Data Envelopment Analysis

Defining partial preference functions which require up to three different threshold parameters for each sub-objective makes using PROMETHEE a relatively complex task. Estimating these parameters might be diffi-cult/impossible in many decision environments. Even if the parameters can be specified it is often hard to provide a sound analytical foundation for doing so. Additionally, partial preference rankings are more difficult to understand than additive preference functions and hence the results obtained are more difficult to communicate to stakeholders. 

Data Envelopment Analysis (DEA) is used to comparatively evaluate decision making units (DMUs) that convert inputs into outputs and identify sources and levels of relative inefficiency for every DMU with respect to each input and output (cf. Cooper 2001, p. 183). The approach was originally developed by Chames et al. (1978). DEA has received considerable attention in literature. The bibliography in Seiford (1994) already contains 

Using DEA requires three major steps. In a first step, the DMUs to be considered in the analysis have to be determined. These have to be homogeneous with respect to their tasks and the types of inputs and outputs used. The second step is to select the input and output factors that are to be used for the comparison. DEA also accommodates qualitative factors with the only requirement being that numerical values have to be assigned to all factors. Finally, a mathematical programming model such as the basic formulation provided below is solved separately for every DMU. 

The objective function (4.1) minimizes the efficiency measure E. For the smallest E obtained the slack variables are maximized. This objective hierarchy is achieved by including the very small parameter 8 that subordinates the maximization of the slack variables under the minimization of E. Equations (4.2) and (4.3) specify the output and input factor comparisons. The slack variables contain the surplus of output factors and underconsumption of input factors respectively as compared to the virtual DMU. The weight parameters 7ru are determined by the optimization model and describe the linear combination of real DMUs constituting the virtual DMU. Restriction (4.4) contains non-negativity constraints. 

The DEA model estimates an empirical production function which achieves the highest value of outputs that could be generated based on the input-output vectors of the DMUs analyzed. The efficiency of an individual DMU is measured by the distance of its input-output combination to this production function. An individual DMU is enveloped from above if the model can identify a combination of other output vectors for the same input vector that is at least as good as the one of the DMU considered for all output factors. Analogous it is enveloped from below if the model can identify a combination of other input vectors for the same output vector that requires less than or the same amounts of inputs as the one of the DMU considered. If a DMU cannot be enveloped by a combination of other DMUs it is efficient. In this case the measure of efficiency E takes on a value of 1 and the slack variables are zero. For inefficient DMUs the value of the efficiency measure indicates the extent to which all outputs could be increased or all inputs be decreased and the slack factors provide the absolute units by which specific inputs could be decreased/outputs increased in addition to the general increase/decrease if the DMU were to be brought to efficient performance levels. These improvements are only indicative of potential improvements because the projection to the efficient frontier can also be based on a virtual DMU.43 

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As discussed in Chapter 2.2.2 a broad range of criteria have to be considered if an in-depth assessment of individual sites is required. In practice matters are further complicated by the fact that the majority of sites host plants from multiple value chains. In this chapter a uniform decision support tool is developed to ensure consistent evaluations in all instances requiring site assessments. To this end Chapter 4.1 introduces the field of Multiple Criteria Decision Analysis (MCDA). Two different families of tools that could be applied to the decision problem at hand are discussed in greater detail in Chapters 4.2 and 4.3 respectively. As the use of Data Envelopment Analysis (DEA) for multiple criteria decision problems has been proposed in literature, too, the method is introduced in Chapter 4.4. An evaluation model for specialty chemicals production sites developed in cooperation with the industrial partner is presented in Chapter 4.5 and insights from application case studies are reported. 

Adler N, Friedman L, Sinuany-Stern Z (2002) Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research 140 249-265... 

Andersen P, Petersen NC (1993) A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science 39 1261-1264... 

Charnes A, Cooper WW, Lewin AY, Seiford LM (1994) Data Envelopment Analysis Theory, Methodoloy, and Application. Kluwer Academic Publishers, Boston et al. 

Doyle J, Green R (1993) Data Envelopment Analysis and Multiple Criteria Decision Making. Omega International Journal of Management Science 21 713-715... 

Lesney MS (2004) Paints, Pigmenty, and Dyes. Chemical Engineering News - Enterprise of the Chemical Sciences supplement 82 29-32 Lessard DR, Lightstone JB (1986) Volatile exchange rates can put operations at risk. Harvard Business Review 64 107-114 Lewin AY, Seiford LM (1997) Extending the frontiers of Data Envelopment Analysis. Annals of Operations Research 73 1-11 Li H, Clarke-Hill CM (2004) Sino-British joint ventures in China Investment patterns and host country conditions. European Business Review 16 44-63... 

Stewart TJ (1996) Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis. The Journal of the Operational Research Society 47 654-665... 

Tong L. Ding R.J. 2008. Efficiency assessment of coal mine safety input by data envelopment analysis. The Journal of China University of Mining Technology (18) 0088-0092. 

Multi-attribute utility models Analytic network process Data envelopment analysis In product development, the data that is available for decision making is often imprecise and fuzzy. MCDM models, however, often cannot effectively support decision making based on such imprecise information. To resolve this difficulty, fuzzy MADM methods are developed (Rao 2007). Fuzzy logic is a branch of mathematics that allows a computer to model the real world the same way that people do. It provides a simple way to reason with vague, ambiguous, and imprecise input or knowledge (Kahraman 2008). 

