Skip to main content
SpringerLink
Log in

Prioritization of Supply Chain Performance Measurement Factors by a Fuzzy Multi-criteria Approach

  • Chapter
  • First Online:
Applications of Multi-Criteria and Game Theory Approaches

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

Abstract

Measurement of supply chain performance is an important issue to identify success, to understand processes, to figure out problems and where improvements are possible as well as provide facts for decision-making. Using classical performance measurement techniques, it may not be possible to incorporate judgments of decision makers comprehensively. Hence, we propose a fuzzy multi-criteria evaluation method for this purpose in the framework of supply chain performance measurement. Fuzzy DEMATEL is used to prioritize the performance measurement criteria of supply chain. We also present a sensitivity analysis using different linguistic scales.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Angerhofer BJ, Angelides MC (2006) A model and a performance measurement system for collaborative supply chains. Dec Supp Syst 42(1):283–301

    Article  Google Scholar 

  • Bai C, Sarkis J (2012) Supply–chain performance–measurement system management using neighbourhood rough sets. Int J Prod Res 50(9):2484–2500

    Article  Google Scholar 

  • Beamon BM (1999) Measuring supply chain performance. Int J Oper Prod Mgmt 19(3):275–292

    Article  Google Scholar 

  • Berrah L, Cliville V (2007) Towards an aggregation performance measurement system model in a supply chain context. Comput Ind 58(7):709–719

    Article  Google Scholar 

  • Bhagwat R, Sharma MK (2007a) Performance measurement of supply chain management using the analytical hierarchy process. Prod Plan Con 18(8):666–680

    Article  Google Scholar 

  • Bhagwat R, Sharma MK (2007b) Performance measurement of supply chain management: A balanced scorecard approach. Comput Ind Eng 53(1):43–62

    Article  Google Scholar 

  • Chang B, Chang CW, Wu CH (2011) Fuzzy DEMATEL method for developing supplier selection criteria. Expert Syst Appl 38(3):1850–1858

    Article  Google Scholar 

  • Chang YH, Yeh CH, Cheng JH (1998) Decision support for bus operations under uncertainty: A fuzzy expert system approach. Omega 26(3):367–380

    Article  Google Scholar 

  • Chen LH, Chiou TW (1999) A fuzzy credit–rating approach for commercial loans: A Taiwan case. Omega 27(4):407–419

    Article  MathSciNet  Google Scholar 

  • Chen SJ, Hwang CL, Hwang FP (1992) Fuzzy multiple attribute decision making: methods and applications. Lecture notes in economics and mathematical systems, Springer–Verlag, Berlin

    Google Scholar 

  • Chibba A (2007) Measuring supply chain performance measures prioritizing performance measures. Licentiate thesis, Luleå University of Technology, Sweden

    Google Scholar 

  • Christopher M (1992) Logistics and supply chain management. Pitman Publishing, London

    Google Scholar 

  • Christopher M, Towill D (2000) Supply chain migration from lean and functional to agile and customized. Supply Chain Mgmt: Int J 5(4):206–213

    Article  Google Scholar 

  • Fisher ML (1997) What is the right supply chain for your product?. Harvard Bus Rev March–April Reprint number: 97205, pp 105–116

    Google Scholar 

  • Gabus A, Fontela E (1972) World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Centre, Switzerland

    Google Scholar 

  • Gabus A, Fontela E (1973) Perceptions of the world problematic: communication procedure, communicating with those bearing collective responsibility (DEMATEL report no. 1). Battelle Geneva Research Centre, Switzerland

    Google Scholar 

  • Gunasekaran A, Kobu, B (2007) Performance measures and metrics in logistics and supply chain management: a review of recent literature for research and applications. Int J Prod Res 45(12): 2819-2840

    Google Scholar 

  • Gunasekaran A, Patel C, Tittiroglu E (2001) Performance measures and metrics in a supply chain environment. Int J Oper Prod Mgmt 2(1–2):71–87

    Article  Google Scholar 

  • Hausman WH (2004) Supply chain performance metrics. In: Harrison TP, Lee HL and Neale JJ (eds) The practice of supply chain management: where theory and application converge. Springer Science and Business, New York, pp 61–73

    Google Scholar 

  • Hess P, Siciliano J (1996) Management: responsibility for performance. McGraw–Hill, New York

    Google Scholar 

  • Hoehn W (2003) Managing organizational performance: linking the balanced scored to a process improvement technique. In: Proceedings of the 4th annual international symposium in industrial engineering on the performance–based management. Kasetsart University, Bangkok, pp 1–12

    Google Scholar 

  • Huang SH, Uppal M (2002) A product driven approach to manufacturing supply chain selection. Supply Chain Mgmt: Int J 7(4):189–199

