen analysis measures is known. In some situations, however, certain measures can play either input or output roles. Consider using the number of interns on staff in a study of hospital efficiency. Such a factor clearly constitutes an ou...

In conventional data envelopment analysis it is assumed that the input versus output status of each of the chosen performance measures is known. In some situations, however, certain performance measures can play either input or output roles. We refer to these performance measures as flexible measures. This paper presents a modification of the stand...

Standard data envelopment analysis (DEA) models cannot be used directly to measure the performance of a supply chain and its members, because of the existence of the intermediate measures connecting those members. This observation is true for any situations where DMUs contain multi-stage processes. This chapter presents several DEA-based approaches...

In data envelopment analysis (DEA), performance evaluation is generally assumed to be based upon a set of quantitative data. In many real world settings, however, it is essential to take into account the presence of qualitative factors when evaluating the performance of decision making units (DMUs). Very often rankings are provided from best to wor...

The paper consists of a theoretical discussion of the circumstances justifying the use of R&D to increase the productivity of a firm's manufacturing capability. There are three possibilities: Increase the productivity of existing capacity; re‐equip with purchased existing best‐practice technology; or use R&D to develop new technology. The choice de...

Abstract Peer review of research proposals and articles is an essential element in R&D processes world-wide. In most cases, each reviewer evaluates a small subset of the candidate proposals. The review board is then faced with the challenge of creating an overall \consensus" ranking on the basis of many partial rankings. In this paper we propose a...

The business case for hiring external IT consultants is compelling. Consultants can represent a rich source of valuable, short-term capabilities. From a resource-based perspective, however, the fungible nature of these capabilities argues against their long-term strategic value. Furthermore, IT consultants may be at odds with existing internal capa...

This paper presents an improved efficiency measurement tool by modifying the existing data envelopment analysis methodology to permit the incorporation of expert knowledge. A previous paper examined the inclusion of such knowledge within the additive model. This information appeared in the form of a binary classification of a subset of the decision...

In many real world applications where DEA is applied, DMUs can often be put into groups, such as those which may be under a single management team. This often means that the multipliers used within a group should be common across that group’s members. The case example examined in this regard is one involving a set of power plants, with each contain...

Over the recent years, we have seen a notable increase in interest in data envelopment analysis (DEA) techniques and applications. Basic and advanced DEA models and techniques have been well documented in the DEA literature. This edited volume addresses how to deal with DEA implementation difficulties involving data irregularities and DMU structura...

In a relatively short period of time, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. It has been successfully applied to a whole variety of problems in many different contexts worldwide. The analysis of an array of these problems has been resistant to other methodolo...

In data envelopment analysis (DEA), performance evaluation is generally assumed to be based upon a set of quantitative data. In many real world settings, however, it is essential to take into account the presence of qualitative factors when evaluating the performance of decision making units (DMUs). Very often rankings are provided from best to wor...

Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision-making units (DMUs). It is particularly useful when no a priori information is available on the trade-offs or relationships among various performance measures. A shortcoming of the DEA model, however, is its inability to provide a measur...

This paper examines the effectiveness of joint decision making within 87 pairs of buyer-supplier relationships in manufacturing. Joint decision making is an important attribute of a more cooperative supply chain relationship that may ultimately result in a better performance. Efficiency is modeled as a multiple criteria problem using Data Envelopme...

An appropriate performance measurement system is an important requirement for the effective management of a supply chain. Two hurdles are present in measuring the performance of a supply chain and its members. One is the existence of multiple measures that characterize the performance of chain members, and for which data must be acquired; the other...

This paper examines the problem of aggregating ordinal preferences on a set of alternatives into a consensus. This problem has been the subject of study for more than two centuries and many procedures have been developed to create a compromise or consensus.We examine a variety of structures for preference specification, and in each case review the...

In many real world applications involving performance measurement, it is necessary to deal with qualitative data factors. This chapter discusses the modeling of such factors within the DEA structure.

This paper presents a methodology for dealing with performance evaluation settings where factors can simultaneously play both input and output roles. Model structures are developed for classifying Decision-Making Units (DMUs) into three groups according to whether such a factor is behaving like an output, an input, or is in equilibrium, neither wan...

IMF policies have been widely criticized in the aftermath of the Asian crisis. Key critics questioned the appropriateness and the sequencing of financial liberalization programs which, along with insufficient monitoring and inadequate prudential regulations, left the financial sectors of the affected countries highly leveraged and exposed. This pap...

An issue of considerable importance, both from a practical organizational standpoint and from a costs research perspectives, involves the allocation of fixed resources or costs across a set of competing entities in an equitable manner. Cook and Kress (Eur. J. Oper. Res. 119 (1999) 652) propose a data envelopment analysis (DEA) approach to obtain a...