OEE dashboards for Malaysian factories.

OEE — Overall Equipment Effectiveness — is one number that tells you how productive a machine actually is. It multiplies three things: availability (was it running when it was supposed to be?), performance (was it running at full speed?), and quality (was the output usable?). World-class OEE is around 85%. Most SMB factories that aren't measuring it are sitting somewhere between 30% and 60% — and don't know it.

Because the gap between what your machines could produce and what they actually produce is almost always bigger than people expect, and the reasons usually surprise the owner. OEE makes the gap visible and assignable: micro-stoppages, slow ramp-ups, quality scrap, unplanned downtime — each gets its own line in the dashboard, with timestamps.

Often no. We retrofit cycle counters, machine-state sensors, and key process variables onto whatever you already have — Advantech, Siemens, Mitsubishi PLCs, even older standalone equipment with a small sensor add-on. Industry4WRD-friendly approach.

First useful screen: usually 4–6 weeks for one line. Full OEE with downtime reasons, shift comparison, trend analytics: usually 6–10 weeks. We tune in production with real data after launch.

Most of our SMB OEE engagements start in the low- to mid-five-figure MYR range, scoped to one line. Adding more lines is typically cheaper per line — most of the cost is the first-line build. We scope a fixed price after a free discovery call.

Yes. OEE data is most useful when it flows into other places — production planning, maintenance scheduling, costing, monthly reports. We integrate with whatever you have or build a small layer in front of it.

Yes. Once OEE data is flowing, predictive maintenance models can spot failure signals before downtime hits. Anomaly detection flags shifts that produced unusual quality or performance numbers. ML forecasting can use OEE history to plan production capacity. The dashboard is the foundation; the AI compounds it.