Why OEE Is the Holy Grail of Manufacturing KPIs
In manufacturing we are always finding ourselves under pressure to deliver more products in less time and find ways to improve the efficiency and effectiveness of factory production. Like any business, this process begins with establishing key performance indicators KPIs and deducing which ones are the most valid for our needs.
In manufacturing, many brands in the space are increasingly turning to overall equipment effectiveness as the "Holy Grail” of manufacturing KPIs.
"OEE (Overall Equipment Effectiveness) is the gold standard for measuring manufacturing productivity,” says OEE.com. "Simply put – it identifies the percentage of manufacturing time that is truly productive. An OEE score of 100% means you are manufacturing only Good Parts, as fast as possible, with no Stop Time. In the language of OEE that means 100% Quality (only Good Parts), 100% Performance (as fast as possible), and 100% Availability (no Stop Time).”
In this article, we are going to break down the variables which make up OEE and discover how AI and machine learning are further enhancing OEE’s role as the Holy Grail of manufacturing KPIs.
Planned Production Time
Before you can begin calculating OEE, you need to establish your facilities planned production time. You do this by subtracting all scheduled loss – plant shutdowns, weekends, breaks/lunches, or periods where there are no orders, etc. – from All Time (every minute of every day or 24/7 time.
The number you are left with is your planned production time, and it’s this metric we are interested in when calculating OEE. After all, it would skew the results severely to include periods where you know in advance no production will be taking place.
Availability
This metric calculates the total number of planned and unplanned stoppages where no production is taking place. A score of 100% means the process is constantly running during planned production time.
Performance
Similar to availability but deals with more nuance as it refers to slow cycles and small stops, rather than the binary stop/go of the former. A score of 100% means there are no slow cycles or small stops, and the process is consistently running at maximum speed.
Quality
The quality variable refers to the quality of the parts being produced by the manufacturing process by counting the number of defects – including parts which can be reworked into good parts. A score of 100% means zero defects and only good parts are being produced.
OEE
You can now calculate OEE by multiplying availability, performance, and quality together and representing it as a percentage of planned production time. Obviously, these three factors all being considered together make achieving a high OEE score challenging. For example, even if you have a score of 90% in each category, this will result in an OEE score of just 73% - for reference, a world class OEE score is widely considered to be around 85%.
However, it’s important to consider that this world class ideal OEE was established some time ago (the 1970s), in another country – assuming you are not reading this in Japan – and for the automotive industry. Today, most manufacturers achieve an OEE score of around 60%, and there are actually far more companies out there with a score of 45% or lower than those with 85% and higher.
The real percentage of manufacturers who achieve world-class OEE is around 2.5%, with most brands considering the Goldilocks zone to be around 36-60%.
"The important point is – don’t fixate on the absolute value of the number,” continues OEE.com. "Fixate on your ability to improve that number. Even within a manufacturing plant it can be counterproductive to set just one OEE target. For example, if you have two identical production lines but one line makes a single product and the other line makes 10 different products would you expect them to have the same OEE score? No. The line making 10 different products is likely to experience far greater Availability Loss due to Changeovers and thus a lower OEE score.”
AI and Machine Learning
These technologies can help manufacturers improve OEE by offering greater insights and visibility in the availability, performance, and quality of their production processes.
Innovations such as predictive maintenance can improve availability by allowing manufacturers to address potential issues before they result in major stoppages; data analysis helps improve the overall performance of assets by identifying inefficiencies; and virtual modelling and digital twinning can improve quality and reduce defects, whilst also being better at identifying them when they do occur – allowing for more accurate data.
According to 2022 research by McKinsey, "[When] implemented successfully, [AI and ML] deliver irresistible returns. Across a wide range of sectors, it is not uncommon to see 30 to 50 percent reductions in machine downtime, 10 to 30 percent increases in throughput, 15 to 30 percent improvements in labor productivity, and 85 percent more accurate forecasting.”
Final Thoughts
Manufacturers searching for the Holy Grail of KPIs would do well to consider OEE. For our money, it offers the clearest and most comprehensive view of a factory’s overall performance and provides clear guidance for how and where improvements are needed. AI and machine learning can offer significant assistance in both calculating these factors and improving them.
OEE and the Holy Grail of manufacturing are sure to be part of the conversation at Connected Manufacturing Forum 2023, being held in June at the Westin Buckhead Atlanta.
Download the agenda today for more information and insights.