Lead Time in Supply Chain Management of Additive Manufacturing: Issues Computation Manufacturing Systems

This book addresses the failure of conventional ERP and MRP systems to accurately compute lead times in additive manufacturing (AM) supply chains. Fixed deterministic lead time parameters - designed for stable traditional manufacturing - systematically fail in AM environments where build times vary with part geometry, post-processing is highly variable, machine availability is stochastic, and specialist material procurement carries long uncertain lead times. The research develops a five-phase AM lead time taxonomy covering pre-production, machine queue, build, in-process interruption, and post-processing, then constructs the AM-SCM Lead Time Computation Framework (AMLTCF) - a modular stochastic model producing full lead time probability distributions rather than single-point estimates. Validated across aerospace, medical device, and consumer goods case studies, the AMLTCF reduces lead time prediction error from 22-39% (ERP baseline) to 7-12%, enabling accurate customer commitments, optimised scheduling, and reduced safety stock. Machine learning models (XGBoost, LSTM) further improve prediction accuracy when sufficient historical data exists.

Pages: 64, Paperback, LAP Lambert Academic Publishing