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Measuring & Optimizing Overall Equipment Effectiveness with Machine Learning

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Overall equipment effectiveness (OEE) is a metric used in manufacturing operations to see and understand how efficiently processes and equipment are being used. It looks at facilities, time, materials, and the productivity of each of those discrete aspects of the manufacturing process. Given the budget constraints that all manufacturers face, it’s obvious that equipment effectiveness measures are so popular and necessary.

To dive into the concept of overall equipment effectiveness further, there are three major aspects that you must consider when it comes to setting key performance indicators (KPIs) for OEE.

  • Availability: The amount of time that the equipment is ready and useful for operation
  • Performance: How well the equipment works at maximum operating speed
  • Quality: The percentage of ‘good’ parts that are produced (as opposed to parts that need to be scrapped)

Key Performance Indicators for Overall Equipment Effectiveness

Because each of these KPIs is quantifiable, there are calculations managers can use to benchmark their performance, thus allowing them to find places for improvement and then dial up the optimal outputs for their processes. The KPIs are fairly straightforward to calculate.

Availability is measured as the portion of time that a given machine is ready to operate. It is sometimes referred to as ‘uptime’, and is designated as a simple ratio of operating time over scheduled time, where operating time takes into account the actual measured time that the machine is used, and scheduled time would be, for example, the time that the machine is used on a shift.

Performance is measured as a ratio of the actual pace of the machine over the designed speed of the machine, and as such doesn’t consider either quality or availability. As an uncomplicated example, if a machine can travel at 100 MPH, and it’s only driven at 50 MPH, the performance would be shown as 50%.

Quality is measured as the ratio of good parts to the overall total of the parts output of the machine. This is also considered the yield.

Key Benefits of Using Overall Equipment Effectiveness

The old saying ‘you can’t manage what you can’t measure’ applies quite well to OEE. Having the ability to measure each of these KPIs means manufacturers that adopt OEE can better manage their processes.

Some of the benefits of using OEE as a tool for manufacturing include:

  • Optimizing machine usage: Developing an understanding of a machine’s performance allows manufacturers to optimize that performance with subtle adjustments
  • Improving process quality: Producing fewer defective products means less waste and better ROI
  • Reducing repair costs: Knowing the expected machine efficiency means that proactive measures can be taken to repair prior to major breakdowns

Optimizing Overall Equipment Effectiveness with ML

From an OEE perspective, it’s a best practice to measure at the step in the process where there’s a bottleneck, or potential constraint. No matter what’s being manufactured, there’s always a point in the process that can become an obstacle. It’s at that point that OEE is critical to understand what’s happening, as it’s there that determines the overall performance.

In the past, measuring OEE and making adjustments based on those measures was something that happened largely manually, and was based on historical knowledge. But it shouldn’t come as any surprise that each of these is an ideal use case for machine learning. According to Accelerate Your Data-Driven Transformation, a commissioned study conducted by Forrester Consulting on behalf of RapidMiner, 76% of manufacturers view increased operation efficiency as a very important or critical outcome of machine learning initiatives. As we describe in our blog post on predictive maintenance, the major driver for the Industry 4.0 Revolution is the rapid development of the Internet of Things (IoT).

As sensors become more embedded in machines, as well as integral to manufacturing, it becomes easier for manufacturers to automatically measure the necessary components to optimize their intricate operations. The data that is generated by the sensors can be used to ensure that the appropriate machine learning algorithms have enough data to be useful.

For the above-mentioned three major aspects of OEE (availability; performance; quality), here’s how machine learning can be leveraged:

  • Availability: Machine learning algorithms can help lower the amount of time needed to setup or retool manufacturing lines, based on previous similar occurrences, helping to increase OEE
  • Performance: The data gathered can help an ML algorithm identify roadblocks or slowdowns in production, and then leverage predictive maintenance to lessen or eliminate them
  • Quality: ML algorithms can be applied to increase the usable manufacturing yields of a process

Final Thoughts

OEE is a valuable tool in almost every manufacturing operation and, by using the proper machine learning techniques, manufacturers can truly optimize their operational efficiencies, easily and automatically.

If you’re curious to learn more about the effects that AI and ML are having on your manufacturing processes, sign up for a free, no obligation AI assessment. We’ll help you explore the most impactful ways you can use AI in your operations.


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Kristen M. Vaughn

Kristen M. Vaughn

Kristen Vaughn is a Digital Marketing Manager at RapidMiner. She develops, manages, and executes digital strategies to better reach audiences, provide the information that users are looking for and create engaging experiences across online channels.