    Article  Google Scholar 

  • Jeng DJF, Tzeng GH (2012) Social influence on the use of clinical decision support systems: revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique. Comput Ind Eng 62(3):819–828

    Article  Google Scholar 

  • Kaplan RS, Norton D (1992) The balanced scorecard –measures that drive performance. Harvard Bus Rev 70:71–9

    Google Scholar 

  • Lambert DM, Pohlen TL (2001) Supply chain metrics. Int J Logist Mgmt 12(1):1–19

    Article  Google Scholar 

  • Li RJ (1999) Fuzzy method in group decision making. Comput Math Appl 38(1):91–101

    Article  MathSciNet  MATH  Google Scholar 

  • Lin CJ, Wu WW (2008) A causal analytical method for group decision–making under fuzzy environment. Expert Syst Appl 34(1):205–213

    Article  Google Scholar 

  • Liou JJH, Yen L, Tzeng GH (2008) Building an effective safety management system for airlines. J Air Transp Mgmt 14(1):20–26

    Article  Google Scholar 

  • Naylor JB, Naim MM, Berry D (1999) Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain. Int J Prod Eco 62(1–2):107–118

    Article  Google Scholar 

  • Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Euro J Oper Res 156(2):445–455

    Article  MATH  Google Scholar 

  • Otto A, Kotzab A (2003) Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. Euro J Oper Res 144(2):306–320

    Article  MATH  Google Scholar 

  • Parker C (2000) Performance measurement. Work Study 49:63–66

    Article  Google Scholar 

  • Ramaa A, Rangaswamy TM, Subramanya KN (2009) A review of literature on performance measurement of supply chain network. In Proceedings of the 2nd international conference on emerging trends in engineering and technology, ICETET–09

    Google Scholar 

  • Ross TJ (1995) Fuzzy logic with engineering applications. McGraw-Hill, New York

    Google Scholar 

  • Selldin E, Olhager J (2007) Linking products with supply chains testing Fischer’s model. Supply Chain Mgmt: Int J 12(1):42–51

    Article  Google Scholar 

  • Sink D, Tuttle T (1989) Planning and measurement in your organization of the future. IE Press, Norcross

    Google Scholar 

  • Slack N (1991) The manufacturing advantage. Mercury Books, London

    Google Scholar 

  • Stewart G (1995) Supply chain performance benchmarking study reveals keys to supply chain excellence. Logist Inf Mgmt 8(2):38–44

    Article  Google Scholar 

  • Talluri S, Narasimhan R, Nair A (2006) Vendor performance with supply risk: a chance–constrained DEA approach. Int J Prod Eco 100(2):212–222

    Article  Google Scholar 

  • Tseng ML (2009) A causal and effect decision making model of service quality expectation using grey–fuzzy DEMATEL approach. Expert Syst Appl 36(4):7738–7748

    Article  Google Scholar 

  • Turkett RL (2001) Lean manufacturing implementation–lean supply chain. Notes for the course of “IOE, Manufacturing Strategies”, www.engin.umich.edu/class

  • Wong WP, Wong KY (2007) Supply chain performance measurement system using DEA modeling. Ind Mgmt Data Syst 107(3):361–381

    Article  Google Scholar 

  • Wu WW, Lee YT (2007) Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Syst Appl 32(2):499–507

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy Sets Inf Con 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

  • Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning – I. Inf Sci 8:199–249

    Article  MathSciNet  MATH  Google Scholar 

  • Zhou Q, Huang W, Zhang Y (2011) Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Sci 49(2):243–252

    Article  Google Scholar 

  • Zimmermann HJ (1991) Fuzzy set theory and its application, 2nd edn. Kluwer, Dordrecht

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, 34367, İstanbul, Turkey

    I. U. Sari, S. Ugurlu & C. Kahraman

Authors
  1. I. U. Sari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Ugurlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Kahraman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Kahraman .

Editor information

Editors and Affiliations

  1. LSIS, Aix-Marseille University, Marseille, France

    Lyes Benyoucef

  2. LSIS, CNRS, Marseille, France

    Jean-Claude Hennet

  3. Department if Industrial Engineering and, Indian Institute of Technology, Kharagpur, West Bengal, India

    Manoj Kumar Tiwari

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag London

About this chapter

Cite this chapter

Sari, I.U., Ugurlu, S., Kahraman, C. (2014). Prioritization of Supply Chain Performance Measurement Factors by a Fuzzy Multi-criteria Approach. In: Benyoucef, L., Hennet, JC., Tiwari, M. (eds) Applications of Multi-Criteria and Game Theory Approaches. Springer Series in Advanced Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-5295-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5295-8_8

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5294-1

  • Online ISBN: 978-1-4471-5295-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation