Agility of the Drug Supply Chain Considering Economic and Social Aspects: Use of Internet of Things and Big Data Analysis

Amirhamzeh  Razavinejad; Seyed Esmaeil Najafi; Mehran Khalaj; Hamed Kazemi Pour

doi:10.31181/ijes1512026214

Authors

Amirhamzeh Razavinejad Department of Industrial Engineering, SR.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0000-1640-8519
Seyed Esmaeil Najafi Department of Industrial Engineering, SR.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0002-8734-5436
Mehran Khalaj Department of Industrial Engineering, Pa.C, Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0001-6510-2911
Hamed Kazemi Pour Department of Industrial Engineering, CT.C, Tehran, Iran Author https://orcid.org/0000-0002-1848-8927

DOI:

https://doi.org/10.31181/ijes1512026214

Keywords:

Drug supply chain management, Robust feasibility optimization, Uncertainty, Internet of Things, MOGWO algorithm, Economic analysis

Abstract

The importance of drug supply and the efficiency and effectiveness of the drug supply chain as a strategic commodity are not hidden from anyone. Supplying raw materials for the production, storage, and distribution of medicines within a supply chain network is crucial. Today, the development of the Internet of Things and the analysis of vast amounts of data have enabled the rapid preparation of large data production resources, allowing decisions in the drug supply chain network to be made quickly with the aid of appropriate tools. This article aims to provide a framework for agile drug supply chain management, focusing on the Internet of Things and big data analysis to manage the country's drug supply chain effectively. Rebif-22-44 drugs, which are used in the treatment of MS disease, are investigated in this paper, and an attempt is made to minimize the costs of the drug supply chain network design and minimize the maximum unmet demand of drug distribution based on big data sources by using a framework. MOGWO analysis tools and epsilon constraints are discussed. Considering the existence of uncertainty in drug demand and transportation costs, the robust possibilistic planning method has been used to control uncertain parameters. The analysis of big data shows that, in order to reduce the shortage of drugs in the country, it is necessary to use more supplies and increase the number of drug production centers and warehouses. This leads to an increase in the design costs of the supply chain network. In the analysis of the model, 11 efficient solutions were obtained by the MOGWO algorithm, and seven efficient solutions were obtained by the epsilon constraint method. It was also observed that, in Iran, with the increase in the uncertainty rate, the demand for Rebif-22-44 drugs has increased, and this has led to a rise in network design costs and drug shortages in the country. Concretely, lowering the maximum unmet demand from 78 to 47 units required raising the total network cost from 1,303,212.72 to 1,498,842.94 (USD), quantifying the cost–equity trade-off, and MOGWO produced a broader, faster Pareto set than the ε-constraint method (11 solutions in 68.49 s, MSI = 195,630 vs. seven solutions in 1,342.15 s, MSI = 184,4.

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