Overall Equipment Effectiveness (OEE) is a manufacturing performance metric that is used to identify lost opportunities and measure improvement efforts.
OEE combines downtime, speed, and quality losses into one metric to determine how much quality product is produced compared to how much should have been produced in a given time. Essentially, OEE measures the percentage of time that is actually productive. Calculating OEE is done by multiplying three factors together:
Availability accounts for the unplanned downtime losses (breakdowns, changeovers) of a process by calculating the percentage of time the process is actually operating during the time it is available to operate. The typical calculation for the availability factor is:
Unplanned downtime includes changeovers and breakdowns. Available time excludes events that are beyond the control of the operations team, like power outages or production curtailments. Every company has different criteria for calculating available time, but the important thing is to be consistent and always calculate it the same way. Let’s look at an example for one day:
|Planned Downtime =||60 minutes|
|Available Time =||1440 – 60 = 1380 minutes|
|Unplanned Downtime =||331 minutes|
|Operating Time||(Available Time) – (Downtime) = 1380 – 331 = 1,049 minutes|
|= 0.76 or 76%|
The performance factor of OEE shows how well a machine runs by measuring the speed loss that occurs during operating time. Traditionally, the performance factor is calculated on a time basis.
The ideal cycle time is the time it should take to produce one unit. A simpler way to calculate the performance factor is to compare the actual quantity produced to the target production rate.
Both equations will give the same percentage result, but using production units instead of time is easier for everyone to understand. Here is an example performance factor calculation:
|Target production rate =||630 units per hour = 10.5 units per minute|
|Operating time =||1,049 minutes|
|Target units =||10.5 x 1,049 = 11,015|
|Actual units||9,020 units|
|= 0.82 or 82%|
Not all products that are produced meet the quality specifications. The quality factor accounts for quality losses by comparing the number of good units to the total number of units produced.
If there were 8,749 good units, the quality factor is calculated as follows:
|= 0.97 or 97%|
Once each of the factors (Availability, Performance, and Quality) have been calculated, they can be multiplied to determine the OEE. Using the values from the above examples:
|OEE = (Availability) x (Performance) x (Quality) =
0.76 x 0.82 x 0.97 = 0.60 or 60%
Although each of the individual parameters may seem acceptable on its own, the process is only producing quality product 60% of the time. This seems rather low doesn’t it? Actually, 60% is a typical OEE value for manufacturing operations and values under 50% are quite common. However, OEE is not meant for comparing different processes, or even similar processes with different circumstances. The following example illustrates the problem with OEE comparisons.
|Variable||Process A||Process B|
|Available Time (minutes)||1440||1300|
|Changeover time (minutes)||300||30|
|OEE (%)||.64*.92*.99 = 66%||.83*.80*.91 = 66%|
Both Process A and Process B have an OEE of 66%, but which one would you rather have? Process A has a lot of changeover time from product changes, but when it is running, it runs well. They could benefit from setup reduction or improved production scheduling. On the other hand, Process B has a much higher availability because there is much less changeover time, but would you like to have 91% quality instead of 99% like Process A?
You can see that there are many ways to get the same OEE, so care must be taken when making OEE comparisons. The power of OEE comes from analyzing the losses, not the resulting OEE score.
The OEE calculation is standard, but collecting the right values is anything but. Every facility is different and has different data collection needs.
When using OEE it is important to have a simple and flexible way to collect data and track downtime, speed, and quality losses so action can be taken on the operating floor. Automatic event tracking with real-time displays can help operators log information and make corrective actions in a timely manner. Dedicated OEE software, or a combination of a data historian and data visualization tools can be used to track and analyze production losses and calculate OEE.
Visit the dataPARC website for more information about software applications that can help with OEE analysis and reporting.
OEE: The Complete Guide
A free guide to help implement, analyze, and improve Overall Equipment Effectiveness.