Manufacturing
From MPS through shop floor execution. Autonomous AI agents handle BOM explosion, capacity constraints, quality disposition, and maintenance scheduling.
The Manufacturing Challenge
Mid-market manufacturers face a planning paradox: too complex for spreadsheets, too small for the SAP IBP / Kinaxis implementations that Fortune 500 companies can afford. The result is a patchwork of ERP transactions, Excel workarounds, and tribal knowledge.
How Autonomy Helps
Master Production Scheduling
Strategic production planning with rough-cut capacity checks. The system balances demand requirements against available capacity, material constraints, and changeover costs — continuously, not just on Monday.
Multi-Level BOM Explosion
Recursive component requirements with scrap rates and yield adjustments. Support for phantom assemblies, optional components, and configurable lot sizing rules (LFL, FOQ, POQ, EOQ, MIN/MAX).
Manufacturing Execution
The MO Execution agent manages manufacturing order release, sequencing, expediting, and deferral — deciding in <10ms whether to release, split, or defer based on capacity, material availability, and downstream demand urgency.
Make-vs-Buy Decisions
The Subcontracting agent evaluates internal vs external manufacturing routing based on capacity utilization, cost differentials, lead time impact, and quality considerations.
Quality Management
The Quality Disposition agent evaluates holds and determines accept, reject, rework, scrap, or use-as-is — factoring in downstream demand urgency, rework cost, and quality history.
Continuous Learning from the Shop Floor
Every production decision generates outcome data that feeds back into agent training through reinforcement learning. Yield patterns, changeover durations, quality rejection rates — agents learn your specific manufacturing reality and improve their decisions continuously. And because agents never sleep, never take holidays, and don't break for lunch, your production planning operates around the clock. The night shift quality hold gets evaluated immediately, not when a planner arrives the next morning.
Typical Results
- 20-35% reduction in total supply chain cost
- Exception worklist reduced from 800+ to under 20 items daily
- Planning cycle compressed from weekly to continuous
- Institutional knowledge captured and self-reinforcing
- 24/7 autonomous operation — agents handle the repetitive so planners focus on what matters
See Autonomy for manufacturing
Walk through a production planning scenario with your data.