Manufacturing
AI applied to real plant operations
Purchasing and production planned via an Excel mixing history, sales intuition, and "what we did last year." Result: excess stock or key stockouts.
Machines break, lines stop, technicians are called. Unplanned downtime costs remain the top agenda item in the monthly committee.
Defective batches caught at the end of the line or, worse, via client claim. Process data holds the root causes, but no one reads it in real-time.
Weekly/monthly close requiring manual consolidation across systems. By the time management sees it, it is no longer actionable.
01
Models blending your sales history with seasonality, calendar, and external signals (raw material prices, weather, holidays). Purchasing drops the guessing game.
What changes
Typical 20-35% reduction in excess stock. Key SKU stockouts dropping 40-60%. Purchasing decisions anticipated by weeks vs manual model.
02
Models learning normal line behavior — temps, pressures, cycle times, vibrations — and alerting in real-time when patterns break. Way before the operator notices or the batch ruins.
What changes
Defects caught during processes, not post-mortem. Measurable drop in scrap and claims. Root causes backed by data, not "shift manager hypotheses."
03
Based on breakdown history and critical equipment telemetry, models estimate the probable failure window. Maintenance is planned before the downtime — not after.
What changes
Unplanned downtime dropping 20-40% on applied equipment. Total maintenance costs drop by avoiding emergencies.
04
For multi-SKU plants with capacity constraints: weekly production mix proposals factoring margin, deadlines, raw materials, and load. Humans decide; the model gives a data-backed baseline.
What changes
Improved margins via better mix decisions. Fewer unnecessary tooling changes. Deadlines met without overproducing.
05
Single pane for OEE, quality, costs, and plan vs. actual, streamed from existing systems (MES, ERP, sensors). Management sees operations when it matters, not at month-end.
What changes
Decisions taking a week are taken same-day. Committee meetings run on current data — no more Monday decks with last Thursday's numbers.
3-4 weeks
Audit of operational systems, data availability, and critical processes. We identify 2-3 best 12-month ROI use cases for your specific plant.
8-14 weeks
End-to-end implementation of one use case (forecasting, anomalies, predictive maintenance) on a bounded perimeter — one line, key SKU, or equipment family.
6-9 months
For industrial groups wanting to consolidate several cases over a shared data/model layer, continuously updated.
No. You need some data. Our initial audit spots if your data supports a use case. Often, the first months are data plumbing — and we tell you that before starting, not after.
Depends. Annual seasonality forecasting needs 2-3 clean years. Anomaly detection starts with weeks of normal operations. Predictive maintenance needs breakdown history plus telemetry. We pin this down in the AI Readiness phase.
Most offer reporting labeled as AI. Ask yourself: are you making different purchasing, production, or maintenance decisions thanks to it? If not, their AI isn't applied — usuallyan adoption/config issue. We audit this before you buy anything new.
Readiness: €6k-€12k. Single process pilot: €30k-€70k. Multi-case platform: €90k-€180k+ over months. Rule: every invested euro needs an 18-month business case ROI.
Hard constraint. We deploy architectures respecting OT/IT segregation, on-prem if needed, touching operational networks only in controlled, signed read-only ways. Data leaves the plant only if strongly justified.