OEE calculation explained step by step comes down to one line: OEE = Availability × Performance × Quality, where each factor is a decimal between 0 and 1. The shortcut form uses the same numbers: OEE = (Good Count × Ideal Cycle Time) ÷ Planned Production Time.
Those two formulas answer the question searchers actually ask. The rest of this guide works through a single shift on a single CNC cell and carries the same numbers from raw shift data to a final 71.8% OEE, so you can rebuild every figure by hand. Updated for 2026, it also covers the Excel layout and the mistakes that quietly inflate a score.
What follows is roughly a twenty-minute job once your downtime data is in decent shape. Most of the effort sits before the arithmetic, in agreeing definitions.
Table of Contents
What You Need to Calculate OEE
OEE (Overall Equipment Effectiveness) is a lean metric from Total Productive Maintenance that scores one asset against its own plan. Before you touch a formula, collect these inputs and write down who owns each definition.
- Calendar time for the window, in minutes. A single 8-hour shift is 480 minutes.
- Planned downtime: scheduled breaks, planned maintenance, planned changeovers. These are excluded, not penalised.
- A stop threshold in minutes. Stops shorter than the threshold count as Performance losses; longer stops count as Availability losses.
- Actual stop time, split into unplanned breakdowns, changeovers, minor stops and everything else, with a reason code for each.
- Ideal cycle time in seconds per good part: the fastest sustained rate with no defects, no minor stops and full speed.
- Total Count (all parts produced, good and bad) and Good Count (parts that passed on the first attempt).
- A data source: manual log, sensor, or MES. Pick one and note who records it.
Two of those inputs do most of the damage when they are wrong. Ideal cycle time and the stop threshold are both judgement calls, and both quietly move the final number without anything changing on the floor.
Practitioners posting in r/InjectionMolding split on exactly this: some find manual logging good enough, others point out that a lot of downtime never gets recorded at all, which flatters nothing but makes the record look better than reality. Budget time for data quality rather than assuming the sheet is right.
OEE Calculation Explained Step by Step
Every figure below comes from one shift on one CNC machining cell running a single part number. The inputs are the shift report.
| Input | Value |
|---|---|
| Shift length | 480 minutes |
| Scheduled breaks | 30 minutes |
| Planned maintenance | 45 minutes |
| Unplanned breakdown stops | 55 minutes |
| Changeover | 20 minutes |
| Other stops above the threshold | 12 minutes |
| Ideal cycle time | 10 seconds per part |
| Total Count | 1,850 parts |
| Reject Count | 105 parts |
| Stop threshold | 5 minutes |
1. Define the Measurement Period
Pick one asset, one product, one contiguous window. Measuring two products in one OEE number is meaningless, because each has its own ideal cycle time.
Do not average several shifts by taking the mean of their OEE percentages. OEE multiplies, so it is weighted by planned time. Total the minutes, total the counts, then calculate once. The running practitioner view is that most plants sit somewhere between 60% and 70%, so a first baseline is usually lower than anyone expected.
2. Calculate Planned Production Time
Planned Production Time is the time you genuinely intended the asset to produce. Calendar time minus breaks minus planned downtime.
480 − 30 breaks − 45 planned maintenance = 405 minutes of Planned Production Time.
That 405-minute denominator is the single most disputed line on the sheet. Exclude planned maintenance and your OEE rises. Count a scheduled break as downtime and it collapses. Put idle time caused by running out of material or missing a schedule into a separate schedule loss, which sits outside OEE entirely.
3. Calculate Availability
Availability asks one question: when the asset was supposed to run, how much of that time was it actually running?
First subtract all stop time from Planned Production Time to get Run Time. Unplanned breakdowns, changeovers, and stops longer than your 5-minute threshold all count here. Stops under 5 minutes are logged as Performance losses instead, so they stay in Run Time.
Stop time = 55 breakdown + 20 changeover + 12 other = 87 minutes. Run Time = 405 − 87 = 318 minutes.
Availability = Run Time ÷ Planned Production Time = 318 ÷ 405 = 0.785, or 78.5%.
At 78.5% you lost 87 minutes of planned time. A cell running under 80% availability usually has a changeover or a breakdown story, and changeover is the one most plants can attack with SMED and better setup preparation.
