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A hybrid approach using data envelopment analysis and artificial neural network for optimising 3PL supplier selection

Author

Listed:
  • Rakesh D. Raut
  • Sachin S. Kamble
  • Manoj G. Kharat
  • Hemendu Joshi
  • Chirag Singhal
  • Sheetal J. Kamble

Abstract

Today third party logistics (3PL) service providers are into almost all the businesses right from providing raw material to packaging. 3PLs are also getting involved in customers concerned with operations. At a suitable price and consideration of all affecting criteria, anything can be outsourced. Any exporter needs to find a smart balance of what to perform in-house and what to hire outside providers to do (i.e., increase verticals and reduce horizontal supply chain operations). Thus, the demand of 3PL provider has become an important issue for organisations seeking quality customer service and cost reduction. The current research presents an integrated data envelopment analysis (DEA) and artificial neural networks (ANN) methodology for the evaluation and selection of 3PL providers. DEA is employed to identify the maximally efficient 3PL and to eliminate the unsuitable ones; and, ANN to rank and make the final selection. The proposed method enables decision makers to better understand the complete evaluation and selection process of 3PL selection. Furthermore, this approach provides a more accurate, effective, and systematic decision support tool for 3PL selection. Finally, an actual industrial application is presented to demonstrate the proposed method.

Suggested Citation

  • Rakesh D. Raut & Sachin S. Kamble & Manoj G. Kharat & Hemendu Joshi & Chirag Singhal & Sheetal J. Kamble, 2017. "A hybrid approach using data envelopment analysis and artificial neural network for optimising 3PL supplier selection," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 26(2), pages 203-223.
  • Handle: RePEc:ids:ijlsma:v:26:y:2017:i:2:p:203-223
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    Citations

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    Cited by:

    1. Lechtenberg, Sandra & Hellingrath, Bernd, 2021. "Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests," ERCIS Working Papers 37, University of Münster, European Research Center for Information Systems (ERCIS).
    2. Mehdi Soltanifar & Hamid Sharafi, 2022. "A modified DEA cross efficiency method with negative data and its application in supplier selection," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 265-296, January.
    3. Pankaj Dutta & Bharath Jaikumar & Manpreet Singh Arora, 2022. "Applications of data envelopment analysis in supplier selection between 2000 and 2020: a literature review," Annals of Operations Research, Springer, vol. 315(2), pages 1399-1454, August.
    4. Shiva Moslemi & Abolfazl Mirzazadeh & Gerhard-Wilhelm Weber & Mohammad Ali Sobhanallahi, 2022. "Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1116-1157, September.

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