Impact of AI-Based Demand Forecasting on Pharmaceutical Logistics

DOI: https://doi.org/10.63345/ijrmp.v11.i5.1

Devraj Sahu

Independent Researcher

Chhattisgarh, India

Abstract

Pharmaceutical logistics is a critical domain where the accuracy of demand forecasting directly impacts operational efficiency, supply chain resilience, and patient outcomes. With the advent of artificial intelligence (AI), forecasting methods have experienced significant improvements in both accuracy and timeliness. This study investigates the role of AI-based demand forecasting within the pharmaceutical logistics sector, highlighting its transformative impact on inventory management, distribution efficiency, and cost reduction. By examining historical forecasting models and contrasting them with contemporary AI approaches, the paper elucidates how machine learning techniques, neural networks, and data analytics enable more precise prediction of drug demand, mitigate supply chain disruptions, and optimize inventory levels. Statistical analysis is provided to demonstrate performance improvements in key operational metrics. The findings indicate that AI-driven methods can substantially reduce forecast errors and operational costs while improving responsiveness to market fluctuations. This manuscript offers an integrated review of past literature up to 2021, outlines the methodology of the present study, and presents empirical results supporting the benefits of AI implementation in pharmaceutical logistics.

Keywords

AI-based demand forecasting; pharmaceutical logistics; supply chain optimization; machine learning; inventory management

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