Overall Equipment Effectiveness (OEE) is a valuable metric that measures the efficiency of a machine.
OEE takes into consideration the Availability, Performance, and Quality of a machine. OOE (Overall Operational Effectivness) and TEEP (Total Effective Equipment Performance) are identical to OEE - except in how they define Availability.
In mathematical terms, OEE = Availability * Performance * Quality.
For the purposes of calculating OEE, these underlying metrics are defined as follows.
Availability is calculated differently for each of the OEE metrics.
The amount of time the machine is in-cycle is compared to the amount of time a job has been scheduled on the machine.
The amount of time the machine is in-cycle compared to all-time during scheduled shifts.
The amount of time the machine is in-cycle compared to all time.
Performance refers to the speed that parts are produced while the machine is in cycle. It is calculated as the average in-cycle time to produce a part divided by the ideal in-cycle time to produce a part. The more parts produced within a period of time, the higher a machine's performance percentage.
The quality metric is the percentage of parts that are good. Any rejected parts from a cycle will lower the quality percentage.
Example Calculations for OEE
As an example, say a machine produces 1 part per cycle and a cycle takes 10 minutes to complete including setup.
Theoretically, the machine should produce 48 parts over the course of an eight hour shift.
8 hours * 60 minutes per hour = 480 minutes / 10 minutes per cycle = 48 cycles * 1 part per cycle = 48 parts
If a machine actually produces 48 good parts with no downtime over the course of a shift it's OEE will be 100%
Availability = 8 Hours / 8 Hours = 100%
Performance = 10 minutes per part / 10 minutes per part = 100%
Quality = 48 good parts / 48 good parts = 100%
OEE = 100% * 100% * 100% = 100%
In the real world, this machine may be more likely to be up for 6 hours of its scheduled 8 hours uptime. It may still produce the required 48 parts but 6 of these parts are rejected and not considered good.
In this case:
Availability = 6 Hours / 8 Hours = 75%
Performance = 10 minutes per part / 10 minutes per part = 100%
Quality = 42 good parts / 48 good parts = 87.5%
OEE = 75% * 100% * 87.5% = 65.6%
Note that in this case the performance metric was still considered to be 100% since the machine reached its part production goal. OEE is such a valuable measure because it takes these underlying metrics into consideration. By using OEE to measure your machine's efficiency, you are taking a much more comprehensive approach to identifying weak links on your shop floor. Not only does OEE allow you to identify these weak links, it also provides you with a value to determine which links are the weakest.
In the case above, Availability dropped to 75% because of 2 hours of unscheduled downtime and Quality dropped to 87.5% because of 6 rejected parts. In order to best improve our OEE in this scenario, we should work on raising our Availability since it has a lower value and brings down OEE the most. If it was possible and cost effective to upgrade equipment in a way that increases the machine's in-cycle time to 7.5 hours of each 8 hour shift, the availability would increase to 93.75%. In this case the overall OEE in the above example would be 82%.
Now, OEE = 93.75% * 100% * 87.5% = 82%
Even if there is no improvement in the Quality of the parts produced by this machine, our OEE improves by nearly 17 points.
An increase in Overall Equipment Effectiveness results in an increase in revenue as more good parts are being produced in a timely manner. This is why MachineMetrics provides you with this metric. We want to supply you with the information necessary to create a more efficient shop and OEE is an important piece of data that will help you to do so.
The queried range of time.
The amount of time over the queried range of time covered by non-optional shifts. (See the company page on your dashboard and click on a shift to see if it is set to optional.)
The amount of time that a machine has been scheduled to operate over the queried range of time. (Scheduled time includes all time when a job was running on a machine.)
Scheduled Time In-Cycle
The amount of time a machine has been in-cycle (active) during its scheduled times.
Ideal Part Time
The theoretical shortest amount of time that a machine should be able to complete a part.
The total number of parts produced over the queried range of time.
The number of Total Parts that represent good parts.
OEE has three components. Availability, Performance, and Quality.
Scheduled Time In-Cycle / Scheduled Time
Ideal Part Time / (Scheduled Time In-Cycle / Scheduled Total Parts )
Good Parts / Total Parts
OEE, OOE, and TEEP
OEE = Availability * Performance * Quality
OOE = OEE * (Scheduled Time / Non-Optional Time)
TEEP = OEE * (Scheduled Time / All Time)
There is no standard method for aggregating OEE, and the concept of an aggregate OEE isn't consistent due to the nature of how OEE is calculated. When we aggregate OEE, we're creating a single OEE value that accounts for time on multiple different jobs or similar jobs running in different equipment resulting in different ideal cycle times. We can still attempt to create a composite value by weighing all of the individual items with an appropriate factor. In choosing factors for OEE and components of OEE, we also want to maintain the invariant that (OEE = Availability * Performance * Quality) both for individual items and the composite.
Each component of OEE is weighted by both a factor unique to itself and a Value Function that is shared by all the components.
The Value Function factor is any weighting factor from an individual item that is applied consistently to all three components. This factor can change the resulting availability, performance, and quality values that come out in aggregation, but will never affect the aggregate OEE value (it cancels out). Examples of value functions are ideal part time and part value. By default, MachineMetrics calculates OEE with ideal part time.
For more information on how we aggregate OEE, please contact email@example.com
If you're interested in further reading, check out our blog post!