Combines Sales Orders, rolling Forecasts and prioritized demand with CRD, multi-level BOMs, production cycles and procurement lead times to calculate CTB readiness dates, while a substitute-material console controls shortages and substitution strategies.
A global high-tech electronics manufacturer must manage firm orders, rolling forecasts and rapidly changing customer demand. Products have deep BOMs, long-lead electronic components and extensive substitute relationships. A single-part-number shortage view can therefore misclassify readiness and customer-request-date feasibility.
Sales Orders, Forecasts and other demand sources have different priorities and change continuously
Deep BOM structures make readiness-date calculation complex
Procurement lead times, semi-finished production cycles and final CRD must be linked
Substitutes have different priorities, qualifications and usage constraints
Shortage analysis must quickly simulate different substitution strategies
The solution first establishes a common demand priority model, then explodes the BOM from finished-goods CRD and calculates earliest material-ready dates using inventory, inbound supply, procurement lead times, semi-finished cycles and WIP status. The substitute console incorporates approved substitutes, priorities and rules into CTB calculations and shows the readiness-date impact of each substitution scenario.
Management of Sales Order / Forecast / Min-Balance and other demand sources
Multi-level CTB calculation driven by CRD and BOM structure
Unified netting of inventory, inbound supply, purchasing and WIP
Substitute groups, priorities, qualification status and usage rules
Rapid recalculation of shortages, substitute scenarios and material-ready dates
Rolling forecast updates with frozen-window control
Moves shortage checking from individual parts to order-level CTB promise analysis
Makes substitute availability and decisions centralized and transparent
Sales, PMC and purchasing collaborate around the same material-ready date
Demand changes can quickly identify affected orders and materials
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AI for Manufacturing Transformation