4. Calculate Performance
Performance compares what you made against what the machine could have made while it was running. First convert Ideal Cycle Time into an Ideal Run Rate. At 10 seconds per part, the ideal rate is 6 parts per minute, or 360 parts per hour.
Theoretical output over the 318 minutes of Run Time is 318 × 6 = 1,908 parts.
Performance = Total Count ÷ theoretical output = 1,850 ÷ 1,908 = 0.970, or 97.0%.
That 5.8% gap is the Performance loss. It comes from slow cycles, running below the ideal rate, and the small stops you deliberately kept inside Run Time. Performance above 100% is impossible if the ideal cycle time is honest, which is exactly why a padded ideal cycle time flatters this factor later.
5. Calculate Quality
Quality is the simplest line and the most commonly fudged. Good Count divided by Total Count, using first-pass output only.
Good Count = 1,850 total − 105 rejects = 1,745 good parts. Quality = 1,745 ÷ 1,850 = 0.943, or 94.3%.
Rework does not count as good. A part that needed a second pass through the machine was produced badly the first time, and counting it as good lets a scrap problem hide inside a quality score. Startup rejects, where the first parts after a changeover or a restart are out of spec, are real losses too.
6. Multiply the Three Factors
Convert each percentage to a decimal and multiply. OEE = 0.785 × 0.970 × 0.943 = 0.718, so 71.8% for the shift.
The shortcut formula should return the same number. Good Count × Ideal Cycle Time = 1,745 × 10 seconds = 17,450 seconds, which is 290.8 minutes. Divide by 405 minutes of Planned Production Time and you get 0.718 again. When the two disagree, an input is being counted twice or a count is wrong.
Here is the spreadsheet layout, cell by cell, for Excel or Google Sheets.
| Cell | Label | Formula |
|---|---|---|
| B2 | Shift length (min) | 480 |
| B3 | Breaks (min) | 30 |
| B4 | Planned downtime (min) | 45 |
| B5 | Planned Production Time | =B2-B3-B4 |
| B6 | Stop time above threshold (min) | 87 |
| B7 | Run Time | =B5-B6 |
| B8 | Availability | =B7/B5 |
| B9 | Ideal cycle time (sec) | 10 |
| B10 | Ideal run rate (parts/min) | =60/B9 |
| B11 | Theoretical output | =B7*B10 |
| B12 | Total Count | 1850 |
| B13 | Good Count | =B12-105 |
| B14 | Performance | =B12/B11 |
| B15 | Quality | =B13/B12 |
| B16 | OEE | =B8*B14*B15 |
Format B8, B14, B15 and B16 as percentages. Multiply the decimals, not the percentages: 78.5 × 97.0 × 94.3 gives 718, not 0.718.
7. Interpret the OEE Result
71.8% is a solid, believable shift result for discrete manufacturing. Here is how the usual ladder reads.
| OEE score | What it means | What to do |
|---|---|---|
| 100% | Perfect production: no stops, ideal speed, zero defects | Nothing, it will not last |
| 85% | World-class benchmark for discrete manufacturing | Treat as a ceiling, not a target |
| 60% | Typical for a discrete plant with real room to improve | Set the first improvement goal here |
| 40% | A plant that has only just started measuring | Fix data collection before chasing losses |
85% is hard arithmetic, not marketing. Three merely decent scores of 90% each multiply to 0.9 × 0.9 × 0.9 = 72.9%, which surprises most managers the first time they see it. That is why a plant can improve every factor and still sit below the benchmark.
So do not read a single number as a verdict. Our shift lost 87 minutes to stops, which is 21.5 points of Availability and the largest single block of loss here. Attacks on that have more headroom than a quality rate already above 94%.
In Six Sigma, OEE usually shows up as the response variable in a DMAIC project: it sets the baseline, quantifies variation, and confirms whether an improvement held. Benchmarks differ by process type, so a flow or batch plant should not be judged against a discrete ladder.
8. Improve OEE After the Calculation
Rank your losses, take the biggest, and give it an owner and a date. The Six Big Losses are the standard map.
| Loss | Factor | Typical cause |
|---|---|---|
| Unplanned stops | Availability | Breakdowns, material shortages, faults |
| Planned stops | Availability | Changeovers, scheduled maintenance, setups |
| Slow cycles | Performance | Reduced speed, warm-up, machine ageing |
| Small stops | Performance | Stops under your threshold: jam, misfeed, refills |
| Startup rejects | Quality | First parts after a changeover or restart |
| Production rejects | Quality | Defects, wear, process drift, scrap |
Run a Pareto of stop reason codes for a few weeks and the pattern usually names itself. Reason codes only help if the list is short and the operators actually pick one, so agree a small fixed list rather than a free-text box.
