OEE in Perspective
As mentioned in our previous posts, OEE is a terrific metric for measuring and monitoring ongoing performance in your operation. However, like many metrics, it can become the focus rather than the gage of performance it is intended to be.
The objective of measuring OEE is to identify opportunities where improvements can be made or to determine whether the changes to your process provided the results you were seeking to achieve. Lean organizations predict performance expectations and document the reasons to support the anticipated results . The measurement system used to monitor performance serves as a gauge to determine whether the reasons for the actual outcomes were valid. A “miss” to target indicates that something is wrong with the reasoning – whether the result is positive or negative.
Lean organizations are learning continually and recognize the need to understand why and how processes work. Predicting results with supported documentation verifies the level of understanding of the process itself. Failing to predict the result is an indicator that the process is not yet fully understood.
Problem Solving with OEE
Improvement strategies that are driven by OEE should cause the focus to shift to specific elements or areas in your operation such as reduction in tool change-over or setup time, improved material handling strategies, or quality improvement initiatives. Focusing on the basic tenets of Lean will ultimately lead to improvements in OEE. See the process in operation (first-hand), identify opportunities for improvement, immediately resolve, implement and document corrective actions, then share the knowledge with the team and the company.
Understanding and Managing Variance:
OEE data is subject to variation like any other process in your operation. What are the sources of variation? If there is a constant effort to improve performance, then you would expect to see positive performance trends. However, monitoring OEE and attempting to maintain positive performance trends can be a real challenge if the variances are left unchecked.
What if change-over times or setup times have been dramatically reduced? Rather than setting a job to run once a week, it has now been decided to run it daily (five times per week). What if the total downtime was the same to make the same number of parts over the same period of time? Did we make an improvement?
The availability factor may very well be the same. We would suggest that, yes, a signficant improvement was made. While the OEE may remain the same, the inventory turns may increase substantially and certainly the inventory on hand could be converted into sales much more readily. So, the improvement will ultimately be measured by a different metric.
Cycle time reductions are typically used to demonstrate improvements in the reported OEE. In some cases, methods have been changed to improve the throughput of the process, in other cases the process was never optimized from the start. In other instances, parts are run on a different and faster machine resulting in higher rates of production. The latter case does not necessarily mean the OEE has improved since the base line used to measure it has changed.
Another example pertains to manual operations ultimately controlled through human effort. The standard cycle time for calculating OEE is based on one operator running the machine. In an effort to improve productivity, a second operator is added. The performance factor of the operation may improve, however, the conditions have changed. The perceived OEE improvement may not be an improvement at all. Another metric such as Labour Variance or Efficiency may actually show a decline.
Another perceived improvement pertains to Quality. Hopefully there aren’t to many examples like this one – changing the acceptance criteria to allow more parts to pass as acceptable, fit for function, or saleable product (although it is possible that the original standards were too high).
Changing standards is not the same as changing the process. Consider another more obvious example pertaining to availability. Assume the change over time for a process is 3o minutes and the total planned production time is 1 hour (including change over time). For simplicity of the calculation no other downtime is assumed. The availability in this case is 50% ((60 – 30) / 60).
To “improve” the availability we could have run for another hour and the resulting availability would be 75% (120 – 30) / 120. The availability will show an improvement but the change-over process itself has not changed. This is clearly an example of time management, perhaps even inventory control, not process change.
This last example also demonstrates why comparing shifts may be compromised when using OEE as a stand-alone metric. What if one shift completed the setup in 20 minutes and could only run for 30 minutes before the shift was over (Availability = 60%). The next shift comes in and runs for 8 hours without incident or down time (Availability = 100%). Which shift really did a better job all other factors being equal?
When working with OEE, be careful how the results are used and certainly consider how the results could be compromised if the culture has not adopted the real meaning of Lean Thinking. The metric is there to help you improve your operation – not figure out ways to beat the system!
We are currently offering our Excel OEE Spreadsheet Templates and example files at no charge. You can download our files from the ORANGE BOX on the sidebar titled “FREE DOWNLOADS” or click on the FREE Downloads Page. These files can be used as is and can be easily modified to suit many different manufacturing processes. There are no hidden files, formulas, or macros and no obligations for the services provided here.
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