Data Envelopment Analysis for Managerial Control and Diagnosis*

Data envelopment analysis (DEA) is a linear programming-based technique that converts multiple input and output measures into a single comprehensive measure of productive efficiency. This is accomplished via the construction of an empirically based production frontier and by the identification of peer groups. Each unit is evaluated by comparison against a composite unit that is constructed as a convex combination of other units in its peer group. DEA has now been applied in a variety of managerial contexts. In this paper we draw on theories of decision making, measurement and control, the mathematical properties of DEA, prior reported applications, and our own experience, to assess the potential of DEA as a general management tool. We first make the distinction between managerial diagnosis and control. We show how measurement requirements differ for these two managerial decision contexts, and argue that DEA has the potential to provide support in each context. Measurement and decision support criteria for each activity are then developed by reference to the literature on diagnosis and control. Based on its mathematical definition and properties, the relevant attributes of DEA are then derived. The technique is evaluated in each capacity by comparison to the appropriate set of criteria. This evaluation is supported with evidence from our experience with DEA in a large public-sector organization. We argue that the structural properties of DEA, critical managerial choices in its application, and situationally specific factors, interact to determine the strengths and limitations of DEA in each decision context. Implications for research and practice are discussed.     

A potential use of data envelopment analysis for the inverse classification problem

We propose a methodology that uses data envelopment analysis (DEA) for solving the inverse classification problem. An inverse classification problem involves finding out how predictor attributes of a case can be changed so that the case can be classified into a different and more desirable class. For a binary classification problem and non-negative decision-making attributes, we show that under the assumption of conditional monotonicity, and convexity of classes, DEA can be used for inverse classification problem. We illustrate the application of our proposed methodology on a hypothetical and a real-life bankruptcy prediction data.     

Evaluating productive performance: A new approach based on the product-mix problem consistent with Data Envelopment Analysis

We propose a new approach to estimate technical coefficients from a set of Decision Making Units (DMUs) under the assumption that their production plans are set by process engineers through Linear Programming (LP) techniques. The idea behind this approach is that most manufacturing and agricultural firms routinely resort to LP-based modeling in their decision making processes in order to plan output production and, therefore, this particularity should be taken into account when estimating their technical efficiency. A usual model of LP for these sectors is the so-called product-mix problem, which we relate to a standard Data Envelopment Analysis (DEA) model in terms of the Directional Distance Function. In this paper, we finally show how to estimate the technical coefficients of a sample of Andalusian farms in Spain and how this information can be seen as a complement to the usual by-products associated with estimating technical efficiency by DEA.     

Benchmarking of maintenance and outage repair in an electricity distribution company using the value-based DEA method

Benchmarking of electricity distribution utilities has been widely used as a means to contribute for the adoption or reinforcement of enhanced competitiveness and innovation practices to optimize costs, increase customer satisfaction, improve corporate image and maximize profits. The purpose of this paper is to present a benchmarking study for the maintenance and outage repair activity carried out by a Portuguese electricity distribution company, EDP Distribuição (EDP-D), using the value-based DEA method, which builds on links between data envelopment analysis (DEA) and multiple criteria decision analysis (MCDA). This study illustrates the impact of the incorporation of managerial preferences in the classification and ranking of 40 network areas served by EDP-D, confronting the results with a previous study based on a BCC DEA model. In order to deal with the underlying uncertainty, the value-based DEA method for performance evaluation is adapted to include the concept of super-efficiency. Besides identifying best practices, sources of inefficiency, gaps relatively to best practices and opportunities for improvement, this analysis supports the introduction of corrective measures and informs decisions about future goals.     

Ranking using η-efficiency and relative size measures based on DEA

In this paper, we propose a new way to define the size of organizations or Decision Making Units (DMUs) based on Data Envelopment Analysis (DEA). A great number of managerial decisions, social and economic activities, political analysis and engineering process include in their analysis some information about the size of corporations. Usually, organizations consider particular attributes such as revenues, employees, number of clients, profit and so forth to evaluate their sizes, which could result on an incomplete and/or partial size evaluation. Here, we investigate this question more deeply and propose a non-parametric approach for ranking size. To address this issue, we first construct a DEA super-efficiency target vector defined over an extended Technology set and find a common set of weights to determine new efficiencies and sizes, possibly greater than one. After, we define rotation and translation of DMUs and show how to construct a range area, associated to η-efficiencies and relative sizes that are less than or equal to one. Additionally, based on managerial decisions, we show how to use these ideas to achieve new η-efficiency and relative size goals for one or more DMUs in the set. We consider an empirical application to illustrate our approach on a dataset of international airlines and conclude the paper with some final remarks and future directions.     

