Decision intelligence for manufacturing links MES, ERP, maintenance, quality and sensor data to the plant’s recurring decisions — when to maintain, whether to hold a lot, how to schedule, which supplier to use — recommends the best option with evidence, routes it to the accountable engineer or planner, writes the action back and measures the result on the P&L.
The decisions that run a plant
| Decision | Signal | Context it needs | Authority | Outcome measured |
|---|---|---|---|---|
| Maintenance timing | Vibration, temperature or ΔP drift | Production plan, slowdown calendar, spares, failure history | Augmented — reliability engineer | |
| Quality hold / release | SPC shift, scrap spike | Recipe, material lot, customer spec, operator notes | Augmented — quality lead | |
| Schedule change | Order change, machine down, material late | Capacity, changeovers, due dates, labor | On-the-loop — planner | |
| Supplier reallocation | Supplier delay or quality issue | Open orders, alternate capacity, contract terms | Augmented — procurement | |
| Energy optimization | Tariff peak, excess O₂, idle load | Process constraints, energy prices | Automated in bounds | |
| Inventory policy | Demand shift, lead-time change | Forecast, service targets, working capital | Augmented — supply chain |
Why a plant digital twin changes the conversation
A heatmap on the actual geometry of the plant makes condition data legible to everyone — operators, engineers, finance. The fast-forward control turns a maintenance debate into a visible trade-off: act this week for $180K and 36 hours, or wait and risk a $4.2M, six-day outage. That is scenario intelligence people can see. (See scenario intelligence.)
Discrete and process manufacturing
Discrete manufacturing
Automotive, electronics, machinery: scrap, changeovers, OEE, supplier delivery, robot cells.
Process manufacturing
Chemicals, refining, food, pharma, oil and gas processing: rotating equipment, fouling, relief-valve compliance, energy.
Common data
MES, ERP, CMMS/EAM, historians (PI), LIMS, quality systems, SCADA, energy meters.
Common authority
Engineers and planners approve; automation grows in low-risk, reversible decisions.
What to measure
| KPI | How decision intelligence moves it |
|---|---|
| OEE | Fewer unplanned stops; faster recovery decisions |
| Unplanned downtime | Maintenance pulled into planned windows |
| Scrap and rework | Earlier holds and parameter corrections |
| Schedule adherence | Faster, evidence-based re-planning |
| Energy intensity | Continuous, bounded optimization |
| Decision latency | Signal-to-action measured in hours, not days |
Example: compressor K-301
- SignalStage-2 vibration reaches 7.8 mm/s against a 7.1 alarm.
- ContextA planned slowdown is scheduled this week; the bearing kit is on site; two similar failures occurred in 2024.
- OptionsRun to the March turnaround, swap bearings in this week’s slowdown, or shut down for an overhaul now.
- DecideThe reliability engineer approves the slowdown swap: $180K and 36 hours instead of a $4.2M, six-day trip risk.
- Act & learnA work order posts to the CMMS; the teardown confirms wear and updates the model.
A 90-day start
- Choose maintenance timing or quality holdsHigh value, frequent, data already exists.
- Connect 3–8 systemsHistorian, CMMS, MES, ERP and the production plan.
- Model the decision and its ownerReliability engineer or quality lead owns it.
- Shadow, then executeRecommendations become draft work orders, then approved write-backs.
- Plant decisions are cross-system by nature — that is why they’re slow.
- A digital twin makes trade-offs visible to operations and finance.
- Start augmented; automate bounded, reversible decisions later.