OEE vs OPE (TEEP) – Which Should Manufacturers Calculate?
What’s the difference between OEE and OPE metrics in manufacturing? This is an interesting question I see asked a lot. Below, I’ll get into the difference between OEE and OPE (which is also known as TEEP) and highlight the advantages of tracking both.
In my synopsis below, I’ll also add in a different metric/measurement that is actually proving to be preferred by top manufacturing executives and business leaders.
What is OEE?
OEE stands for Overall Equipment Effectiveness. We go into more detail about what OEE is and what it’s not here.
OEE is a metric used to calculate and score all the elements of machine effectiveness in the manufacturing process. It combines availability, utilization, production, and quality metrics into the score. It is supposed to summarize the efficiency of a machine, cell, or production line during the manufacturing process.
Many manufacturers view it is an important metric to optimize around; however, we’ve talked before about some of the nuances that come with this metric that must be explored before it can be used as a true rubric for process and product improvement.
You can use a free OEE calculator here and calculate OEE yourself.
What is OPE? (also called TEEP)
OPE stands for Overall Production Effectiveness. It can also be called TEEP (Total Effective Equipment Performance).
The biggest difference between OPE and OEE is that OPE includes disconnected elements that may not be included in OEE calculations. This means that it includes activities like selective procedures and manual processes that don’t include the machines themselves or may not apply to every product in a production run.
OPE will also commonly include planned downtime in the calculation. This is not usually calculated in OEE.
Typically, employees must collect the data for the OPE calculation manually; as this is the point of the calculation. It captures processes that are not often easily measured by a sensor or machine and must have planned downtime inserted into the data.
As a next step, manufacturers concerned with OPE generally want to enter this information into some kind of database or analytics system to analyze the data more quickly and efficiently.
OEE vs OPE – which is better?
It always comes down to finding the right tool for the right job. When applied to the right business and the right problem correctly, both metrics offer benefits to manufacturers.
For example, SensrTrx allows manufacturers to track and visualize both depending on which makes sense for their business.
OEE is an industry standard across a lot of major industries. Theoretically, it is a very objective metric with a uniform set of criteria that allows manufacturers to improve their efficiency by using it as a measuring stick vs. previous calculations.
The biggest drawback of optimizing around OEE is that the number itself isn’t all that important. Often, manufacturing employees will fudge the numbers or run calculations so that they can maintain an industry acceptable OEE number.
It’s far more important for manufacturers to establish a true OEE metric for their business and then work to determine what factors are most important for them to optimize or improve based on accurate data.
What about OPE?
All the caveats about OEE also apply to OPE; however, OPE offers some additional benefits as a calculation to manufacturers that are accurately collecting and analyzing things like planned downtime and other manual processes. It simply provides some additional context.
With either metric, context really is what manufacturers should be seeking. Both metrics are meant to provide an objective snapshot of how efficient the manufacturing production line/cell/machine is operating. If the data is good, the measurement should really just be providing a point to compare and improve upon.
What is better to measure?
Truthfully, there is nothing wrong with choosing to believe in OEE or OPE as gospel, or choosing to completely ignore these metrics. I’ve heard manufacturers on both sides of the fence make a good case.
Ultimately, it is about the visibility on their factory floor that a manufacturer has that determines how helpful either metric will or won’t be.
The manufacturers that do the best job of improving their overall manufacturing productivity and efficiency are the ones with the best data and the clearest picture of what is actively influencing things like quality and availability.
I wrote a lot about visibility here and honestly believe it is a far better idea to work towards than obsessing over which metric to use and what the ultimate score is.
Again, you can measure both metrics easily with a tool like SensrTrx. It will also provide the visibility needed to properly improve the variables responsible for the culmination of both metrics.
Hopefully, that provides some insight into this topic. If you have questions, please leave them below.