To analyse regulatory risk, all types of risk analyses is applicable from simplified standard risk analyses, to highly detailed analyses using simulations to investigate the effects of various future scenarios on the company situation in changing regulatory frameworks -e.g. through data envelopment analysis (DEA), etc. 

Regulatory risk -b + -b Coarse risk analysis Risk matrices, Simulation (e.g. data envelopment analysis)... 

Garcia, P. A. A., Schirru R. Frutuoso, P. F. 2005. A Fuzzy Data Envelopment Analysis Approach For FMEA. Progress in Nuclear Energy 46 N. 3-4 359-373. 

It is not the goal of this book to provide a detailed description of how AHP works, therefore, anyone interested in a comprehensive review of how to use AHP method, please refer to Saaty (1980). For a literature review on the integrated analytic hierarchy process and its applications, please refer to the work developed by Ho (2008), where he reviews the five tools that are commOTily combined with the AHP process, like mathematical programming, quality function deployment (QFD), meta-heuristics, SWOT analysis and data envelopment analysis (DEA). 

Norita A, Berg D, Simons G (2006) The integration of analytical hierarchy process and data envelopment analysis in a multi-criteria decision-making problem. Int J Inform Technol Decis Making 5(2) 263-276... 

Cooper, W.W., Seiford, L.M., Zhu, J./ HANDBOOK OF DATA ENVELOPMENT ANALYSIS Models and Methods... 

Pre-qualification reduces a large set of initial suppliers to a smaller set of acceptable suppliers for further assessment. De Boer et al. (2001) have cited many different techniques for pre-qualification. Some of these techniques are categorical methods, data envelopment analysis (DEA), cluster analysis, case-based reasoning (CBR) systems, and multi-criteria decision making method (MCDM). Several authors have worked on pre-qualification of suppliers. Weber and Ellram (1992) and Weber et al. (2000) have developed DEA methods for pre-qualification. Hinkel et al. (1969) and Holt (1998) used cluster analysis for pre-qualification and finally Ng and Skitmore (1995) developed CBR systems for pre-qualification. Mendoza et al. (2008) developed a three phase multi-criteria method to solve a general supplier selection problem. The paper combines analytic hierarchy process (AHP) with goal programming for both pre-qualification and final order allocation. 

Liu, R, R Y. Ding, and V. Lall. 2000. Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Management An International Journal. 5(3) 143-150. 

The paper proceeds with a discussion of the absorption capacity analysis in the context of comparative performance analysis. The following section is about Data Envelopment Analysis with specific emphasis on the CCR Model. Section fom presents the absorption capacity analysis of NUTS II regions in Turkey and discusses the results of the DEA model with respect to macroeconomic, administrative and financial dimensions of absorption capacity. The paper ends with the conclusion section. 

Data Envelopment Analysis (DEA) is a nonparametric, deterministic performance analysis tool. DEA is a "data oriented" approach for evaluating the performance of a set of peer units called Decision Making Units (DMUs) which convert multiple inputs into multiple outputs (Cooper et al., 2000). DEA is among the highly preferred methods of performance or efficiency analysis basically due to a number of advantages over parametric methods. Unlike most other approaches like regression analysis that need a priori assumptions, DEA requires very few assumptions. It does not attempt to explain the nature of the relations between the multiple inputs and multiple outputs that belong to the analysis units. 

The study focuses on the comparative performance evaluation of EU pre-accession fxmds from the point of absorption capacity. The absorption capacity concept is undertaken in three major dimensions namely, macroeconomic, adrriinistrative and financial. Relating to this objective, an analytical approach is proposed utilizing the data envelopment analysis. The method is implemented on the NUTS 11 regions in Turkey that have already enjoyed... 

Cooper, W.W., Seiford, L.M. Zhu, J. (2004). Data envelopment analysis history, models and interpretations. Handbook of Data Envelopment Analysis, W.W. Cooper, L.M. Seiford J. Zhu (ed.) pp.1-39. Kluwer Academic Publishers, Boston. 

Thanassoulis, E. (2001). Introduction to the theory and application of Data Envelopment Analysis A Foundation text with integrated software, Kluwer Academic Publishers, Boston. Zerbinati, S. (2004). Europeanization and EU Funding in Italy and England A Comparative Local Perspective, Journal of European Public Policy, vol. 11. No.6, pp. 1000-1019. 

There are several assessment tools for evaluating the safety (and health) performance of construction contractors. Ng et al. (2005) developed Safety Performance Evaluation (SPE), which was a safety performance assessment model used for evaluating construction contractors. El-Mashaleh et al. (2010) applied data envelopment analysis for evaluating construction contractors safety performance. 

A number of methods are available for preselection. These include ranking and weighting, benchmarking, statistical analysis, data envelopment analysis, analytical hierarchy process, and several artificial intelligence-based methods. 

Xu, J., Li, B. and Wu, D. 2009. Rough data envelopment analysis and its application to supply chain performance evaluation. International Journal of... 

Data Envelopment Analysis (DEA) fills this void (Cooper et al. 1999). The strength of DEA is that it lets each hospital choose the set of weights that would maximize its performance ratio. However, it is ranked against a virtual hospital which has its attribute values as the convex combination of the attribute values of all real hospitals. As each of the real hospital chooses the best set of weights for itself, the virtual hospital becomes the benchmark that cannot be outperformed. 

Cooper, W., Seiford, L., Tone, K. (1999). Data envelopment analysis. Boston Kluwer. 

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