Executive summary
- Overall Equipment Effectiveness (OEE) combines availability, performance and quality into a single measure of how well equipment is used.1
- ISO 22400-2 defines OEE and related key performance indicators for manufacturing operations, so they can be calculated the same way across lines and sites.2
- OEE is only as good as its data. Manual tally sheets hide short stops and slow cycles; automatic capture from machines reveals them.
- Linking machine data to work orders, products and quality results in an MES turns a number into actions.3
- These principles are built into Cardia MES.
1. Why OEE
Most factories can say how much they produced. Fewer can say how much they could have produced with the same equipment and time, and where the difference went. OEE answers that question in one figure, and — more importantly — breaks the difference into causes that can be acted on.
2. Define it once
The classic definition, popularised through Total Productive Maintenance, is:1
ISO 22400-2 provides standard definitions for these and related indicators, which helps avoid each line inventing its own variant.2 The most important decision is what counts as planned production time — for example whether planned maintenance and breaks are excluded — and to apply that decision everywhere.
Availability, performance and quality multiply, so modest losses in each compound into a much lower overall figure. Improving the weakest factor usually gives the largest gain.
3. From losses to actions
Total Productive Maintenance groups losses into categories such as breakdowns, set-up and adjustment, minor stops, reduced speed, process defects and start-up losses.1 Each maps to one OEE factor:
| OEE factor | Typical losses | Typical actions |
|---|---|---|
| Availability | Breakdowns, changeovers, waiting for material | Maintenance planning, faster changeovers, material staging |
| Performance | Minor stops, reduced speed | Root-cause fixes for jams, tuning, operator training |
| Quality | Scrap, rework, start-up rejects | Process control, first-article checks, inspection |
4. Capture data automatically
Hand-written records typically capture long stops and miss the rest. Automatic capture uses signals the machine already has:
- Run/stop state for availability, with every stop timestamped.
- Cycle or part counters for performance.
- Reject counters or inspection results for quality.
Operators then only add what the machine cannot know: the reason for a stop, chosen from a short, agreed list.
5. Add production context
A stop is more useful when it is known which order, product, operation and shift it affected. That context lets a plant compare OEE by product, find which changeovers take longest, or see whether quality losses follow a specific material lot.
6. Where MES fits
IEC 62264-3 describes the activities of manufacturing operations management — production, maintenance, quality and inventory — and how they exchange information.3 OEE draws on all four. An MES is the natural place to calculate it, because it already holds the work orders, routings, quality results and material lots that give machine data its meaning.
7. Common pitfalls
- Inconsistent definitions that make one line look better than another for no real reason.
- Too many stop reasons, so operators pick “other”. A short list, refined over time, works better.
- Wrong ideal cycle times, which distort the performance factor.
- Using OEE to blame people. It measures equipment and process losses; used as a stick, it stops being reported honestly.
8. How Cardia implements it
- Work orders, routings and bills of materials define what should be produced and how.
- Operator reporting and a downtime log capture counts, stops and reasons per work centre.
- OEE is calculated and rolled up into snapshots for dashboards by machine, line and period.
- Inspections and non-conformance reports feed the quality factor and traceability.
- Lot genealogy and ERP synchronisation connect production to materials and business systems.
9. Conclusion
OEE is simple to state and easy to get wrong. A standard definition, automatic data capture, a short list of stop reasons and the production context from an MES make it a reliable tool for improvement. That is the approach taken in Cardia MES.
References
- Nakajima, S. (1988). Introduction to TPM: Total Productive Maintenance. Productivity Press.
- ISO 22400-2:2014. Automation systems and integration — Key performance indicators (KPIs) for manufacturing operations management — Part 2: Definitions and descriptions. International Organization for Standardization.
- IEC 62264-3:2016. Enterprise-control system integration — Part 3: Activity models of manufacturing operations management. International Electrotechnical Commission.
This white paper is provided for information. Figures labelled “Established” or “Peer-reviewed” are taken from the cited sources; figures labelled “Industry claim” have not been independently verified by LIDAVI; anything labelled “Design target” or “Hypothesis” is LIDAVI’s own and must be confirmed in a site-specific feasibility study. Nothing here is a performance guarantee.