Joint Use of Factor Analysis (FA) and Data Envelopment Analysis (DEA) for Ranking of Data Envelopment Analysis

This article combines two techniques: data envelopment analysis (DEA) and Factor analysis (FA) to data reduction in decision making units (DMU). Data envelopment analysis (DEA), a popular linear programming technique is useful to rate comparatively operational efficiency of decision making units (DMU) based on their deterministic (not necessarily stochastic) input–output data and factor analysis techniques, have been proposed as data reduction and classification technique, which can be applied in data envelopment analysis (DEA) technique for reduction input – output data. Numerical results reveal that the new approach shows a good consistency in ranking with DEA.

Authors:

• Reza Nadimi
• Fariborz Jolai

Keywords:

• Effectiveness
• Decision Making
• Data EnvelopmentAnalysis
• Factor Analysis

References:

[1] A. Charnes, W.W. Cooper, E. Rhodes, "Measuring the efficiency of
decision making units", European Journal of Operations Research 2
(1978) 429 -444.
[2] L. Easton, D.J. Murphy, J.N. Pearson, "Purchasing performance
evaluation: with data envelopment analysis", European Journal of
Purchasing & Supply Management 8 (2002) 123-134.
[3] N. Adler, B. Golany, "Evaluation of deregulated airline networks using
data envelopment analysis combined with principal component analysis
with an application to Western Europe", European Journal of
Operations Research 132, (2001) 260-273.
[4] N. Adler, J. Berechman, "Measuring airport quality from the airlines-
viewpoint: an application of data envelopment analysis", Transport
Policy 8 (2001) 171-181.
[5] R.D.Banker, A.Charnes,W.W.Cooper, "Some models for estimating
technical and scale inefficiencies in data envelopment analysis",
Management Science 30(9)(1984)1079-1092
[6] How to perform and interpret Factor analysis using SPSS,
www.ncl.ac.Uk/iss/statistics/docs/Factoranalysis.html, 2002.
[7] J.Zhu, "Data envelopment analysis vs principal component analysis : An
illustrative study of economic performance of Chinese cities", European
Journal of Operation Research 111,(1998) 50-61.
[8] M.K. Epstein, J.C. Henderson, "Data envelopment analysis for
managerial control and diagnosis", Decision Science 20, (1989) 90-119.
[9] B.S. Everitt & G. Dunn, "Applied Multivariate Data Analysis", Edward
Arnold, London, pp304 (1991).
[10] T. Hastie, R. Tibshirani, "Discriminant analysis by Gaussian mixtures",
J. Roy. Statist. Soc, B 58 (1996) 155-176.
[11] J.D. Banfield, A.E. Raftery, "Model-based Gaussian and non-Gaussian
clustering", Biometrics, 49 (1993) 803-821.
[12] J.D. Banfield, A.E. Raftery, "Model-based clustering, discriminant
analysis, and density estimation", J. Amer. Statist. Assoc, 97 (2002)
611-631.
[13] D.G. Cal├▓, "Gaussian mixture model classification: a projection pursuit
approach", Comput. Statist. Data Anal., 52 (2007) 471-482.