215
Chapter 11
Determining Sources of
Relative Inefciency in
Heterogeneous Samples
Using Multiple Data
Analytic Techniques
Sergey Samoilenko
andKweku-Muata Osei-Bryson
Contents
Introduction ......................................................................................................216
DEA with CA and NNs .................................................................................... 219
CA and DEA ................................................................................................ 219
NNs and DEA ............................................................................................. 220
Description of the Methodology ...................................................................... 220
Description of Steps 3–5 of the Methodology ...............................................223
Step 3: Generate a Black Box Model of Transformative Capacity of
Each Cluster.............................................................................................223
Step 4: Obtain Simulated Sets of the Outputs for Each DMU in Each
Cluster .....................................................................................................223
Step 5: Determine the Sources of the Relative Ineciency of the
DMUs in the Sample ...............................................................................223
216Sergey Samoilenko and Kweku-Muata Osei-Bryson
Abstract: Data envelopment analysis (DEA) is widelyusedbyresearchers and prac-
titioners alike to assess relative performance of decision-making units (DMUs).
Commonly, the dierence in the scores of relative performance of DMUs in the
sample is considered to reect their dierences in the eciency of conversion of
inputs into outputs. In the presence of scale heterogeneity, however, the source of
the dierence in scores becomes less clear, for it is also possible that the dierence in
scores is caused by heterogeneity of the levels of inputs and outputs of DMUs in the
sample. In this chapter, we present and demonstrate a hybrid ve-step methodology
that involves the use of DEA, cluster analysis, and neural networks, and that allows
an investigator to determine the source of relative heterogeneity.
Keywords: Cluster Analysis, neural nets, data envelopment analysis, relative eciency,
heterogeneous samples.
Introduction
Data envelopment analysis (DEA) is a widely used nonparametric analytic tool (e.g.,
Asmild et al. 2007; Chen and van Dalen 2010; Doyle and Green 1994; Gillen and
Lall 1997; Khalili et al. 2010; Khouja 1995; Shao and Lin 2001), which is com-
monly applied in the research and practitioner communities to determine the rela-
tive eciencies of the decision-making units (DMUs). Any entity that receives a
set of inputs and produces a set of outputs could be designated as a DMU; thus,
any group of such entities could be subjected to DEA. As a result, this method
has been applied to evaluate productivity and performance of airports (Gillen and
Lall 1997; Martin and Roman 2001; Pels et al. 2001), eciency of US Air Force
maintenance units (Charnes et al. 1985), hospitals (Grosskopf et al. 2001; Gruca
Motivation for Steps 3 and 5 of the Methodology .........................................223
Motivation for Step 3 ...............................................................................223
Motivation for Step 5 ...............................................................................224
Illustrative Example ...........................................................................................225
Description of the Illustrative Dataset ........................................................... 225
Application of the Methodology to the Illustrative Dataset .......................... 226
Results of Step 1: Evaluate the Scale Heterogeneity Status of the Dataset ..... 227
Results of Step 2: Determine the Relative Eciency Status of Each DMU .... 228
Results of Steps 3 and 4: Generate Simulated Sets of the Outputs for
Each Cluster Based on Black Box Models of Transformative Capacity
Processes ................................................................................................. 228
Results of Step 5 .......................................................................................229
Discussion and Conclusion ...............................................................................230
References .........................................................................................................231
Appendix: Results of Cluster Analysis ................................................................236

Get Knowledge Discovery Process and Methods to Enhance Organizational Performance now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.