Intelligent Data Analytics Methods in Adaptive Inventory Planning

Abstract

This article examines intelligent data analytics methods applied in adaptive inventory planning under dynamic market conditions. The possibilities of integrating demand forecasting with replenishment optimization models aimed at improving the efficiency of supply chain management are analyzed. The role of machine learning methods – including stochastic modeling, evolutionary algorithms, and reinforcement learning – in shaping adaptive procurement and resource allocation strategies is examined. Particular attention is paid to minimizing total costs while maintaining the required service level and increasing the resilience of logistics systems under uncertainty.