Suppliers Evaluation in Uncertain Conditions by Combining Dempster Shafer and Multi-Criteria Decision Making Techniques

Document Type : Production & Operations Management

Authors

1 Assistant Professor, Management Department, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran

2 MSc. Student, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran

Abstract

One of the key decisions in organizations is the selection of suppliers. Typically, a list of identified suppliers is evaluated based on a set of criteria, which indicates a multi-criteria decision-making problem. There are multiple methods and multiple criteria for evaluating suppliers. The nature of such decisions is complex in conditions of uncertainty. The purpose of this study is to provide a method for evaluating suppliers in conditions of uncertainty. In this research, evaluation criteria are extracted from the supplier potential matrix, and after determining the importance of criteria using the Best-Worst Method, the degree of uncertainty is calculated with Gray Relational analysis and finally, Dempster-Shafer method is used to rank suppliers. Research data has been gathered from hardware industry experts, with knowledge and experience in purchasing and procurement. A real case study is presented to illustrate the proposed hybrid approach to supplier selection. In the capability dimension, quality and capacity of the warehouse and in the willingness dimension, willingness to share information and mutual interaction are respectively the best and worst indicators. Also, the ranking of suppliers was conducted in conditions of uncertainty. Research results are important from a few perspectives. First, the criteria presented are public and can be used in different industries and conditions. Second, gray relational analysis is used to calculate uncertainty, which is due to the use of less data.

Keywords

References

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History

  • Receive Date: 10 November 2019
  • Revise Date: 24 December 2019
  • Accept Date: 02 February 2020

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