Generative AI-Enhanced Demand Forecasting and Capacity Sharing in Resilient Manufacturing Systems

Abstract

The increasing turbulence in global supply networks, driven by geopolitical tensions, climate-related disruptions, and volatile demand patterns, has elevated resilience to a strategic imperative in manufacturing systems. At the same time, recent advances in generative artificial intelligence have opened fundamentally new possibilities for demand forecasting by synthesizing complex, multimodal information and generating probabilistic scenarios rather than deterministic point estimates. When coupled with emerging architectures for capacity sharing among independent firms, these capabilities can transform how manufacturing networks absorb shocks, reallocate resources, and maintain continuity. This paper presents a system-level analysis of the integration of generative AI-enhanced demand forecasting with reciprocal capacity sharing mechanisms, emphasizing structural trade-offs, architectural choices, and governance frameworks. It examines how large-scale generative models can capture latent demand structure across heterogeneous data sources and produce forecast ensembles that support contingent capacity pooling contracts. The discussion addresses the interdependencies between forecasting accuracy, information asymmetry, trust formation, incentive alignment, and the overall resilience of manufacturing ecosystems. Further, the paper explores infrastructure requirements for data exchange, model deployment, and real-time orchestration of shared capacity, as well as the fairness, sustainability, and policy dimensions that arise when algorithmic coordination governs distributed production resources. By avoiding narrow technical formalization and instead focusing on the socio-technical fabric that binds intelligent forecasting with cooperative resource management, the analysis contributes a holistic perspective on designing resilient manufacturing systems for an era of deep uncertainty.