AI-Driven Forecasting for Strategic Inventory Planning in Volatile Markets

Abstract

This article analyzes the transformation potential offered by Artificial Intelligence (AI) in strategic inventory planning in the event of turbulent markets. Traditional inventory systems are typically inept in counter-reacting towards rapidly evolving changes and lead towards inefficiency, increased cost, and operational disruption. Systematically reviewing the potential offered by AI-based forecasting methods—ranging from predictive analytics and cognitive supply chain platforms to resilience-based forecasting methods—over these weaknesses, this research depicts the immense potential offered by AI in the form of accuracy, responsiveness, cost saving, and resilience. Key challenges in the implementation of AI—ranging from data amalgamation, explanation-friendliness of the algorithm, data quality, and scalability—are critically analyzed and complemented by pragmatic recommendations on the mitigation of these barriers. Detailed tables and graphical representations further validate the potential offered by the use of AI-based forecasting methods in the form of increased forecast accuracy, cost saving, and better tolerance towards supply chain disruption. Strategic implications and research directions and the potential offered by the use of hybrid AI-human forecasting models and explainable AI models in the improvement of forecasting effectiveness and stakeholder acceptability are discussed in the subsequent portion. Practical researchers and researchers have been offered by this research study useful information about the effective management of turbulence in the marketplace using the potential offered by the use of AI.