Optimization of Pharmacy Supply Chains Using AI and Machine Learning

Abstract

Pharmacy supply chains are under intensifying pressure to deliver the right medicine, in the right quantity, at the right time—while minimizing costs, preventing expiries, and complying with stringent regulatory and quality standards. Conventional forecasting and replenishment methods often struggle with volatile demand, multi-echelon dynamics (manufacturer–wholesaler–pharmacy), and constraints such as cold-chain integrity and controlled-substance regulations. This manuscript presents an integrated framework for optimizing pharmacy supply chains using artificial intelligence (AI) and machine learning (ML). We synthesize current methods in demand forecasting, inventory control, routing and scheduling, and risk management, and we propose a practical methodology that combines feature-rich forecasting (gradient-boosted trees and sequence models), stochastic inventory optimization, reinforcement learning for adaptive reorder policies, and constraint-aware vehicle routing. 

A detailed study protocol outlines quasi-experimental deployment across community and hospital pharmacies, including data governance, evaluation metrics, and change-management steps. Illustrative results (based on a realistic implementation scenario) indicate reductions in stockouts (30–50%), expiries (25–40%), and total holding costs (10–20%), with improvements in service level (3–10 percentage points) and forecast accuracy (MAPE reductions from ~22–28% to ~9–14%). We conclude with discussion on interpretability, workforce upskilling, and governance, emphasizing a socio-technical approach that blends human oversight with algorithmic optimization to achieve resilient, ethical, and efficient pharmacy supply chains.