Procurement and production planned and scheduled (“locked and loaded”) of finished goods based on low-quality demand forecasts with limited or costly flexibility without anticipating material and capacity needs leading to excessive lead times or missed ATP commitments.
Goals and Objectives
AI-enabled anticipatory order-, make-, ship-to-order fulfillment process that optimizes inventory commitment and obsolescence risk, shipping cost and capacity risks, and manufacturing cost risk to meet target on-time order fill rates utilizing base-load, postponed and just-in-time configuration, production, and additive manufacturing (network of 3D printing and advanced materials) that adapts to seasonal or product lifecycle swings in aggregate demand.
Cloud, industry cloud, IoT, prescriptive trade-off analytics, artificial intelligence, machine learning, block chain, 3D Printing, 3D Product Virtualization & Customization
Use Case Summary
Address the growing challenge of fulfilling demand for “long-tail” low-volume, high-volatility, often configurable products at lower cost to serve optimized customer service level targets. Also, enable customer driven customization of products in digital and physical properties.