Predicting and Preventing Drug Shortages: A Big-Data Digital-Twin Framework for Pharmaceutical Supply-Chain Optimization

Abstract

Drug shortages put patients at risk and make the health system less efficient.  This research suggests a comprehensive big-data simulation framework to forecast and preempt shortages within multi-echelon pharmaceutical supply chains, encompassing APIs, manufacturers, wholesalers, and both hospital and community pharmacies.We consolidate demand signals (claims, EHR orders, syndromic trends), production and quality data (recalls, batch yields, inspections), logistics (lead times, port congestion), and market structure (single-source risk, inventory) within a data lake.  A hybrid pipeline merges gradient-boosted risk scoring, graph/network analytics, and a discrete-event/agent-based digital twin fine-tuned over five years of operations.  Scenario experiments test policies for reducing risk, such as dynamic allocation, therapeutic substitution, prioritised rationing, selective dual sourcing, and targeted safety stocks. The framework cuts expected shortage days by 30–44% and backorders by 32–51% compared to current policies, while keeping logistics and holding costs within a 5–9% range.  The early-warning risk scores (AUC≈0.85) give you 2–6 weeks to start taking steps to reduce the risk.  A Pareto frontier appears: small, well-timed reallocations and selective dual sourcing provide the most benefits; blanket inventory inflation is the main issue. Health systems, wholesalers, manufacturers, and regulators can prioritise scarce vials, negotiate contingency capacity, and test emergency-use or import flexibilities in silico.  Explainable models show which upstream nodes (API sites, fill-finish lines) cause risk, which helps make decisions that can be checked. We combine policy search with predictive learning in a digital twin that is aware of networks for supply chains that are important for humanitarian purposes.Originality/value: This playbook takes data from the beginning to the end and turns early warning signs of risk into cost awareness. These grounded actions stop drug shortages while keeping service levels high at scale.