Forecasting remains manual
Purchasing and production planned via an Excel mixing history, sales intuition, and "what we did last year." Result: excess stock or key stockouts.
We work with factories and production plants that already have operational data — and are wasting it. Forecasting that sharpens purchasing, anomalies detected before downtime, maintenance that stops being corrective. Business first, sensors second.
Industry has spent a decade investing in MES, ERP, and IoT. The data is there — but decisions are still made via intuition because no one is using it to predict, alert, or optimize. AI doesn't solve a lack of data. It solves the lack of exploitation of the data you already had.
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.
Five concrete applications delivering measurable ROI when clean operational data exists — often, part of our project is exactly that "cleaning."
Models blending your sales history with seasonality, calendar, and external signals (raw material prices, weather, holidays). Purchasing drops the guessing game.
Typical 20-35% reduction in excess stock. Key SKU stockouts dropping 40-60%. Purchasing decisions anticipated by weeks vs manual model.
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.
Defects caught during processes, not post-mortem. Measurable drop in scrap and claims. Root causes backed by data, not "shift manager hypotheses."
Based on breakdown history and critical equipment telemetry, models estimate the probable failure window. Maintenance is planned before the downtime — not after.
Unplanned downtime dropping 20-40% on applied equipment. Total maintenance costs drop by avoiding emergencies.
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.
Improved margins via better mix decisions. Fewer unnecessary tooling changes. Deadlines met without overproducing.
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.
Decisions taking a week are taken same-day. Committee meetings run on current data — no more Monday decks with last Thursday's numbers.
We don't sell "digital transformation." We audit your data — MES, ERP, SCADA, IoT — figuring out where a model delivers immediate value. If data quality is lacking, we say so, proposing plumbing fixes first. Models arrive after the plumbing.
Audit of operational systems, data availability, and critical processes. We identify 2-3 best 12-month ROI use cases for your specific plant.
End-to-end implementation of one use case (forecasting, anomalies, predictive maintenance) on a bounded perimeter — one line, key SKU, or equipment family.
For industrial groups wanting to consolidate several cases over a shared data/model layer, continuously updated.
in your plant? A 30-minute call covering your production mix, operational systems, and concrete pain points. We leave with 2-3 candidates and clear investment clarity.