Supply chain network design based on Big Data Analytics: heuristic-simulation method in a pharmaceutical case study

Fariba Goodarzian, Peiman Ghasemi, Andrea Appolloni*, Irfan Ali, Leopoldo Eduardo Cárdenas-Barrón

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Digital technology is a pivotal aspect of the ongoing digital transformation upon which supply chain networks (SCNs) heavily rely. Through the utilization of Big Data Analytics (BDA), organizations can gain insights into sales, production and formulate decision support systems. The pharmaceutical supply chain emerges as a crucial player in producing and distributing medications vital for treating COVID-19 patients. In this study, we have innovatively designed a multi-echelon Pharmaceutical Supply Chain Network (PSCN) using BDA during the COVID-19 pandemic. We have developed a novel Mixed-Integer Non-Linear Programming model addressing allocation, production planning, and inventory control decisions, known as the LAPI problem. A transformation method is employed to convert the non-linear model into a linear one to enhance computational efficiency. The primary objective of our mathematical model is to minimize the total cost and CO2 emissions of the transportation system and established centres while maximizing the shelf life of medicines. Our experiments reveal that medium- and large-scale problems, characterized by the three main attributes of BDA–variety, velocity and volume (3 V‟s), pose challenges for optimal solutions. We present a case study of Iran to validate the mathematical model for large-scale problems. Employing a simulation approach, we estimate the demand for required pharmaceuticals, underscoring our proposed model’s real-world applicability and effectiveness. This research contributes to the growing necessity of eco-friendly and efficient supply chain strategies, especially in the critical domain of pharmaceuticals during pandemic situations.

Original languageEnglish
JournalProduction Planning and Control
Early online date21 May 2024
DOIs
Publication statusE-pub ahead of print - 21 May 2024

Keywords

  • COVID-19
  • Digital transformation
  • Heuristics Simulation
  • Pharmaceutical industry
  • Supply chain network

ASJC Scopus subject areas

  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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