Performance Measurement and Evaluation for Sustainable Supply Chains using Rough Set and Data Envelopment Analysis

  • Chunguang Bai
  • Joseph Sarkis
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 174)


Performance measurement of sustainable supply chains is not a trivial issue. The complexities associated with measurement of supply chains is well known. Expanded sustainability measures for organizations and their supply chains only causes additional complexity. With many and varied measures helping organizations to distill information such that only the most pertinent and direct measures that provide information can make the process both more effective and efficient. In order to meet these goals this chapter provides an integrative approach that will help first distill and filter measures that are less redundant and then utilize these measures to arrive at a single performance value that managers can use for comparative and benchmarking analysis. We utilize a novel rough set theoretic approach and data envelopment analysis (DEA) as tools for distillation and integration of a variety of sustainable supply chainperformance measures. An example from sustainable supply chain data helps provide insights into the methodology.


Supply Chain Data Envelopment Analysis Data Envelopment Analysis Model Very High Relative Performance Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the National Natural Science Foundation of China Project (71102090)


  1. Bai C, Sarkis J (2010) Integrating sustainability into supplier selection with grey system and rough set methodologies. Int J Prod Econ 124(1):252–264CrossRefGoogle Scholar
  2. Bai C, Sarkis J et al (2012) Evaluating ecological sustainable performance measures for supply chain management. Supply Chain Manage: An Int J 17(1):78–92Google Scholar
  3. Banker RD (1984) Estimating most productive scale size using data envelopment analysis. Eur J Oper Res 17(1):35–44Google Scholar
  4. Beynon M, Curry B et al (2000) Classification and rule induction using rough set theory. Expert Syst 17(3):136–148CrossRefGoogle Scholar
  5. Chae B (2009) Developing key performance indicators for supply chain: an industry perspective. Supply Chain Manage: An Int J 14(6):422–428CrossRefGoogle Scholar
  6. Charnes A, Cooper WW et al (1978) Measurement in the efficiency of decision making units. Eur J Oper Res 2:429–444CrossRefGoogle Scholar
  7. Cooper WW, Seiford LM, Tone K (2007) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-Solver software, 2nd Edition. Springer, HeidelbergGoogle Scholar
  8. Gunasekaran A, Kobu B (2007) Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. Int J Prod Res 45:2819–2840CrossRefGoogle Scholar
  9. Hervani AA, Helms MM et al (2005) Performance measurement for green supply chain management. Benchmarking: An Int J 12(4):330–353CrossRefGoogle Scholar
  10. Hu Q, Yu D et al (2008a) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594CrossRefGoogle Scholar
  11. Hu Q, Yu D et al (2008b) Neighborhood classifiers. Expert Syst Appl 34(2):866–876CrossRefGoogle Scholar
  12. Liang JY, Shi ZZ (2004) The information entropy, rough entropy and knowledge granulation in rough set theory. Int J Uncertain Fuzz 12:37–46CrossRefGoogle Scholar
  13. Ong CS, Huang JJ et al (2005) Building credit scoring models using genetic programming. Expert Syst Appl 29(1):41–47CrossRefGoogle Scholar
  14. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356CrossRefGoogle Scholar
  15. Sadoyan H, Zakarian A et al (2006) Data mining algorithm for manufacturing process control. The Int J Adv Manuf Tech 28(3):342–350CrossRefGoogle Scholar
  16. Sarkis J (2000) A comparative analysis of DEA as a discrete alternative multiple criteria decision tool. Eur J Oper Res 123(3):543–557CrossRefGoogle Scholar
  17. Sarkis J (2007) Preparing your sata for DEA. In: Zhu ve J, Cook WD (eds) Modelling data irregulaties structural complexities in data development analysis. Springer, New York (Chapter 17)Google Scholar
  18. Shepherd C, H Gunter (2006) Measuring supply chain performance: current research and future directions. Int J Prod Perform Manage 55(3/4):242–258CrossRefGoogle Scholar
  19. Shyng JY, Wang FK et al (2007) Rough Set Theory in analyzing the attributes of combination values for the insurance market. Expert Syst Appl 32(1):56–64CrossRefGoogle Scholar
  20. Talluri S, Sarkis J,(2002) A model for performance monitoring of suppliers. Int J Prod Res 40(16):4257–4269CrossRefGoogle Scholar
  21. Tone K (2002) A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res 143(1):32–41CrossRefGoogle Scholar
  22. Wilson DR, Martinez TR (1997) Improved heterogeneous distance functions. J Artif Intell Res 6:1–34Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.School of Management Science and EngineeringDongbei University of Finance and EconomicsDalianPeople’s Republic of China
  2. 2.Graduate School of ManagementClark UniversityWorcesterUSA

Personalised recommendations