An Integrated Framework for AI and Predictive Analytics in Supply Chain Management

Abstract

Artificial intelligence (AI) and predictive analytics are reshaping supply chain management by enabling data-driven, proactive, and resilient operations across planning, sourcing, production, logistics, and fulfillment. This paper proposes an integrated framework that fuses descriptive, predictive, and prescriptive analytics with governance, people, and technology enablers to create end-to-end value. First, the framework standardizes and virtualizes heterogeneous data (ERP, WMS, TMS, IoT sensors, telematics, partner feeds) into a continuously updated knowledge graph, supporting feature stores and model registries. Second, it operationalizes forecasting and optimization through modular services: demand sensing using hybrid time-series and machine learning models; inventory optimization via stochastic policies; network, production, and transportation planning solved with mixed-integer programming enhanced by learning-augmented heuristics; and exception detection using anomaly identification on streaming signals. Third, a decision orchestration layer links predictions to business rules and rewards, enabling reinforcement learning for dynamic pricing, allocation, and capacity hedging, while digital twins support scenario planning under uncertainty. Robust MLOps underpins the lifecycle with data quality checks, bias and drift monitoring, continuous training, canary deployment, and rollback. Governance defines ownership, KPIs, explainability standards, and model risk controls, embedded within a change management program that upskills talent and aligns incentives. The framework is evaluated against resilience, cost-to-serve, service level, and sustainability objectives, emphasizing traceable emissions accounting and circularity signals from returns and refurbishment. Implementation follows a value-focused roadmap: identify high-impact use cases, codify reusable data products, industrialize models behind APIs, and scale via a cloud-based control tower that exposes alerts, recommendations, and what-if simulations to cross-functional squads. Case illustrations show reductions in forecast error, working capital, expedite spend, and carbon intensity, alongside improved on-time-in-full and supplier reliability. The contribution is a cohesive, modular architecture that reconciles methodological rigor with operational practicality, enabling organizations to convert predictive insight into prescriptive action at enterprise scale. Future research should benchmark design choices across industries, quantify governance trade-offs, and formalize carbon-aware objective functions that internalize risk, volatility, and societal costs fully.