Changeover is the loss most often described as the biggest recoverable one, which makes SMED the natural first project. Then re-measure the same period with the same definitions and confirm the gain held without quality slipping.
Finally, roll the level up: shift to day, day to week, week to line. Weighted roll-ups use total planned time and total counts, never an average of percentages.
Common OEE Calculation Mistakes
Almost every disputed OEE number I have seen traces back to one of these.
- Counting breaks as downtime. Breaks belong outside Planned Production Time. Fix: subtract them in step 2, before anything else.
- Using a padded ideal cycle time. Set the cycle time at the theoretical maximum the machine could ever hit and Performance flatters you forever. Fix: use the fastest rate sustained for a full run at spec.
- Counting rework as good output. Fix: Good Count is first-pass output only.
- Leaving percentages unmultiplied. Fix: convert to decimals, multiply, then format as a percentage.
- Hiding changeover in planned downtime. Planned changeovers are ordinary production, not calendar time. Fix: keep them as stop time inside Planned Production Time so they show up in Availability.
- Moving the stop threshold to hit a target. Raising the threshold from 5 minutes to 10 reclassifies stops and shifts the score with no change on the floor. Fix: set the threshold once, write it down, and leave it.
- Averaging OEE percentages across products or shifts. Fix: total the time and counts first, then calculate once.
- Comparing a plant to a benchmark with a different definition. Two plants at 70% may be using different ideal cycle times and different stop rules. Fix: document the definitions alongside the number.
One last warning. Publishing a single OEE number on the shop floor tends to read as surveillance, and operators respond by protecting the score rather than the problem. Show the three factors and the loss list, and let the conversation stay on losses.
Frequently Asked Questions
What does 85% OEE mean?
85% is the widely cited world-class benchmark for discrete manufacturing. It means the asset is running 85% of its planned production time at ideal speed while producing almost no defects, for example roughly 90% Availability multiplied by 95% Performance and 99% Quality. Three average 90% scores only multiply to 72.9%, so treat 85% as a ceiling rather than a realistic target.
What is a good OEE rate?
For discrete manufacturing, 40% is typical of a plant that has only just started measuring, 60% is fairly typical with substantial room to improve, 85% is considered world class, and 100% represents perfect production with no stops, ideal speed and no defects. Benchmark your own baseline first, then set a target a few points above it.
What are the six big losses in OEE?
The six big losses split across the three factors. Availability loses time to unplanned stops such as breakdowns and to planned stops such as changeovers and scheduled maintenance. Performance loses time to slow cycles and to small stops under your stop threshold. Quality loses output to startup rejects after a changeover and to production rejects during the run.
How do I calculate the ideal cycle time for OEE?
Take the fastest sustained cycle observed over a full production run at specification, with no minor stops and no quality loss. Cross-check it against the design or engineering theoretical rate, then agree the number between engineering and production and document it. Changing it later without recording why makes scores from different periods impossible to compare.
How often should OEE be measured?
Measure at the shortest cycle that produces stable data, which is usually one shift per asset. Review it daily for loss tracking and roll it up weekly to find patterns. Measuring more often than that adds data entry without adding insight, and a plant that logs nothing on the floor gets numbers no amount of analysis can rescue.
Why is my OEE lower than the benchmark?
Lower scores usually come from one of three places: downtime that was never recorded, an ideal cycle time set too fast, or a stop threshold that puts small stops into Availability instead of Performance. Check those three first before assuming the plant is underperforming. Benchmark numbers also differ between discrete and process manufacturing, so compare like with like.
Conclusion
Start with one representative shift on one asset. Agree the ideal cycle time and the stop threshold, subtract breaks and planned downtime, total the stop time, and run the three factors through the spreadsheet above. That gives you a baseline you can defend.
Then find the largest single loss and fix that one before touching anything else. The OEE calculation explained step by step is only worth the hour it takes once it points at the next problem on the floor.