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

Abstract

PurposeSupplier 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/approachThe 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.FindingsThis 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/implicationsThis 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 implicationsThis 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/valueIn 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.

Assessing the feasibility of hyperlocal delivery model as an effective distribution channel

“…, 2020), choosing possible destinations for medical treatments (Guru et al. , 2022) and supplier selection in the pharmaceutical context (Modibbo et al. , 2022), and identifying raw material suppliers in the steel industry (Azimifard et al.…”

“…This method is also used extensively in the operations research domain. Several researchers have employed MCDM techniques for analysing various strategic problems, including prioritising The feasibility of hyperlocal delivery model suppliers (Tirkolaee et al, 2020), choosing possible destinations for medical treatments (Guru et al, 2022) and supplier selection in the pharmaceutical context (Modibbo et al, 2022), and identifying raw material suppliers in the steel industry (Azimifard et al, 2018). There are several MCDM methods: analytical hierarchy process (AHP), ANP, DEA, TOPSIS, fuzzy set theory, VIKOR, PROMETHEE, DEMATEL and ELECTRE.…”

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“…Modibbo et al [37] by presenting a mixed-integer linear programming model using a multicriteria decision-making process, the best qualified suppliers in the pharmaceutical industry are examined and a model for supplier selection problems is proposed. ey reduce the size of the data from the concept of analysis.…”

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“…Am is the correlation between the variable matrix and the observed variable X, and An represents the correlation between the variable matrix and the observed variable Y. ε and δ are the errors existing in the observed variable matrices m and n. ζ represents an unobservable random item. As shown in Figure 1 (Ali et al, 2020;Sharma et al, 2020;Ahmadini et al, 2021;Modilbbo et al, 2022):…”