Solution
OEE Optimization
Track Overall Equipment Effectiveness at the machine and line level in real time — with Availability, Performance, and Quality calculated directly from live machine data, production counts, and cycle time — giving operations teams a reliable, transparent metric they can act on rather than just report.
Business challenge
OEE is one of the most widely used metrics in manufacturing, but in most plants it is also one of the least trusted. Numbers are calculated from manual logs filled in after the shift, from spreadsheets that aggregate data inconsistently across lines, or from systems that are never updated when process changes occur. The result is an OEE figure that tells leadership what happened on paper rather than what actually happened on the machines. When availability, performance, and quality are not broken out with machine-level evidence, teams cannot identify which factor is driving the loss — or on which machine. Without that granularity, OEE becomes a reporting exercise rather than an improvement tool. Improvement targets are set without knowing whether the constraint is downtime frequency, cycle time drift, or quality rejections.
Factobrain solution pattern
Factobrain calculates OEE continuously using data captured directly from machine operation. Availability is derived from actual runtime against planned production time — using shift schedules and machine state logs rather than manual estimates. Performance is calculated from real production counts and ideal cycle time — comparing what the machine actually produced against what it should have produced at its rated speed. Quality uses structured rejection data logged by operators with reason codes, giving an accurate count of good parts versus total parts produced. Each of the three components is displayed separately with the machine-level data that produced it, so losses can be traced to their source. Trends over time show whether OEE is improving or degrading — and whether the driver is availability, performance, or quality losses — making improvement prioritization precise and evidence-based.
ROI signals to monitor
- Reliable OEE backed by actual machine runtime, production count, and rejection data — not manual estimates
- Immediate breakdown of losses by Availability, Performance, and Quality component at the machine level
- Clear identification of which machines and lines are driving OEE losses across the plant
- Shift-by-shift trend visibility showing whether improvement actions are having measurable impact
- Faster improvement prioritization because teams know whether the constraint is downtime, speed loss, or quality
- Consistent OEE definitions across shifts and lines — ending disagreements about how the metric is calculated
Move from use case to deployment plan
Book a demo to map this use case to line selection, KPI baselines, and rollout milestones.