The complete OEE resource for UK manufacturing. Calculate your score, understand what's driving your losses, and turn the number into an improvement plan.
⚙️ Open OEE CalculatorOEE is a single percentage that tells you how much of your planned production time is genuinely productive — making good product, at full speed, with no unplanned stops. It is calculated by multiplying three independent loss measures together.
OEE was developed in the context of Total Productive Maintenance (TPM) and is now the most widely used manufacturing KPI globally. Its value is not the overall score — it's the ability to identify which type of loss is dragging performance down, so improvement effort goes to the right place.
Each component is expressed as a decimal before multiplying. The result is expressed as a percentage. Because it is multiplicative, losses compound — a line at 90% Availability, 95% Performance and 98% Quality achieves only 83.8% OEE.
Each component captures a different category of production loss. Understanding which is your weakest is the most important output of any OEE calculation.
Target: ≥ 90%
Full Availability guide →Target: ≥ 95%
Full Performance guide →Target: ≥ 99%
Full Quality guide →| Planned run time | 8 hrs (480 min) |
| Actual run time after 90 min downtime | 6.5 hrs (390 min) |
| Availability (390 ÷ 480) | 81.3% |
| Ideal cycle time: 2 min/unit · Units produced: 162 | — |
| Performance (162 × 2 ÷ 390) | 83.1% |
| Good units: 150 of 162 produced | — |
| Quality (150 ÷ 162) | 92.6% |
| OEE (81.3% × 83.1% × 92.6%) | 62.6% |
Notice: three components that each look reasonable individually compound to produce a significantly lower OEE. This is the multiplicative effect — and why all three must be addressed to reach world-class performance.
85% is widely cited as world-class, but the right benchmark depends on your operating model. A flexible multi-SKU line will never match a single-product continuous line — and shouldn't be expected to.
| OEE Score | Classification | What it typically means |
|---|---|---|
| ≥ 85% | World-class | High-volume, stable running. Continuous improvement culture in place. Rare for multi-SKU lines. |
| 75–84% | Good | Strong performance. Minor losses in one or two components. Realistic target for most FMCG lines. |
| 60–74% | Typical | Industry average. Significant improvement potential. Top 3 stop reasons likely unresolved. |
| 45–59% | Below average | Reactive maintenance, uncontrolled changeovers, or significant quality issues. Immediate action needed. |
| < 45% | Poor | Systemic problems. Line may need full TPM review, standards rebuild, or capital assessment. |
OEE varies significantly by sector and operating model. These benchmarks reflect typical ranges for well-run sites — not theoretical maximums.
The most common mistake is trying to improve all three OEE components simultaneously. Fix your weakest component first, measure the result, then move on. Spreading effort across all three is less efficient than sequential focus.
Categorise every stop by reason and duration over 4 weeks. In most sites, the top 3 stop reasons account for 60–75% of all downtime. You cannot fix what you haven't measured. This single step usually reveals the highest-value intervention immediately.
SMED (Single Minute Exchange of Die) separates internal changeover tasks (can only be done with the line stopped) from external tasks (can be prepared while running). Moving tasks from internal to external typically cuts changeover time by 30–50% without capital investment.
Performance losses from micro-stops are the hardest to capture because operators restart in under 2 minutes without recording them. Run a dedicated minor-stop observation study for a full shift — count every restart. The accumulated loss is almost always significantly larger than anyone estimates.
Quality losses cluster at line start-up and changeover more than during steady-state running. Standardising start-up sequences, pre-heating sealing tools, and defining clear release criteria before steady-state production begins can reduce start-up scrap by 50–70%.
OEE data only drives improvement if it's reviewed daily and converted into actions with owners and deadlines. A 15-minute daily review using the previous shift's data — top loss, root cause, action — is more effective than weekly reports reviewed by management only.
Enter your shift data and get an instant OEE score with Availability, Performance and Quality breakdown — plus the financial cost of your losses vs the 85% benchmark.
⚙️ Open OEE Calculator →OEE (Overall Equipment Effectiveness) measures how effectively a manufacturing line uses its planned production time. It matters because it converts operational data into a single number that reveals the true cost of inefficiency — in lost units, lost revenue and wasted labour. A site running at 65% OEE is only getting 65p of productive output for every £1 of planned capacity. The gap between actual and world-class OEE represents the financial case for improvement.
For food and FMCG manufacturing, a good OEE score depends on the operating model. A high-speed single-SKU line should target 80–85%. A multi-SKU line with frequent allergen changeovers may be performing well at 60–68%. The most useful benchmark is your own trend over time — consistent improvement of 2–5% per year is more meaningful than hitting an industry number. Always measure OEE per line, not per site, to avoid averaging out your best and worst performers.
OEE measures effectiveness during planned production time only — it excludes time the line was not scheduled to run. TEEP (Total Effective Equipment Performance) measures effectiveness against all calendar time (24 hours, 365 days). A line running one 8-hour shift per day with 85% OEE has a TEEP of roughly 28%. OEE is the right metric for operational improvement. TEEP is useful for capital investment decisions — it shows how much capacity exists in unscheduled time before you need new equipment.
Always measure OEE per line first. Site-level OEE averages out your best and worst performers and hides where the real losses sit. A site average of 72% might conceal a flagship line at 88% and a problem line at 54%. The 54% line is where the intervention belongs. Once individual lines are understood, site-level OEE is useful for reporting trends to management, but it should never drive operational decisions.
The most common cause is Performance losses — specifically minor stops that operators restart quickly without recording. The line looks busy because it is running most of the time, but dozens of 1–2 minute stops per shift accumulate to significant lost output. A line can look 95% busy but only produce 80% of its potential output. Running a dedicated minor-stop observation study for one shift — counting every restart — usually reveals the true scale of the problem.
The ideal cycle time should be based on the theoretical maximum speed of the equipment at its design capacity — not average observed speed. Check the equipment manufacturer's specification for rated throughput, then convert to time per unit. If no spec is available, use the best sustained performance observed over a full shift (not a short burst) as a proxy. Using average speed instead of ideal speed inflates Performance and produces a misleadingly high OEE.
The six big losses are the standard framework for categorising OEE losses: (1) Equipment failure / breakdowns, (2) Setup and adjustments / changeovers — both are Availability losses. (3) Minor stops and idling, (4) Reduced speed — both are Performance losses. (5) Start-up and yield losses, (6) Defects and rework — both are Quality losses. Every production loss in a manufacturing environment falls into one of these six categories, which is why the framework is universal across industries.