Neural Computing and Applications

, Volume 29, Issue 7, pp 555–564 | Cite as

Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry

  • Vipul Jain
  • Arun Kumar Sangaiah
  • Sumit Sakhuja
  • Nittin Thoduka
  • Rahul Aggarwal
Original Article

Abstract

Supplier selection is one of the key activities of purchase management in supply chain. Supplier selection is a multifaceted problem relating qualitative and quantitative multi-criteria. This paper deals with a supplier selection problem in an Indian automobile company. The work presents selection of headlamp supplier using integrated fuzzy multi-criteria decision-making approaches: analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). The selection process starts with identifying the criteria based on literature review and interviewing industry experts. Weights to criteria are assigned using AHP, and suppliers are ranked using AHP and TOPSIS. Consistency tests are carried out to check the quality of expert’s inputs. Also, sensitivity analysis is done to check the robustness of the approach. The results address that fuzzy approaches could be effective and more accurate than the existing approaches for supplier selection problems.

Keywords

Supplier selection AHP TOPSIS Consistency test Sensitivity analysis 

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Vipul Jain
    • 1
  • Arun Kumar Sangaiah
    • 2
  • Sumit Sakhuja
    • 3
  • Nittin Thoduka
    • 3
  • Rahul Aggarwal
    • 3
  1. 1.Victoria Business SchoolVictoria University of WellingtonWellingtonNew Zealand
  2. 2.School of Computing Science and EngineeringVIT UniversityVelloreIndia
  3. 3.Department of Mechanical EngineeringG.D.Goennka World Institute & Lancaster UniversitySohnaIndia

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