AI-POWERED FORECASTING IN SUPPLY CHAIN: ACCURACY, SPEED, AND SCALABILITY

Abstract

The growing complexity and dynamism of global supply chains demand advanced forecasting tools that go beyond traditional statistical and heuristic approaches. Artificial Intelligence (AI), with its capabilities in data-driven learning and pattern recognition, has emerged as a transformative force in supply chain forecasting. This paper explores how AI-powered forecasting models enhance three critical dimensions: accuracy, speed, and scalability. Drawing upon recent advancements in machine learning (ML) and deep learning (DL), the study contrasts AI-based methods with classical forecasting techniques and presents empirical evidence from diverse industry case studies. We investigate the performance of key AI models—such as Long Short-Term Memory (LSTM) networks, Transformer architectures, and ensemble learners—across a variety of supply chain contexts including retail, logistics, and manufacturing. In doing so, we uncover the advantages, limitations, and trade-offs of deploying AI in real-world forecasting systems. The study also identifies the infrastructural and organizational prerequisites for scaling AI solutions across multi-tier global supply networks. Our findings highlight AI's potential to deliver highly adaptive, real-time, and granular forecasts, while also outlining challenges related to model interpretability, data readiness, and deployment complexity. This research contributes a critical evaluation of the current landscape and provides a roadmap for future implementation and innovation in AI-driven supply chain forecasting.