Benchmarking firm performance from a multiple-stakeholder perspective with an application to Chinese banking

We know very little about how a firm would compare against its peers when evaluated from a multiple-stakeholder perspective where the same variables are interpreted differently. Since most medium-to-large organizations acknowledge the multi-dimensional nature of their operations, finding out the performance evaluations of various stakeholders can inform managerial decision-making. Thus, the main motivation for this study is to capture the interactions among different perceptions on a common set of performance measures. Using data envelopment analysis (DEA), we work with an approach that allows a flexible designation of inputs and outputs based on varying perspectives of five key stakeholders in banking. The versatile approach demonstrates that different views from the stakeholder universe can be summarily captured in DEA scores. A numerical example on Chinese commercial banks identifies the compliant (efficient) banks versus rigid (inefficient) banks, as well as the amenable stakeholders (those evaluating banks as efficient) versus the recalcitrant stakeholders (those evaluating banks as inefficient). The aligned views held by management and shareholders as evidenced by significant correlation among performance rankings imply reduced agency costs. Similarly, shared perceptions between customers and employees may encourage management to examine how this important business interface can be improved.     

A risk perception model of alliance structuring

The literature on the strategic alliance structuring process does not provide an adequate view of the role of decision-makers in that process, especially about how alliance partners form their structural preferences. Given the critical role of structure in alliance management, the decision-making process regarding the choice of an appropriate alliance structure deserves particular research attention. We propose a model of strategic alliance structuring that has managerial risk perception as its core. Our model consists of the following parts: the antecedents of risk perception, relational risk and performance risk, risk perception and structural preference, and the resolution of preferences. We suggest that the structural preferences of partners are based on their perceptions of relational risk and performance risk, and the overall objective is to minimize the total risk. We also develop a number of propositions as elaboration of the model to facilitate empirical research and the effective management of the structuring process.     

Set-valued DEA production games

In this paper, a generalization of the linear production model is considered on the basis of a DEA-inspired technology in which the maximization of the production levels is formulated as a multi-objective linear programming problem. When multiple decision-makers cooperate by pooling their resources and sharing their technologies in the production process, the final production must then be divided between the agents involved, and a multi-commodity game arises. Such a game is referred to as a set-valued DEA production game. It is shown that, by adopting two different excess functions to measure the dissatisfaction of the coalitions, two different core concepts emerge, namely the preference core and the dominance core. Moreover, we provide the procedure to determine allocations in the respective least cores and show how to compute the nucleolus in the case of the excess function leading to the preference core. Finally, the results are illustrated with a case study.     

一个新的考虑非期望产出的非径向-双目标DEA模型

如何实现对环境效率更准确的定量评价,是国际上从事DEA研究的学者们当前关注的问题之一。在传统DEA效率模型的基础上,综合考虑评价过程中期望产出与非期望产出之间的差异以及实际生产过程中人们追求期望产出最大化和非期望产出最小化的双重目标,构建非期望产出的非径向-双目标DEA环境效率评价模型,通过线性加权和法转化为一个求最大值的单目标线性规划问题。研究结果表明,这种新模型不仅可以分析DEA有效性与Pa-reto最优之间的关系,还利用决策单元的投影获得投入和产出的可调整量,从而提高考虑非期望产出的环境效率评价的精度。实证分析结果与现实情况的高度吻合,说明这种新的非径向-双目标DEA环境效率模型是有效的。     

Efficiency evaluations with context-dependent and measure-specific data envelopment approaches: An application in a World Bank supported project

We evaluate the efficiency of decision making units (DMUs) in a World Bank supported Social Risk Mitigation Project (SRMP) in Turkey through context-dependent and measure-specific data envelopment analysis (DEA) approaches. The results suggest that the efficiency evaluations with context-dependent and measure-specific DEA play various roles in an organization such as setting attainable targets to DMUs, setting long and short term targets to DMUs separately, grouping of DMUs, and improving internal competition between DMUs. Four main contributions of this study can be summarized as follows. Firstly, the study shows the applicability of context-dependent and measure-specific DEA methodologies in a World Bank supported large scale project to increase the effectiveness of the project. Secondly, it outlines some important managerial conclusions of context-dependent DEA clustering approach. Moreover, we propose an alternative approach for attractiveness scores computations in case of exogenous group formations. Finally, the study proposes and applies measure-specific version of context-dependent DEA approach.     

