Calculating and monitoring overall equipment effectiveness (OEE) is one of the most important things you can do as a manufacturer. It is a unified metric that shows you where your production performance is at, and can highlight key areas in which you need to improve.
However, a lot of mistakes are made when calculating OEE that manufacturers need to be aware of. Being conscious of these common issues will ensure that you are not missing out on good opportunities for growth.
Overemphasis on the overall OEE Score
The OEE score is a combination of several critical factors, and while it's tempting to focus on this single number, it doesn't tell the full story. An excellent overall score can mask underlying issues in specific areas, leading to complacency and missed opportunities for improvement.
For example, you may have a really great availability and performance score that is making up for a poor quality score. Looking at the entire picture may mislead you into thinking you don't need to make any improvements, but looking at it more granularly will show that there's room for improvement.
How to Fix It: Treat the OEE score as a summary, not the entire report. It's like having a health check-up. The overall health score is useful, but you also want to know your blood pressure, heart rate, and cholesterol level! Break it down into its components and analyse each area separately to identify specific issues and opportunities. This approach ensures that you understand the strengths and weaknesses of your production process, allowing you to make targeted improvements where they're most needed.
Not Involving Operators
Operators are your eyes and ears on the production floor. They have a firsthand understanding of the nuances of the equipment and are often the first to notice when something is off. However, their insights are frequently overlooked, especially when decision-making is disconnected from the day-to-day operations.
How to Fix It: Create a culture of involvement and communication. Encourage operators to share their observations and suggestions, and involve them in problem-solving initiatives. Regularly hold team meetings to discuss OEE results and brainstorm improvement strategies. This not only taps into their valuable insights but also fosters a sense of ownership and accountability, leading to a more engaged and proactive workforce.
Poor Data Collection and Analysis
Reliable data is the foundation of effective OEE measurement, but manual collection methods are error-prone and often result in incomplete or inaccurate data. In order to get the full picture of your performance, you must measure OEE over weeks, if not months. Focusing on one day or one shift allows for too much situational error to occur, so you need a long term average.
Collecting and storing this data manually will prove to be an absolute nightmare. You need a system that not only collects all this data for you, but completes further analysis. Without proper analysis, even the best data won't lead to meaningful insights or improvements.
How to Fix It: Invest in automated data collection technologies to ensure accuracy and comprehensiveness. Pair this with robust data analysis tools and techniques to extract actionable insights. It's also important to train your team to interpret and use this data effectively, as this will help to turn numbers into strategies. Regularly review your data collection and analysis processes to ensure they remain aligned with your operational goals and technological advancements.
Busroot by Output.Industries is the ideal tool to succeed in the above, as it automatically records AND analyses your data for you. Busroot can automatically calculate your OEE score, find exactly where your key issues are, and suggest ways to improve your processes. To check it out for yourself, schedule a demo with us and we will show you how Busroot can transform your business.
Comparing Dissimilar Processes
OEE is most effective when used to measure and compare similar machines or processes. Attempting to compare vastly different operations can lead to misleading conclusions, as the unique context and variables of each process are not accounted for.
For example, it is pointless to compare a production line that requires 1 changeover a day, with one that requires 5. The one that requires 1 changeover will show an artificial advantage over the other due to a higher availability score, and this will give manufacturers a false sense of success.
How to Fix It: Use OEE to track and compare the performance of similar machines, lines, or processes over time. You need to establish clear and relevant benchmarks for each specific operation, taking into account its unique characteristics and constraints. This targeted approach allows for more accurate assessments and more effective, tailored improvement strategies.
Not Including Changeover Times
Changeover times can significantly impact your overall efficiency, but they're often excluded from OEE calculations, as they can't be avoided. This oversight can lead to an incomplete understanding of where time is lost and how the production process can be streamlined.
For example, say in an 8 hour shift you have 3 changeovers at 10 minutes, and you're experiencing approximately 1 hour of lost time due to planned and unplanned stops. Without including your changeover time, you'd have a score of 87%. Including your changeover time, you'd have a score of 81%. You can see how this artificially increases your score, giving an inaccurate reflection of your production performance.
How to Fix It: Ensure you integrate changeover times into your OEE calculations to capture a more comprehensive view of your operational efficiency. Analyse these periods to identify bottlenecks and implement strategies to reduce downtime, such as standardised procedures, employee training, or equipment upgrades. Regularly review and refine these processes to keep your production running smoothly and efficiently.
Using Average Cycle Time to Calculate Availability
Another key mistake when measuring OEE is using average cycle time rather than peak cycle time to calculate availability. Manufacturers may think that using the average cycle time shows a more realistic picture of that production line, but this is inaccurate. It will take into account all the delays and inefficiencies inherently present in the process, which are the things you are trying to highlight. It will also increase your OEE score, again providing a false sense of success.
The planned cycle time should be based on the fastest speed that the production line can operate, as specified by the manufacturer of the machine.
How to Fix It: Shift your focus from the average to the peak cycle time when evaluating Availability. Identify your machine's peak cycle time under ideal conditions and use this as the benchmark for measuring performance. This approach highlights the discrepancies between the current and ideal performance, offering a clearer picture of where and how to streamline processes.
This is another benefit of Busroot, it takes all of your historic data and uses the quickest cycle time on record. A lot of manufacturing analytics tools only extrapolate a cycle time based on assumptions, whereas Busroot actually takes your data and uses it in it's OEE calculations.
Focusing on the OEE of the Entire Facility
A facility-wide OEE score can provide a broad overview, but it can also obscure the specific issues and improvement opportunities of individual machines or production lines. Discrete manufacturers can have anywhere from 10-50 different machines and production lines, so an overall score would make it impossible to see where the issues lie. This lack of detail can lead to misdirected efforts and overlooked problems.
How to Fix It: Break down your OEE analysis to focus on individual machines or lines. This granular approach allows you to identify and address specific issues, leading to more targeted and effective improvements. You should also regularly compare the performance of each component to identify trends, share best practices, and ensure a balanced and cohesive approach to efficiency across your facility.
By recognising and addressing these common OEE pitfalls, you can refine your approach to measuring and improving manufacturing efficiency. This proactive and detailed strategy ensures that your OEE efforts lead to meaningful improvements, driving your production process towards optimal performance and productivity.