Abstract
As the market becomes more global, logistics is now seen as an important area where industries can cut costs and improve their customer service quality. The latest trend is to outsource logistics activities to the outside company (known as third party logistics service providers or 3PL) and to allow the outsourcing company to concentrate on the core competence, improve the service and many more. It results in achieving high quality end results (products or services), at reasonable cost coupled with high customer satisfaction. This article discusses the use of Fuzzy Delphi method to shortlist the most important criteria and most probable service providers and fuzzy TOPSIS (Technique for Order Performance by Similarity to Idea Solution) to choose the best logistics service provider (LSP) by finding the closeness to the Positive Ideal Solution (PIS). The proposed methodology is applied to a case study for an automobile company in north India who wants to outsource the logistic activities to third party LSP in the Indian context. The proposed model can provide the guidelines and directions for the decision makers (DMs) of various companies to effectively select their service providers in the present day competitive logistics markets
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boran FE, Genç S, Kurt M, Akay D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36(8):11363–11368
Bottani E, Rizzi A (2006) A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management: An International Journal 11(4):294–308
Carbone V, Stone MA (2005) Growth and relational strategies used by the European logistics service providers: Rationale and outcomes. Transportation Research Part E: Logistics and Transportation Review 41(6):495–510
Carr AS, Pearson JN (2002) The impact of purchasing and supplier involvement on strategic purchasing and its impact on firm’s performance. Int J Oper Prod Manag 22(9):1032–1055
Chen, Tzeng GH (2004) Combing grey relation and TOPSIS concepts for selecting an expatriate host country. Mathematical and Computer Modeling 40(13):1473–1490
Chen SJ, Hwang CL (1992) Fuzzy multiple attribute decision making - methods and applications, Lecture Notes in Economics and Mathematical Systems. Springer, Berlin Heidelberg New York
Darbari M, Srivastava N, Lavania S, Bansal S (2010) Information modeling of urban traffic system using fuzzy stochastic approach. Proceedings of the World Congress on Engineering, 2010, vol 1, June 30–July 2, 2010, London, UK
kozkan GB, Feyzioglu O, Nebol E (2008) Selection of the strategic alliance partner in logistics value chain. Int J Prod Econ 113:148–158
Gupta R, Sachdeva A, Bhardwaj A (2010) Selection of 3pl service provider using integrated fuzzy delphi and fuzzy TOPSIS. Proceedings of the World Congress on Engineering and Computer Science, 2010, vol II, WCECS 2010, October 20–22, 2010, San Francisco, USA 1092–1097
Hertz S, Alfredsson M (2003) Strategic development of third party logistics providers. Ind Market Manag 32:139–149
Hwang CL, Yoon K (1981) “Multiple attributes decision making methods and applications, a state–of–the–art survey”. Springer–Verlag, New York
Isiklar G, Alptekin E, Büyüközkan G (2007) Application of a hybrid intelligent decision support model in logistics outsourcing. Computers and Operations Research 34(12):3701–3714
Jharkharia S, Shankar R (2007) Selection of logistics service provider: an analytic network process (ANP) approach. Omega 35(3):274–289
Sheu J-B (2004) A hybrid fuzzy-based approach for identifying global logistics strategies. Transportation Research Part E 40:39–61
Kablan A, Ng, WL (2010) High frequency trading using fuzzy momentum analysis. Proceedings of the World Congress on Engineering 2010, vol 1, June 30–July 2, 2010, London, UK
Kahraman C, Yasin AN, Cevik S, Gulbay M, Ayca ES (2007) Hierarchical fuzzy TOPSIS model for selection among logistics information technologies. J Enterprise Inform Manag 20(2): 143–168
Liu H-T, Wang W-K (2009) An integrated fuzzy approach for provider evaluation and selection in third-party logistic. Expert Syst Appl 36(3):4387–4398
Lynch CF (2002) 3PLs: the state of outsourcing. Logist Manag 41(6):T47–T50
Razzaque MA, Sheng CC (1998) Outsourcing of logistics functions: a literature survey. Int J Phys Distrib Logist Manag 26(2):89–107
Sachdeva A, Kumar D, Kumar P (2009) Multi-factor failure mode critically analysis using TOPSIS. J Ind Eng Int 5(8):1–9
Huan-Jyh Shyur, Hsu-Shih Shih (2006) A hybrid MCDM model for strategic vendor selection. Mathematical and Computer Modelling 44:749–761
Skjoett-Larsen T (2000) Third party logistics - from an interorganisational point of view. Int J Phys Distrib Logist Manag 30(2):112–127
Sohail MS, Bhatnagar R, Sohal AS (2006) A comparative study on the use of third party logistics services by Singaporean and Malaysian firms. Int J Phys Distrib Logist Manag 36(9):690–701
Tongzon J (2001) Efficiency measurement of selected Australian and other international ports using data envelopment analysis. Transportation Research A 35:113–128
Vaidyanathan G (2005) A framework for evaluating third-party logistics. Communications of the ACM 48(1):89–94
van Hoek RI (2001) The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain. Int J Oper Prod Manag 21(1/2):15–29
Van Laarhoven P, Berglund M, Peters M (2000) Third-party logistics in Europe - five years later. Int J Phys Distrib Logist Manag 30(5):425–442
Kreng VB, Chao-Yi Wu (2007) “Evaluation of knowledge portal development tools using a fuzzy AHP approach: The case of Taiwanese stone industry”. Eur J Oper Res 176:1795–1810
Wilding R, Juriado R (2004) Customer perceptions on logistics outsourcing in the European consumer goods industry. Int J Phys Distrib Logist Manag 34(8):628–644
Yan J, Chaudhry PE, Chaudhry SS (2003) A model of a decision support system based on case-based reasoning for third-party logistics evaluation. Expert Systems 20(4):196–207
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Gupta, R., Sachdeva, A., Bhardwaj, A. (2011). A Framework for the Selection of Logistic Service Provider Using Fuzzy Delphi and Fuzzy Topsis. In: Ao, SI., Amouzegar, M., Rieger, B. (eds) Intelligent Automation and Systems Engineering. Lecture Notes in Electrical Engineering, vol 103. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0373-9_15
Download citation
DOI: https://doi.org/10.1007/978-1-4614-0373-9_15
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-0372-2
Online ISBN: 978-1-4614-0373-9
eBook Packages: EngineeringEngineering (R0)