OEE that actually moves
production forward
Most plants have an OEE number. Not all of them have one they would stake a production decision on. The problem is never the formula, it is everything that feeds the formula.
You have an OEE number. The question is whether anyone trusts it enough to act on it.
The problem is rarely the formula. It is everything the formula depends on.
The 45-minute morning ritual
Every shift starts with someone manually copying data between SCADA, MES and Excel to produce an OEE report that is already 8 hours out of date. The problem it describes has often already repeated itself.
Four lines. Four definitions of downtime.
On line A, a changeover is planned loss. On line B it is excluded. When the plant manager compares performance, he is not comparing results: he is comparing definitions.
Microstops that add up to a lost shift
A 12-second jolt. A 20-second sensor reset. None gets logged. Cumulatively, micro-interruptions consume 8–12% of available capacity per shift, every shift, without anyone being asked to act.
A six-hour blind spot at 2 AM
A deviation starts at 2 AM and stays invisible until the morning meeting. By then, 600 units have been produced outside specification. The defect rate surfaces three days later in QC review.
74% OEE. Zero actionable next steps.
The number lands in the report. Everyone nods. Nobody knows if it was Availability, Performance or Quality, or which line to prioritize. The meeting ends without a decision. Next week, same number.
Your CI lead building reports, not improvements
Process engineers spend 30–40% of their time collecting and reconciling OEE data. That is one to two days per week not spent on improvement work. The data pipeline is the bottleneck, not the process.
OEE is only useful if it tells you what to fix. This is how Capture closes that gap.
The step forward is not a better dashboard. It is a measurement layer where the number, the cause, and the right action are always the same conversation.
Measure
Machine status, cycle times, stops and rejects captured automatically, no manual entry for what the machine already knows.
Contextualise
Every data point linked to line, product, batch, shift and reason code. The same downtime on two products tells a different story.
Analyse loss
Structural patterns, microstops and Pareto views visible across time. The stop every Tuesday. The drop after every changeover.
Trigger action
Alerts and workflows send the right task to the right role, before the shift ends, not after the review.
Register feedback
The outcome is stored. The system builds operational memory. Next time the same pattern appears, the response is already there.
The number in three systems is a report. The number in one layer is a tool.
The OEE app that ships with Capture
Every status change captured automatically. Every stop linked to a cause. The production history you need to see what actually repeats, per machine, per shift, in one place.
What OEE looks like when the data is actually trustworthy
Used every shift to make a decision, not every month to fill a report.
Poppies Bakeries
Multi-line bakery: OEE & energy
Sadef
Steel processing: OEE & data infrastructure
| Before Capture | After Capture |
|---|---|
| OEE numbers not present or disputed in every review | One shared measurement baseline |
| Microstops invisible or underregistered | Event-driven detection, nothing missed |
| Definitions differ by line | Standardised across all lines |
| Action triggered by memory or habit | Workflows triggered by reliable signals |
| Pilot stays a one-line exception | Template-driven rollout across sites |
Does your OEE give you this level of visibility?
Tell us how you measure OEE today. We will show you what it costs you.
No commitment. One hour. No preparation needed.
Find out if your OEE numbers are reliable
enough to steer with.
An OEE scan is not a sales call. It is a structured review of your current measurement approach, run by engineers who have done this work at Poppies, Sadef, Stellantis and similar plants.
The scan covers
- Downtime definitions: are your categories consistent across lines?
- Reason code structure: do your operators have the right options, in the right workflow?
- State change detection: are you catching microstops, or only the stops that get manually entered?
- Product-linked target speeds: is your performance based on the product actually running?
- Quality logging: is reject data captured at the right moment, with the right context?
- Historical loss analysis: can you identify structural patterns across shifts and weeks?
- Scalability: is your approach repeatable across additional lines or sites?
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