AI-enabled predictive forecasting for inventory optimization in pharmaceutical supply chains

Pharmaceutical supply chains require effective inventory management since drugs are subject to both spoilage and demand variability focused on time, and regulations. Standard forecasting cannot help to fully estimate demand - resulting in overstocks, stockouts and increased operating costs for the company. The present research explores the potential for Artificial Intelligence (AI) using predictive forecasting, in an optimal manner (over stock levels) in pharmaceutical supply chains. The findings demonstrate that predictive forecasting can provide better demand forecasting than traditionally utilized forecasting approaches, using machine learning techniques namely Long Short-Term Memory (LSTM) networks, Random Forest and hybrid ARIMA. The predictive demand is integrated within a method of inventory optimised to determine the best stock requirement to satisfy their stock level, defined by service-level constraints. Simulation results show a significant decrease in both stock outs and holding costs and further supports the potential of using AI based approaches to improve operational performance and value add within the pharmaceutical logistics domain. The findings provide actionable implications for health-sector deliverable providers, manufacturing companies, and distributors involving a compromise between the cost-effective and reliability of service.