Global discrete manufacturer
Anonymized enterprise pattern: multi-plant demand planning and predictive maintenance brought onto one knowledge-graph fabric with live ERP and MES — so asset risk reshapes the schedule, not a side CMMS dashboard.
Context
A multi-plant discrete manufacturer ran demand and capacity in twin spreadsheets. Maintenance lived in CMMS. Expedites were meeting theater. No customer is named; outcomes are qualitative.
Challenge
- Forecasts ignored live downtime and routing constraints.
- Asset risk never reshaped the master production schedule.
- Planners and maintenance argued from different truths.
Approach
- Mapped BOM, routing, and work centers into the supply chain brain.
- First modules: master data health, demand planning, predictive maintenance linked to capacity.
- Planner-approved write-back so the next pass measured what changed.
Outcomes (qualitative)
One executable plan object. Fewer firefights between planning and maintenance. Clearer next-best actions for expedite vs wait.
Related reading
- Demand planning solution — the planning loop behind this case
- Demand planning white paper — the technical detail in full
- Case study: physical AI and PdM — asset health as capacity, not a health score
- Supply chain AI guide — definitions, use cases and evaluation criteria
Frequently asked questions
What kind of manufacturer does this case describe?
A multi-plant global discrete manufacturer running demand planning and predictive maintenance against live ERP and MES data. The customer identity and commercial figures are anonymized.
What changed for the planning team?
Demand, capacity and asset health stopped living in separate tools. Planners worked from one constrained plan with the exceptions ranked, instead of reconciling extracts across plants each week.
Are the outcomes guaranteed?
No. Outcomes are described qualitatively because every network has different data quality, product mix and constraint structure. The pattern is transferable; the numbers are not a promise.
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