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Using Fuzzy Set Based Model for Pharmaceutical Supply Chain Risks Assessment

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Intelligent and Fuzzy Systems (INFUS 2022)

Abstract

Risk assessment in the pharmaceutical supply chain is very important as it directly affects patient health. The importance of access to medicine, especially in today’s pandemic conditions, has been demonstrated this once again. Manufacturer pharmaceutical companies that use outsourcing service providers for their logistics processes are faced with many risks. In this study, the risks of outsourcing in logistics were determined in the context of the pharmaceutical industry. Since the problem contains many criteria due to its structure, multi-criteria decision-making methods were used in the proposed model. In the study, Pythagorean fuzzy sets (PFS) were used to include expert opinions in the process since PFS perform well in dealing with uncertainty and they represent decision makers’ evaluations in a wider range of definitions in the evaluation process. In the study, the importance levels of the risk criteria were determined by the interval-valued Pythagorean fuzzy AHP method, while the risk performance of the logistics service provider 3PL companies was determined by the Pythagorean fuzzy WASPAS method. In this study, procurement services in the pharmaceutical sector were evaluated under three main criteria as delivery, quality and operational, and quality was determined as the most important criterion. Among the quality criteria, the quality management system and good manufacturing practices were obtained as the two most important criteria, respectively. This study contributes to the literature by showing the importance degree of the criteria in outsourcing service of pharmaceutical supply chain and how these risks can be assessed with a fuzzy-based model.

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Correspondence to Selcuk Cebi .

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Yalcinkaya, I., Cebi, S. (2022). Using Fuzzy Set Based Model for Pharmaceutical Supply Chain Risks Assessment. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-09173-5_32

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