Efficiency decomposition in network data envelopment analysis with slacks-based measures

Conventional data envelopment analysis (DEA) treats the production system as a black box when measuring efficiency, ignoring its internal structure. By taking the operations of the component processes of the system into consideration, several network DEA models have been developed. Of these, the slacks-based measure (SBM) approach has attracted much attention for its ability to provide suitable efficiency measures, especially for weakly efficient production units. This paper proposes a general SBM model for network systems, and is able to decompose the system efficiency into a weighted average of the process efficiencies. This relationship holds for all types of network structure. An example shows that the network model has stronger discriminating power than the conventional black-box model, and the system efficiency is indeed a weighted average of the process efficiencies. The decomposition of the system efficiency helps identify key factors to improve the performance of a production unit.

[1]  Han-Ying Kao,et al.  A Discriminative Multi-Objective Programming Method for Solving Network DEA , 2013 .

[2]  C. Kao,et al.  Efficiency decomposition for parallel production systems , 2012, J. Oper. Res. Soc..

[3]  Chiang Kao,et al.  Dynamic data envelopment analysis: A relational analysis , 2013, Eur. J. Oper. Res..

[4]  Joe Zhu,et al.  Network DEA: Additive efficiency decomposition , 2010, Eur. J. Oper. Res..

[5]  K. Tone,et al.  Dynamic DEA: A slacks-based measure approach , 2010 .

[6]  Joe Zhu,et al.  Additive efficiency decomposition in two-stage DEA , 2009, Eur. J. Oper. Res..

[7]  Hirofumi Fukuyama,et al.  Production , Manufacturing and Logistics Identifying the efficiency status in network DEA , 2012 .

[8]  Chiang Kao,et al.  Efficiency measurement for network systems: IT impact on firm performance , 2010, Decis. Support Syst..

[9]  A. Charnes,et al.  Classifying and characterizing efficiencies and inefficiencies in data development analysis , 1986 .

[10]  Chiang Kao,et al.  Efficiency decomposition in network data envelopment analysis: A relational model , 2009, Eur. J. Oper. Res..

[11]  Kaoru Tone,et al.  A slacks-based measure of super-efficiency in data envelopment analysis , 2001, Eur. J. Oper. Res..

[12]  Chiang Kao,et al.  Efficiency decomposition for general multi-stage systems in data envelopment analysis , 2014, Eur. J. Oper. Res..

[13]  Necmi Kemal Avkiran,et al.  Sensitivity analysis of network DEA: NSBM versus NRAM , 2012, Appl. Math. Comput..

[14]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[15]  Walter Ukovich,et al.  A classification of DEA models when the internal structure of the Decision Making Units is considered , 2010, Ann. Oper. Res..

[16]  A. Charnes,et al.  The non-archimedean CCR ratio for efficiency analysis: A rejoinder to Boyd and Färe☆ , 1984 .

[17]  Chiang Kao,et al.  Efficiency measurement for parallel production systems , 2009, Eur. J. Oper. Res..

[18]  Kaoru Tone,et al.  Network DEA: A slacks-based measure approach , 2009, Eur. J. Oper. Res..

[19]  William L. Weber,et al.  A slacks-based inefficiency measure for a two-stage system with bad outputs , 2010 .

[20]  Chiang Kao,et al.  Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks , 2014 .

[21]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[22]  R. Färe,et al.  Intertemporal Production Frontiers: With Dynamic DEA , 1996 .

[23]  Chiang Kao,et al.  Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan , 2008, Eur. J. Oper. Res..

[24]  Kaoru Tone,et al.  Dynamic DEA with network structure: A slacks-based measure approach , 2013 .