Measure how well a line runs, set its pace from demand, buffer inventory against uncertainty and size a queue from two numbers you can observe. Flags the 4 mistakes that most often produce a plausible-but-wrong answer.
Key takeaways
- OEE multiplies availability, performance and quality, and the smallest factor is where to look first.
- Takt time is available time divided by demand, and it sets the pace a line must hold.
- Safety stock grows with demand variation, the square root of lead time and the service level.
- The reorder point is average lead-time demand plus safety stock.
- Little's law links the number in a system to the arrival rate and the time spent there.
Why operations metrics matter
A business that makes or moves things wins or loses on flow: how much gets through, how fast and with how little waste. A handful of simple metrics describe that flow. Each one is a ratio of quantities you can measure, and each points at a specific lever, such as downtime, pace, quality or inventory. Measure them before you decide what to fix.
Overall equipment effectiveness
OEE multiplies three shares: availability, the part of planned time the machine ran; performance, its speed against the ideal; and quality, the share of units that were good. At 90, 95 and 99 percent, OEE is about 84.6 percent. Multiplying, not averaging, is the point, since each loss cuts into what the others leave, and the product shows the biggest loss.
Reading an OEE result
A low OEE is a diagnosis waiting for a cause. Check which of the three factors is smallest, and start there. Compare the machine with itself over time before you compare it with another, because products, changeovers and planned stops differ. Use the same definitions each period so the trend means something.
Takt time sets the pace
Takt time is available production time divided by customer demand. With 450 minutes of production and demand for 90 units, takt time is 5 minutes per unit, or 300 seconds. A station slower than takt is a bottleneck, and one much faster is idle or overbuilding. Takt time comes from demand, so it changes when demand does.
Takt time is not cycle time
Cycle time is how long a station actually takes to complete one unit, and takt time is how long it should take to meet demand. Comparing the two shows the gap. If cycle time is longer than takt, the line cannot keep up. If it is much shorter, capacity is being wasted or inventory is building up ahead of the next step.
Safety stock buffers uncertainty
Safety stock is extra inventory held against demand that runs above average during the wait for a delivery. It grows with the variation in demand, with the square root of the lead time and with the service level you want. With daily demand varying by 20 units, a 9 day lead time and a 95 percent target, the buffer is about 99 units.
The reorder point
The reorder point is the stock level that triggers a new order. It equals average demand during the lead time plus safety stock. With 100 units a day, a 9 day lead time and 99 units of safety stock, the reorder point is about 999. Order when stock falls to that level, and the delivery should arrive before the buffer is used.
Little's law
In a stable system, the average number of items inside equals the arrival rate times the average time each item spends there. Thirty customers an hour who stay half an hour keep about 15 people in the room. It holds for queues, clinics, factories and backlogs, and it lets you find a third quantity from two you can measure.
Limits of the formulas
Each metric assumes steady conditions and consistent definitions. OEE ignores the value of the product, takt time ignores changeovers, safety stock assumes a stable demand distribution and Little's law assumes a stable system. When conditions change sharply, such as a demand spike or a supplier failure, recompute rather than trusting old averages.
Using the metrics together
The metrics connect. Demand sets takt time, takt time and OEE show whether the line can meet it, and safety stock protects against the times it cannot. Little's law links work in progress to lead time. Together they turn a vague sense that things are slow into numbers that point at a cause.
Worked with real numbers
What this looks like in the OEE (overall equipment effectiveness) calculator
The guidance above is easier to judge against figures. Using the OEE (overall equipment effectiveness) calculator worked example, moving availability from 86.0% to 94.0% changes the overall equipment effectiveness from 80.9% to 88.4%.
| Availability | Overall equipment effectiveness | Lost to availability, speed and quality |
|---|---|---|
| 86.0% | 80.9% | 19.1% |
| 88.0% | 82.8% | 17.2% |
| 90.0% | 84.6% | 15.4% |
| 92.0% | 86.5% | 13.5% |
| 94.0% | 88.4% | 11.6% |
Open the OEE (Overall Equipment Effectiveness) Calculator to use your own numbers →
The inputs behind those figures
| Input | Value | Definition |
|---|---|---|
| Availability | 90.0% | Run time as a share of planned time. |
| Performance | 95.0% | Actual speed as a share of ideal speed. |
| Quality | 99.0% | Good units as a share of all units made. |
What goes wrong
Common mistakes
Each of these produces an answer that looks reasonable, which is why they survive review. To catch them in operations metrics: OEE, takt time and safety stock, rerun the OEE (overall equipment effectiveness) calculator with a different assumption and check whether the result moves in the direction the guidance predicts, since an error that survives that test is usually in one of the inputs and not in the arithmetic.
| The mistake | Why it misleads | Do this instead |
|---|---|---|
| Averaging availability, performance and quality | The three factors compound, and an average hides most of the real loss. | Multiply the three shares. |
| Calling takt time the same as cycle time | Takt time is set by demand, while cycle time is what a station really takes. | Compare the two to find the gap. |
| Multiplying by lead time instead of its square root in safety stock | That treats each day's variation as perfectly linked and overstates the buffer. | Use the square root of the lead time. |
| Applying Little's law to a system that is filling up | The law describes a steady state, and a growing queue is outside it. | Use it when arrivals and departures are roughly balanced. |
People also ask
Frequently asked questions
What is OEE?
Overall equipment effectiveness, the product of availability, performance and quality. It shows how much of a machine's potential output is really delivered.
What is a good OEE?
It depends on the process. Compare a machine with its own history and look at which factor is the biggest loss.
How do I calculate takt time?
Divide the available production time by customer demand for the same period.
How do I calculate safety stock?
Multiply the service level factor by the standard deviation of daily demand and the square root of the lead time.
What is the reorder point?
The stock level that triggers an order: average demand over the lead time plus safety stock.
What is Little's law?
The average number of items in a system equals the arrival rate times the average time each spends in it.
How do these metrics fit together?
Demand sets takt time, OEE shows whether the line can keep to it, and safety stock and the reorder point protect against the gaps.
What is the economic order quantity?
The order size that balances ordering costs against holding costs, so total inventory cost is lowest. The EOQ calculator finds it from demand and the two costs.
How does inventory turnover relate to these metrics?
Turnover divides cost of goods sold by average inventory. High safety stock lowers turnover, so you trade the risk of a stockout against the cost of carrying stock.
Why is takt time recalculated when demand changes?
It is the ratio of available time to demand, so a shift in either number moves it. A takt time set for last quarter's demand can leave a line too slow or too fast.
How often should I measure OEE?
Track it every shift or every day and look at the trend, using the same definitions each time. A single reading says little.
Which input moves the overall equipment effectiveness most in the OEE (overall equipment effectiveness) calculator?
Ranked by how far each moves the overall equipment effectiveness across the range tested: availability (3.8%, 4.4%), performance (3.6%, 4.2%) and quality (3.4%, 4.0%).
If I double availability in the OEE (overall equipment effectiveness) calculator, does the overall equipment effectiveness double?
Doubling it from 90.0% to 180.0% takes the overall equipment effectiveness from 84.6% to 169.3%, which is 2.00 times the worked-example figure. So the result scales almost exactly in proportion. Halving it to 45.0% gives 42.3%.