OEE vs. OPE vs. TEEP – Which Should Manufacturers Calculate?
What’s the difference between OEE vs. OPE vs. TEEP metrics metrics in manufacturing? This is an interesting question I see asked a lot. They’re metrics often used, but each is a different method of determining the availability of a production line. Essentially, they help manufacturers understand how many goods they can produce versus how many they are actually producing. All three metrics look at performance, availability, and quality.
- Below, I’ll get into the difference between OEE vs. OPE and OEE vs. TEEP and highlight the advantages of tracking both.
- In my synopsis, 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, performance, 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 as 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. (And, some even debate the role scheduled downtime plays in calculating OEE.) You can use a free OEE calculator here and calculate OEE yourself.
What is OPE? What is TEEP?
OPE stands for Overall Production Effectiveness. It can also be called TEEP (Total Effective Equipment Performance). However, TEEP is the more commonly used metric in manufacturing.
“What is the difference between TEEP and OPE, though?” TEEP measures the percentage of the total time that is productive. TEEP goes one step further than OEE by evaluating the potential of a plant – how much capacity a manufacturer has to produce without having to buy new equipment.
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 things like the time when a machine is not scheduled to run and 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 as we mentioned above. 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.
What is OOE?
While we haven’t really talked about this metric, OOE (Overall Operations Effectiveness) is worth mentioning. OOE measures the availability of all operations from start to finish, including things like planned maintenance.
To calculate OOE, you would use the same formula as OEE, but the primary difference would be how you determine availability. You would include actual production time/ uptime in the OOE formula whereas OEE only includes scheduled time.
While it’s good to have an understanding of OEE, it is rarely used to understand production availability.
OEE vs. OPE and OEE vs. TEEP – 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, all three metrics offer benefits to manufacturers.
For example, Mingo allows manufacturers to track and visualize, automatically, OEE, OPE, and TEEP 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 are the Benefits of 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 efficiently the manufacturing production line, cell, or machine is operating. If the data is good, the measurement should really just be providing a point to compare and improve upon.
Understanding Overall Factory Efficiency: Is OEE, OPE, or TEEP Better to Measure?
Truthfully, there is nothing wrong with choosing to believe in OEE vs. 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 write a lot about visibility 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 Mingo. 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.