AI-Enhanced Demand Forecasting in Pharmaceutical Supply Chains: Building on Geospatial-BI Integration for Machine Learning Demand Models

Abstract

The pharmaceutical supply chain faces persistent challenges in demand forecasting accuracy, leading to stockouts, excess inventory, and suboptimal resource allocation. This paper explores the integration of artificial intelligence (AI) and machine learning (ML) techniques with geospatial-business intelligence (BI) systems to enhance demand forecasting in pharmaceutical supply chains. Building on Chiobi's (2016) foundational work on geospatial analytics and business intelligence integration, this research examines how modern AI techniques can leverage this baseline data architecture to develop sophisticated demand prediction models. Through comprehensive analysis of empirical studies and methodological advances, this paper demonstrates that AI-enhanced forecasting systems, particularly those employing Long Short-Term Memory (LSTM) networks, Random Forest, and XGBoost algorithms, significantly outperform traditional time-series methods. The integration of geospatial features with business intelligence data creates a robust foundation for machine learning models, enabling pharmaceutical supply chains to achieve forecast accuracy improvements of 10-41% while simultaneously reducing inventory costs and improving service levels. This paper presents a conceptual framework for implementing AI-enhanced demand forecasting systems and discusses practical implications, challenges, and future research directions for pharmaceutical supply chain optimization.