Common issues when measuring OEE
Although tools like Factbird’s Manufacturing Intelligence solutions are simple to set up and quickly deliver precise OEE calculations, there is still a learning curve when implementing them.
At the end of the day, manufacturing intelligence solutions are a tool for changing behavior, and many companies struggle to do this efficiently during the start-up stage of using OEE.
Here are some common issues that can creep in when calculating OEE.
Excluding too many stops
A good example of stops that are not included in downtime analysis are micro-stops.
Micro-stops are brief interruptions in the manufacturing process that typically last from a few seconds to a few minutes and have a high aggregate effect on production.
Most manufacturers tend to focus on large downtime events, and it’s easy to underestimate the significant losses associated with micro-stops.
It's vital to make it easy for operators to record micro-stops and to build a culture that supports their measurement. Automatic tracking of micro-stops is the best solution, as operators’ manual estimations of the effects of small stoppages will never be accurate.
So make sure you have automatic tracking of micro-stops to take away some of the burden, reduce the potential for inaccurate data, and better prioritize areas for improvement thanks to a more complete picture of your downtime.
Ignoring or underestimating changeover time
It’s common for teams to underestimate the impact that changeovers have on overall productivity and costs.
It’s easy to estimate 30 minutes for changeovers that take 40 minutes or more.
These inaccurate estimates quickly accumulate and result in large miscalculations for actual production availability.
Making it easier to accurately track changeover times as part of your OEE calculation process can help solve this issue.
Again, automatic detection of downtime with a system that makes it easy to categorize downtime events will result in more accurate downtime data.
Consider software that rates highly for usability or peripherals such as barcode scanners for categorizing downtime to make life easier for your team and improve your overall OEE calculations.
Underestimating the ideal production speed
An easy way to accidentally improve OEE scores is to underestimate your potential production speed.
To solve this potential issue, consider the specified validated speed for each machine, analyze the highest recorded speed in the plant, and test equipment by turning up the speed.
A big clue for a problem like this is having an OEE score that is close to or even over 100%.
Forgetting to exclude rework
Quality calculations are based on right-first-time counts. This means that rework items reduce your quality rate.
So remember to exclude units that need rework from right-first-time production counts. It’s in the name but can easily be missed.
Inaccurate scrap counts
The most common issue when measuring quality in OEE is inaccurate scrap counting.
It’s typically a time-consuming process performed manually by operators and prone to human error.
Fortunately, solutions for these problems can be found thanks to modern manufacturing intelligence tools and professional change management services.
You can track scrap automatically when pairing IIoT edge devices like Factbird® DUO with a sensor and proper training about where to place scrap.
Cultural change is hard
To sum up the previous points, making sure you have accurate and streamlined data collection in place is vital.
If you are automatically collecting downtime data and making stop-cause categorization easy for your team, then you have won half the battle.
The other half of the battle is cultural change.
Getting buy-in and training teams in new procedures requires considerable time and effort.
Operators, managers, and executives will often give pushback on new procedures and want to revert to the status quo.
By showing the value for the individual in better data and continuous improvement, you make the motivation intrinsic and will get much quicker improvements in your production processes.
And that’s it for this lesson. I hope you got some good tips on how to better implement OEE measurement. See you in the next lesson, where we will look at best practices for implementing OEE software!