Large Language Models, Causal Decision Support, and Machine Learning for Pharmaceutical Supply Chain Transformation

Abstract

Pharmaceutical supply chains are undergoing increasing digital transformation as organizations seek better methods for forecasting demand, managing inventory, responding to disruption, and improving operational decision-making. Large Language Models (LLMs), machine learning, and causal analytics provide complementary capabilities for this transformation. This study examines an integrated framework in which LLMs support knowledge interpretation and decision interaction, machine-learning models provide predictive analysis, and causal methods evaluate the potential effects of supply-chain interventions. The framework considers pharmaceutical demand, inventory availability, supplier reliability, transportation conditions, production capacity, and disruption risks as interconnected factors. Machine-learning models can identify patterns and anticipate emerging supply pressures, while causal decision support can examine how actions such as inventory increases, supplier substitution, capacity reallocation, and logistics changes may affect supply-chain outcomes. LLMs can further support the interpretation of analytical outputs, scenario formulation, and communication of complex decisions to supply-chain managers. Counterfactual analysis allows alternative strategies to be examined before implementation. The proposed framework can help pharmaceutical organizations move from conventional predictive monitoring toward intelligent, intervention-oriented, and adaptive supply-chain management.