Predictive Analytics for Global Supply Chain Resilience: A Comprehensive Survey of Data-Driven Stress Test Frameworks for Pharmaceutical Networks

Authors: Pinaki Bose

DOI: https://doi.org/10.37082/IJIRMPS.v10.i3.233073

Short DOI: https://doi.org/hb27sq

Country: United States

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Abstract: This report analyzes the existential crisis in the global pharmaceutical supply chain, exacerbated by the COVID-19 pandemic's exposure of fragile, just-in-time logistics and deep-tier dependency invisibility. It surveys emerging "Stress Test" frameworks and the operational necessity of Big Data analytics and "Digital Twins," synthesizing developments through 2021. The analysis examines metrics like the Risk Exposure Index (REI), Time-to-Recovery (TTR), and Time-to-Survive (TTS), and explores the "Ripple Effect" using Digital Supply Chain Twins (DSCT) to simulate disruptions. Integrating operations research and public health, the report argues that probabilistic predictive analytics is a fundamental requirement for global health security, concluding with a framework for implementing "Supply Chain Nerve Centers" to operationalize these insights.

Keywords: Supply Chain Resilience, Predictive Analytics, Stress Testing, Pharmaceutical Industry, Digital Twin, Risk Exposure Index (REI), Ripple Effect, COVID-19, Time-to-Recovery (TTR), Time-to-Survive (TTS), Operations Research, Conic Programming, Epidemic Modeling.

Paper Id: 233073

Published On: 2022-05-06

Published In: Volume 10, Issue 3, May-June 2022

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