Improving Parts Availability and Forecasting in Global Operations Through Advanced Data Analytics

Abstract

Global production and service networks depend on reliable availability of spare parts to maintain equipment uptime and customer service levels across distributed geographies. The complexity of these networks has increased with product differentiation, shorter life cycles, and heterogeneous usage profiles, while organizations continue to operate under cost and capital constraints. Planning processes must reconcile intermittent and highly skewed demand signals, stochastic transport and procurement lead times, and varying criticality of parts across applications. Conventional deterministic or aggregate forecasting approaches are often insufficient to capture these dynamics and to distinguish uncertainty that is structural from noise that arises from data quality issues or rare events. At the same time, transactional, sensor, and contextual data are increasingly available at scale, creating opportunities for more granular modeling of demand and supply processes. This paper develops an integrated framework for improving parts availability and forecasting in global operations using advanced data analytics. It combines a data architecture tailored to multi-echelon service networks with probabilistic models for demand and lead times and with optimization formulations for inventory and replenishment policies. The study emphasizes the representation of uncertainty, the propagation of predictive distributions into decision models, and the evaluation of policy robustness through simulation. The proposed concepts are discussed with respect to their algorithmic properties, information requirements, and practical implementation aspects within existing planning systems, with attention to both potential benefits and limitations.