OEE has been an effective way to track overall equipment performance for 30 years. However, it is simple to make mistakes in OEE calculation, and given how much they reduce expected returns, these mistakes are far too frequent.

We want to help you identify the biggest mistakes organizations frequently make when monitoring and calculating OEE and offer suggestions for what to do instead so that you can base your decisions on accurate data, carry out meaningful analysis, and utilize OEE to its full potential within your manufacturing environment.

  1. Not paying enough attention to all production stops

Mistakes in OEE calculation typically result from ignoring so-called „positive stops“. In manufacturing, planned and unplanned (or positive and negative) production stops are defined by whether a machine is running or not. Planned stops consist of changeovers, setup time, cleaning, and scheduled breaks such as lunchtime. 

Companies often overlook opportunities to improve the production process, because they consider most stops as planned. As the saying goes, the first step to solving any problem is admitting that you have one. In this case, acknowledging that some production stops are unplanned can be challenging depending on company culture. 

Ultimately, companies should strive for realistic reporting and a desire to truly understand root causes in order to minimize both planned and unplanned production stops.

  • Excluding operators from the OEE implementation process

Operators are the core of a manufacturing operation because they drive efficient daily production. Operators often have greater insight into process problems than anyone else, because they work directly with equipment each day. When you exclude operators from the implementation process, you reduce participation and commitment to driving OEE improvement. This may cause a loss of valuable knowledge and experience in improving processes or solving problems.

Reduced communication, insufficient data, or merely a superficial understanding of the data itself is often the result. Discovering pain points is often difficult, but just talking with your operators about their day-to-day work, or rewarding and motivating them, can help you understand problems related to OEE. 

  • Using standard speed instead of design speed

Standard speed refers to historical data on average run rates or throughput, while design speed, refers to the production speed as designated by the manufacturer, which is the highest achievable speed. 

Comparing operations efficiency using standard speed instead of design speed is misleading because it places a false upper limit on improvement. Companies that use standard speed are comparing OEE scores with average values and not maximizing throughput, when in fact there is more space for improvement. 

  • Insufficient data collection

To calculate OEE correctly, you must collect enough data for availability, performance, and quality scores. Because of the importance of these three components, it is crucial to ensure that your data collection efforts are sufficient. Data collection insufficiency is among the top OEE calculation mistakes we see in the manufacturing industry because of its ability to significantly alter results in unpredictable ways.

This is mainly caused by manual data collection methods; since manually collecting data is a low value add an activity, you may have highly trained machine operators performing these tasks rather than their core jobs. Some of the other reasons can be that operators do not engage in the process properly, or it could be that the company uses standard speed instead of ideal cycle time to calculate OEE. 

  • Focusing only on the OEE score

While companies should focus on the OEE score, they should also analyze the factors that make up the OEE score—Availability, Performance, and Quality. These components can help you identify where production losses are happening and areas for improvement on the shop floor. Availability, performance, and quality allow you to perform detailed root cause analysis and avoid the problem of implementing solutions to the addressed symptom. 

  • Comparing the OEE scores of different plants and machines

Another huge mistake organizations make, is that they compare the OEE scores of different plants, production lines, and machines. This comparison is always a bad idea because it hides important and actionable details. 

In this kind of comparison, you need to take features into consideration, for example, a machine that produces small batches and another that produces larger batches cannot be compared in the same analysis.

According to its OEE goals and environment, each line or machine should be examined independently. Most importantly, keep in mind that OEE ratings should only be compared when machines, processes, and materials are similar. Try avoiding these mistakes to collect accurate production data, work on real numbers, make decisions and develop solutions to achieve desirable results. Once you do this, you and your team will find out that taking action is a lot easier than you thought.