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Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS

Umar Muhammad Modibbo (Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, India) (Department of Statistics and Operations Research, Modibbo Adama University, Yola, Nigeria)
Musa Hassan (Department of Statistics, College of Science and Technology, Adamawa State Polytechnic, Yola, Nigeria)
Aquil Ahmed (Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, India)
Irfan Ali (Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, India)

Management Decision

ISSN: 0025-1747

Article publication date: 13 January 2022

Issue publication date: 22 February 2022

805

Abstract

Purpose

Supplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.

Design/methodology/approach

The concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.

Findings

This study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.

Research limitations/implications

This research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.

Practical implications

This work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.

Originality/value

In this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

Keywords

Acknowledgements

The authors wish to acknowledge the editor-in-chief (Prof. Dr. Brandon Randolph-Seng), and the anonymous reviewers for their valuable suggestions, which substantially improved the quality and presentation of this work.

Funding: This research received no funding from any organization or agency.

Conflict of interest: The authors have no known conflict of interest regarding the authorship and publication of this paper.

Citation

Modibbo, U.M., Hassan, M., Ahmed, A. and Ali, I. (2022), "Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS", Management Decision, Vol. 60 No. 3, pp. 806-836. https://doi.org/10.1108/MD-10-2020-1335

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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