Leveraging multivariate machine learning and large language models for multi-Billion Dollar inventory forecasting

Global supply chains face unprecedented levels of complexity, driven by cross-border e-commerce growth, demand volatility, and macroeconomic uncertainty. Accurate inventory forecasting has become a cornerstone of operational resilience and financial optimization for multinational organizations. This study explores the integration of multivariate machine learning (ML) models and large language models (LLMs) into enterprise-scale forecasting systems, with a particular focus on managing multi-billion-dollar weekly inventory portfolios across international regions. Using US based retail company’s supply chain operations as a case example, the paper examines how advanced ML models improve forecast accuracy by capturing non-linear interactions across demand signals, seasonality, pricing, and promotional data. Complementarily, LLMs are applied for anomaly detection, root-cause analysis, and automated communication of inventory risks to stakeholders. The research highlights how these AI-driven methods align financial reporting with operational execution, influence capacity planning decisions, and ultimately safeguard long-term free cash flow. By unifying predictive modeling with natural language intelligence, organizations achieve both quantitative precision and qualitative interpretability—two dimensions historically considered at odds in forecasting. Findings suggest that enterprises leveraging these methods can realize measurable improvements in forecast accuracy, reduction in manual defect handling, and enhanced scalability across regions. The paper concludes with implications for future supply chain resilience and recommendations for cross-functional adoption of AI-enabled forecasting systems.