基于交叉评价策略的DEA全局协调相对效率排序模型

在DEA(数据包络分析)研究领域,建构在交叉效率概念基础上的现有决策单元排序方法仅以定义的方式给出了用于决策单元排序的交叉效率评价值。对于这种方法构建方式,分别基于管理学的效率概念和多属性决策理论,分析指出其中的交叉效率评价值从本质上讲既与效率的管理学概念不符,也与决策单元的优劣不存在理性逻辑联系。为克服现有决策单元排序方法所存在的上述问题,基于交叉评价策略和效率的管理学概念内涵给出了DEA全局协调相对效率的新概念,在此基础上利用优化理论给出了可以用于决策单元优劣排序的DEA全局协调相对效率测度模型,并通过理论分析和数值案例验证解释了该模型相对于现有决策单元排序方法所拥有的比较优势。     

Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture

In this paper we consider the use of data envelopment analysis (DEA) for the assessment of efficiency of units whose output profiles exhibit specialisation. An example of this is found in agriculture where a large number of different crops may be produced in a particular region, but only a few farms actually produce each particular crop. Because of the large number of outputs, the use of conventional DEA models in such applications results in a poor efficiency discrimination. We overcome this problem by specifying production trade-offs between different outputs, relying on the methodology of Podinovski (J Oper Res Soc 2004;55:1311–22). The main idea of our approach is to relate various outputs to the production of the main output. We illustrate this methodology by an application of DEA involving agricultural farms in different regions of Turkey. An integral part of this application is the elicitation of expert judgements in order to formulate the required production trade-offs. Their use in DEA models results in a significant improvement of the efficiency discrimination. The proposed methodology should also be of interest to other applications of DEA where units may exhibit specialization, such as applications involving hospitals or bank branches.     

PRODUCT OFFERING,PRICING, AND MAKE‐TO‐STOCK/MAKE‐TO‐ORDER DECISIONS WITH SHARED CAPACITY

In an era of mass customization, many firms continue to expand their product lines to remain competitive. These broader product lines may help to increase market share and may allow higher prices to be charged, but they also cause challenges associated with diseconomies of scope. To investigate this tradeoff, we considered a monopolist who faces demand curves, which for each of its potential products, decline with both price and response time (time to deliver the product). The firm must decide which products to offer, how to price them, whether each should be make‐to‐stock (mts) or make‐to‐order (mto), and how often to produce them. The offered products share a single manufacturing facility. Setup times introduce disceonomies of scope and setup costs introduce economies of scale. We provide motivating problem scenarios, model the monopolist's problem as a non‐linear, integer programming problem, characterize of the optimal policy, develop near‐optimal procedures, and discuss managerial insights.     

THE IMPACT OF UNIONS ON OPERATIONS STRATEGY

This paper explores the effect that unions have on a firm's ability to reengineer manufacturing processes. We begin by exploring the various effects that a union may have in a manufacturing environment. Next, we briefly review how unions may affect managerial initiatives to reengineer processes and improve manufacturing performance. The third section analyzes an existing database to test for differences in cycle time and manufacturing performance between union and nonunion firms. Finally, we discuss the implications of the study for future operations strategy research and note how a different form of union‐management relationships is beginning to evolve.     

Channel coordination in supply chains with agents having mean-variance objectives

This paper investigates the issues of channel coordination in a supply chain when the individual supply chain decision makers take mean-variance (MV) objectives. We propose an MV formulation to capture the risk preference of each individual supply chain agent. Through the studies of a wholesale pricing policy, we find that the incorporation of risk concerns into the setting of supply chain coordinating policy is very important because it can substantially affect the achievability of channel coordination. It is also interesting to find that channel coordination depends on how big the net difference between the risk preferences of the supply chain coordinator and the retailer is. Thus, a slightly risk averse supply chain coordinator can successfully coordinate with a slightly risk prone retailer but not a very risk averse retailer. Numerical analyses are included and managerial insights are developed.     

