Collective Optimization Strategies for Global Parts Management Using Multi-Agent Data Analytics

Abstract

Global production and service networks operate through complex interactions among suppliers, warehouses, plants, and distribution centers that jointly manage large portfolios of parts. The resulting coordination problem is shaped by dispersed inventories, heterogeneous lead times, and demand uncertainty that varies across regions and time. Traditional centralized planning architectures encode these interactions in large optimization models that are difficult to solve and maintain as networks grow in size and complexity. At the same time, increased data availability from transactional systems and tracking technologies has created opportunities for more responsive and distributed decision processes. This paper examines collective optimization strategies for global parts management based on a multi-agent data analytics view. The study considers a setting where decision responsibilities are distributed across agents associated with locations and transportation resources. Each agent observes local states and data streams, learns predictive models for demand and lead times, and solves local optimization problems subject to shared constraints. The global problem is modeled through linear structures that capture conservation of flow, capacity limits, and service-level requirements. Coordination emerges through iterative mechanisms that exchange dual variables, price-like signals, or low-dimensional summaries of forecasts and uncertainty sets. The analysis focuses on how decomposition structures, learning architectures, and communication patterns influence cost, service, and scalability properties. The discussion emphasizes trade-offs between centralization and decentralization and outlines conditions under which collective strategies approximate centrally computed policies while accommodating modularity, heterogeneous information, and evolving data-driven models in large global parts networks.