AI-Driven Demand Forecasting and its Integration with Warehouse Management Systems (WMS)

Abstract

The growing complexities in supply chain management necessitate accurate demand forecasting and efficient warehouse operations to meet consumer expectations and minimize costs. Artificial Intelligence (AI) has revolutionized traditional demand forecasting by incorporating big data and machine learning techniques. Integrating AIdriven demand forecasting with Warehouse Management Systems (WMS) creates a unified approach that optimizes inventory, reduces waste, and enhances operational efficiency. This paper provides a comprehensive review of existing literature, proposes a methodology for integrating AI into WMS, and presents results from simulated case studies. The findings suggest significant operational advantages, though challenges like data quality and system interoperability remain. Future work should address

these limitations while exploring emerging technologies to further improve supply chain resilience.