Overview

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.

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