An integrated fuzzy AHP- fuzzy MULTIMOORA model for supply chain risk-benefit assessment and supplier selection

Abstract

Supplier selection is a strategic decision for reducing risk, maximising overall value, and establishing strong, mutually beneficial long-term relationships between the members in supply chain management. Identifying and prioritising the risks and benefits from each supplier enables managers to consider these factors and select the most suitable supplier. We propose an integrated approach for supply chain risk-benefit assessment and supplier selection by combining the fuzzy analytic hierarchy process (AHP) with the fuzzy multiplicative multi-objective optimisation based on ratio analysis (MULTIMOORA). The fuzzy AHP is used to measure the importance of the supply chain risks and benefits and the fuzzy MULTIMOORA is used to rank the suppliers. We present a case study to demonstrate the applicability and exhibit the efficacy of the proposed integrated framework. We also study the uncertainty in the output of our proposed framework using sensitivity analysis.

KEYWORDS:

• Supplier selection
• supply chain risk-benefit
• fuzzy analytic hierarchy process
• fuzzy MULTIMOORA
• sensitivity analysis

Acknowledgement

The authors would like to thank the anonymous reviewers and the editor for their insightful comments and suggestions. Dr Madjid Tavana is grateful for the partial support he received from the Czech Science Foundation (GAČR19-13946S) for this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Some of the names and data presented in this study are changed to protect the anonymity of the company.

Additional information

Notes on contributors

Madjid Tavana

Madjid Tavana is Professor and Distinguished Chair of Business Analytics at La Salle University, where he serves as Chairman of the Business Systems and Analytics Department. He also holds an Honorary Professorship in Business Information Systems at the University of Paderborn in Germany. Dr Tavana is Distinguished Research Fellow at the Kennedy Space Center, the Johnson Space Center, the Naval Research Laboratory at Stennis Space Center, and the Air Force Research Laboratory. He was recently honoured with the prestigious Space Act Award by NASA. He holds an MBA, PMIS and Ph.D. in Management Information Systems and received his Post-Doctoral Diploma in Strategic Information Systems from the Wharton School at the University of Pennsylvania. He has published 17 books and over 270 research papers in international scholarly academic journals. He is the Editor-in-Chief of the International Journal of Applied Decision Sciences, International Journal of Management and Decision Making, International Journal of Communication Networks and Distributed Systems, International Journal of Knowledge Engineering and Data Mining, International Journal of Strategic Decision Sciences and International Journal of Enterprise Information Systems.

Akram Shaabani

Akram Shaabani is a doctoral candidate in the Department of Industrial Management at the Allameh Tabataba'i University in Tehran, Iran. Her research interest is in multi-criteria decision making. She has published several papers in international journals and presented her research and several international and domestic conferences.

Soleyman Mansouri Mohammadabadi

Soleyman Mansouri Mohammadabadi is a doctoral candidate in the Department of Industrial Management at the Allameh Tabataba'i University. His research interest is in decision support systems. He has published several papers in international journals and presented his research in several international and domestic conferences.

Nilofar Varzgani

Nilofar Varzgani is an Assistant Professor in the Department of Business Systems & Analytics at La Salle University in Pennsylvania. She received her BBA from the Institute of Business Administration, Karachi, Pakistan and her Ph.D. from Rutgers University in New Jersey. Her research interest focuses on Supply Chain Management, Financial Analytics, Deterministic and Stochastic environments.