Executive summary
85%
world-class OEE per Nakajima's TPM benchmark, reached by roughly 6% of manufacturers globally
42%→75%
Hutchinson's average OEE gain across 40 sites in 12 countries
Key conclusions
- Measure OEE before you scope the automation spend, and split it into its components. The composite figure alone cannot show which component is failing.
- Each component needs a different fix. Low Availability calls for condition monitoring, low Performance for cycle-time instrumentation and low Quality for in-line inspection.
- Most of the gap between the 85% benchmark and the broad average of about 55–60% comes from a measurement failure. Nobody split the number into its components before buying.
Most automation proposals are approved before anyone has measured the line they are meant to fix. Eighteen months later, nobody can say whether the investment fixed a machine that kept stopping, one that ran slowly or one that produced bad parts. Nobody recorded which of the three problems existed before the money was spent.
Overall Equipment Effectiveness is the diagnostic to run before scoping. OEE multiplies three independent scores: Availability, Performance and Quality. A plant with a serious problem in one score can look identical to a plant with a problem in another, until someone splits the number into its parts.
Each of the three components measures a different kind of failure
Seiichi Nakajima's original Total Productive Maintenance (TPM) framework sets 85% OEE as world class. That figure is built from roughly 90% Availability, 95% Performance and 99.9% Quality. A low score on any one of the three calls for a different kind of automation spend:
| Component | What a low score means | What tends to fix it |
|---|---|---|
| Availability | Unplanned stops and changeovers: the line is down when it should be running. | Condition monitoring, quick-changeover tooling, spares management |
| Performance | The line runs slower than its rated speed, with no stoppage recorded anywhere. | Cycle-time instrumentation, bottleneck rebalancing, operator scheduling |
| Quality | Output that has to be reworked or scrapped after a full production cycle. | In-line inspection, process control, incoming-material checks |
Calculate OEE for your own line
The calculator starts with Nakajima's world-class figures of 90% Availability, 95% Performance and 99.9% Quality. Change each factor to your own line's figures to see which one the composite score hides.
OEE (overall equipment effectiveness) and its three factors
OEE = A × P × Q
Four measurements to take before you scope the spend
- Run OEE for at least two full production cycles before writing the automation brief.
- Split the number into its three components. The composite figure alone cannot tell you what to buy.
- Check how the current figure was calculated. When a plant reports 85%+ and the figure does not survive a second audit, the most common reasons are excluded downtime categories and inflated cycle times.
- Before approval, name the component the proposed automation is meant to improve, and by how much. After installation, the dashboard is the only evidence anyone has.
Few plants reach the 85% benchmark, mainly because they never measured the components
The 85% figure is cited often but reached rarely. Evocon's benchmark analysis draws on tracked production data from more than 50 countries. It finds that around 6% of manufacturers globally report 85% or above, against a broad average closer to 55–60%. Most of the gap exists because nobody measured which component was failing before deciding what to automate.
Where plants take that measurement first, the improvement is real and documented. Hutchinson, a Tier-1 automotive supplier, raised average OEE from 42% to 75% across 40 manufacturing sites in 12 countries, according to TeepTrak's published case study of the programme. That 33-point gain can be traced to specific sites and a specific baseline.
The sequence matters more than the choice of technology. Measure the three components, identify the one that is failing, then scope the automation that fixes that component.
