Efficiency decomposition in network data envelopment analysis: A relational model

Traditional studies in data envelopment analysis (DEA) view systems as a whole when measuring the efficiency, ignoring the operation of individual processes within a system. This paper builds a relational network DEA model, taking into account the interrelationship of the processes within the system, to measure the efficiency of the system and those of the processes at the same time. The system efficiency thus measured more properly represents the aggregate performance of the component processes. By introducing dummy processes, the original network system can be transformed into a series system where each stage in the series is of a parallel structure. Based on these series and parallel structures, the efficiency of the system is decomposed into the product of the efficiencies of the stages in the series and the inefficiency slack of each stage into the sum of the inefficiency slacks of its component processes connected in parallel. With efficiency decomposition, the process which causes the inefficient operation of the system can be identified for future improvement. An example of the non-life insurance industry in Taiwan illustrates the whole idea.

[1]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

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

[3]  R. Färe,et al.  Efficiency of a fixed but allocatable input: A non-parametric approach , 1997 .

[4]  R. Färe,et al.  PRODUCTIVITY AND INTERMEDIATE PRODUCTS: A FRONTIER APPROACH , 1995 .

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

[6]  Magnus Tambour,et al.  Productivity and customer satisfaction in Swedish pharmacies: A DEA network model , 1999, Eur. J. Oper. Res..

[7]  R. F. Rea,et al.  Network DEA , 1999 .

[8]  Bjarne S. Jensen,et al.  Dynamics, Economic Growth, and International Trade , 1998 .

[9]  M. Goto,et al.  Measurement of Dynamic Efficiency in Production: An Application of Data Envelopment Analysis to Japanese Electric Utilities , 2003 .

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

[11]  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..

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

[13]  L. Seiford,et al.  Profitability and Marketability of the Top 55 U.S. Commercial Banks , 1999 .

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

[15]  José Luis Zofío,et al.  Network DEA efficiency in input-output models: With an application to OECD countries , 2007, Eur. J. Oper. Res..

[16]  Thomas R. Sexton,et al.  Network DEA: efficiency analysis of organizations with complex internal structure , 2004, Comput. Oper. Res..

[17]  Rolf Färe,et al.  AN INTERMEDIATE INPUT MODEL OF DAIRY PRODUCTION USING COMPLEX SURVEY DATA , 1995 .

[18]  Shiuh-Nan Hwang,et al.  Measuring Managerial Efficiency in Non-Life Insurance Companies: An Application of Two-Stage Data Envelopment Analys , 2006 .