Efficiency decomposition in network data envelopment analysis

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

In order to measure the efficiency of systems composed of several processes more appropriately, various network data envelopment analysis (DEA) models have been developed. One type of the model, which is able to calculate the system and process efficiencies at the same time, is relational. This paper discusses the relationship between the system and process efficiencies measured from this model, and derives five properties. The first is general to all types of network structure, which states that the efficiency slack of the system is the sum of those of the component processes. This implies that a system is efficient if and only if all its component processes are. The second to fourth correspond to three types of structure, series, parallel, and dynamic. The last states that any unstructured system can be transformed into a series of parallel structures for efficiency decomposition. Numerical examples are used to help explain the idea of each type of decomposition. Efficiency decomposition enables decision makers to identify the processes that cause the inefficiency of a system, and thus to make effective changes to it.

Original languageEnglish
Title of host publicationInternational Series in Operations Research and Management Science
PublisherSpringer New York LLC
Pages55-77
Number of pages23
DOIs
Publication statusPublished - 2014 Jan 1

Publication series

NameInternational Series in Operations Research and Management Science
Volume208
ISSN (Print)0884-8289

All Science Journal Classification (ASJC) codes

  • Management Science and Operations Research
  • Strategy and Management
  • Applied Mathematics
  • Computer Science Applications
  • Software

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    Kao, C. (2014). Efficiency decomposition in network data envelopment analysis. In International Series in Operations Research and Management Science (pp. 55-77). (International Series in Operations Research and Management Science; Vol. 208). Springer New York LLC. https://doi.org/10.1007/978-1-4899-8068-7_3