Robustness and Separation in Multidimensional Screening

A principal wishes to screen an agent along several dimensions of private information simultaneously. The agent has quasilinear preferences that are additively separable across the various components. We consider a robust version of the principal's problem, in which she knows the marginal distribution of each component of the agent's type, but does not know the joint distribution. Any mechanism is evaluated by its worst‐case expected profit, over all joint distributions consistent with the known marginals. We show that the optimum for the principal is simply to screen along each component separately. This result does not require any assumptions (such as single crossing) on the structure of preferences within each component. The proof technique involves a generalization of the concept of virtual values to arbitrary screening problems. Sample applications include monopoly pricing and a stylized dynamic taxation model.     

Creative Search and Strategic Sense-making: Missing Dimensions in the Concept of Dynamic Capabilities

In this paper we discuss the role of managerial agency in creating and shaping dynamic capabilities. We argue that dynamic capability is a phenomenon that enables a deviation to take place from the knowledge that otherwise would have arisen cumulatively from experiential learning. In addition we argue that to create major changes in patterns of knowledge accumulation managers need to be purposefully and creatively engaged. Such agency is identifiable in two cognitive processes we call creative search and strategic sense-making. We show how these processes differ in respect to their temporal orientation and relationship to uncertainty and by adopting a process perspective we demonstrate how creative search, strategic sense-making and experiential learning are complementary. This notion of complementarity implies that these processes, notwithstanding their contrasting characteristics, coexist together and serve to offer an explanation for how knowledge progresses at a firm level. Variance is introduced into the framework proposed through the identification of factors that influence the coexistence of creative search and strategic sense-making. The argument developed is illustrated through the use of an emergent technology context.     

Integrated retail decisions with multiple selling periods and customer segments: Optimization and insights

Integrating retail decisions on such aspects as assortment, pricing, and inventory greatly improves profitability. We examine a multi-period selling horizon where a retailer jointly optimizes assortment planning, pricing, and inventory decisions for a product line of substitutable products, in a market with multiple customer segments. Focusing on fast-moving retail products, the problem is modeled as a mixed-integer nonlinear program where demand is driven by exogenous consumer reservation prices and endogenous assortment and pricing decisions. A mixed-integer linear reformulation is developed, which enables an exact solution to large problem instances (with up to a hundred products) in manageable times. Empirical evidence is provided in support of a classical deterministic maximum-surplus consumer choice model. Computational results and managerial insights are discussed. We find that the optimal assortment and pricing decisions do not exhibit a simple, intuitive structure that could be analytically characterized, which reflects the usefulness of optimization approaches to numerically identify attractive trade-offs for the decision-maker. We also observe that suboptimal inventory policies significantly decrease profitability, which highlights the importance of integrated decision-making. Finally, we find that the seasonality of consumer preferences and supply costs present an opportunity for boosting the profit via higher inventory levels and wider assortments.     

Enhancing standard performance practices with DEA

Data envelopment analysis (DEA) can be interpreted as a natural generalization of elemental practices for efficiency and performance evaluation used in diverse areas, some of which have evolved independently of each other and of the traditional efficiency framework of Charnes et al. [Measuring the efficiency of decision making units. European Journal of Operational Research 1978;2(6):429–44]. The paper aims to show that some standard “real-world” procedures employed by practitioners in the fields of retailing, finance, and social science can be interpreted as unconscious applications of DEA, and that such analyses could be enhanced by the explicit use of DEA. A supplementary aim is to demonstrate that DEA can be derived from concrete examples and first principles. The choice of the examples covers the two typical returns to scale environments of constant returns to scale (CRS) and variable returns to scale (VRS). Motivating and deriving DEA as a generalization of established performance evaluation practices will help to explain and disseminate this methodology to practitioners and to those who find their methods familiar.     

The Steiner cycle and path cover problem on interval graphs

The Steiner path problem is a common generalization of the Steiner tree and the Hamiltonian path problem, in which we have to decide if for a given graph there exists a path visiting a fixed set of terminals. In the Steiner cycle problem we look for a cycle visiting all terminals instead of a path. The Steiner path cover problem is an optimization variant of the Steiner path problem generalizing the path cover problem, in which one has to cover all terminals with a minimum number of paths. We study those problems for the special class of interval graphs. We present linear time algorithms for both the Steiner path cover problem and the Steiner cycle problem on interval graphs given as endpoint sorted lists. The main contribution is a lemma showing that backward steps to non-Steiner intervals are never necessary. Furthermore, we show how to integrate this modification to the deferred-query technique of Chang et al. to obtain the linear running times.