Supply block chain network design in the Iranian pharmaceutical industry by using dynamic modeling and MCDM approach

Document Type : Original research

Authors

1 Industrial Engineering, Mazandaran University of Science and Technology,

2 Mazandaran University of Science and Technology

Abstract

Pharmaceutical counterfeiting is a global issue that poses significant risks to public health. Blockchain technology, as a distributed ledger system, provides an immutable chain of data that facilitates the tracking and verification of pharmaceutical products' authenticity, offering an effective solution to combat counterfeiting in the pharmaceutical industry. The aim of this research is to design a supply blockchain for the pharmaceutical industry to address issues related to counterfeit pharmaceutical products, such as mislabeling, expired products with updated labels, barcodes containing invalid or inconsistent information about the intended medication, and other similar problems within the industry's supply chain.

In this study, at first by using the agent based dynamic system, key factors, variables and vulnerable points in the drug supply chain that will lead to fraud were determined. Then, using open interviews with experts from medical community, insurance industry, pharmaceutical manufacturing and distribution, and academic researchers, key data at the level of actors to prevent drug fraud were identified and these data were divided into three categories in terms of importance using the Delphi method. Then, using the Analytic Hierarchy Process technique for policy-making in the design of the blockchain, the data were prioritized. Finally, the pharmaceutical industry block supply chain was designed as the main achievement of the research with the type and amount of connections between the blocks of this chain and the data distributed at the block level with their degree of importance. The results show the effective roles of the manufacturer, distributor, pharmacy, doctor, insurer, and patient in the blockchain, and drug-related data, including drug UID, drug GTIN, drug LOT, and drug name, are of the highest importance for distribution in the network.

Keywords

Main Subjects

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History

  • Receive Date: 30 November 2024
  • Revise Date: 12 July 2025
  • Accept Date: 19 July 2025

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