The Dangers of OEE as KPI for Manufacturing Operations Management

There is much hype in some circles about OEE (Overall Equipment Effectiveness) as a KPI (Key Performance Indicator)  for manufacturing companies, and as we know from experience,  we must always approach hype with some level of skepticism. This article discusses three reasons why OEE is a potentially dangerous KPI for a manufacturing company to rely on for business decisions and operations management.

What is OEE?

OEE is a measure of how well equipment in a plant are utilized in relation to their full potential. It was conceived to quickly identify areas contributing to big productivity losses including breakdowns, long setups, frequent stops, and defective output.

OEE = Availability x Performance x Quality. Availability, performance, and quality are good solid metrics, and the OEE formula is simple, so why not use OEE as a KPI for the organization? What dangers could be lurking under such simplicity?

Danger #1: OEE does not relate to the company’s true business goals

If the company gets paid to run machines at full capacity all day, OEE might be a decent metric for the business. For example, if you run an electric power company or a chemical processing plant, you might find that OEE relates to your bottom line. However, that is not the reality for many manufacturers. Companies that manufacture discrete products to fulfill customer orders have business goals related to factors that influence customer buying decisions: Schedule, Price/Cost, and Quality. Their KPIs should align with their business goals and OEE does not.

Danger #2: OEE does not address the real constraints to production 

From studying Eliyahu Goldratt’s “The Goal” and his Theory of Constraints (TOC) principles, we understand that the most important considerations in manufacturing operations are to keep the plant running to a “drum beat” and to mitigate the risks of any constraints that can affect the plant rhythm and choke the production rate. The Theory of Constraints is a holistic view that takes the entire plant into account. OEE is focused on local optimization of each work center, but the goal is optimization of the entire production system. OEE assumes that the goal is to keep each work center busy and producing at 100% capacity all the time. However, in the context of the entire production system, it might be acceptable to have areas of low utilization.

The goal is not to keep every work center and piece of equipment busy all the time; the real goal is to get product out on time to match demand, at a low cost, and with high quality. The organization’s metrics should be directly related to the real business goals that lead to the ultimate goal of most manufacturers: higher profits.

The Theory of Constraints is used by manufacturers to identify their production bottlenecks and then work to improve and eventually eliminate them. Not all resources in a plant are potential bottlenecks, so only resources that are (or have potential to become) constraints to production should be closely monitored and optimized for the company to achieve its real manufacturing goals. If the organization focuses on “fixing” work centers with the lowest OEE numbers, it might be under optimizing the overall macro manufacturing process. 

Danger #3: OEE is an aggregate metric that can obfuscate instead of clarify areas for improvement

Aggregate measures like OEE have the risk of hiding underlying issues. Each component of OEE in and of itself (availability, performance and quality) provides better visibility into the organization’s performance. When the sub-metrics are multiplied by each other, as is done with OEE, the resulting number can end up hiding the areas that have the most problems. For example, an area might have high availability and utilization numbers, but a low quality number, but because all of the numbers are multiplied together, the low quality number is hidden and therefore, not addressed.

Not only does OEE hide underlying issues, but it also muddies the waters when it comes to determining areas for improvement. OEE assumes that each of the sub-metrics have equal importance, but for many organizations, a 1% labor performance loss is not as important as a 1% quality loss. For example, it is easy to increase quality by increasing cost. The trick is to increase quality while reducing cost. An area with 90% quality and 70% performance has a different problem than an area with 70% quality and 90% performance, but they can both have the same OEE rate. 

In Summary

Considering the three dangers stated above, do OEE numbers really tell us anything important or useful about our business? Will they lead to more sales and profit? Or do they potentially misguide improvement prioritization efforts?

In addition to the dangers listed above, using OEE as a way to benchmark the business against others is not wise unless we are comparing across very similar types of businesses. The idea that you can have a benchmark goal for OEE of 85% across the industry is unsound. Perhaps it should be 95% for one type of process, and perhaps 70% is okay for another type of process.

If OEE is not the silver bullet metric for manufacturers, which one is? We shall explore that topic in a future blog post. What metrics should an organization look at to monitor profit, schedule and quality goals? What metrics help identify areas for improvement and areas of efficiency loss due to poor quality, low speed, equipment downtime or supplier issues? We should explore some Lean Six Sigma metrics like Cycle Time Efficiency and methods of identifying the underlying causes behind the loss of efficiency. Stay tuned. 

More information on OEE definition in this older post: 

“Overall Equipment Effectiveness (OEE) Or Overall Resource Effectiveness?”
http://www.manufacturing-operations-management.com/manufacturing/2009/11/overall-equipment- effectiveness-oee-or-overall-resource-effectiveness.html

 

Comments

[Mathew Littlefield]

Hi Conrad,

You make great points here, namely:

~OEE is not a silver bullet
~It is largely irrelevant if the drill down components aren't considered
~The system as a whole has to be considered and it is impossible to maximize OEE at every local asset and optimize the whole system
~OEE needs to be considered in the context of other metrics and business goals

It is also important to note that despite all of these issues, it still makes sense for many companies to measure OEE, even in A&D, of course accounting for all of these nuances.

Here is a summary of an interesting Twitter discussion on the same topic on our blog with many similar criticisms of OEE:

http://blog.lnsresearch.com/blog/bid/188869/5-Criticisms-of-the-OEE-Metric-in-140-Characters-or-Less

Matthew Littlefield
President Principal Analyst, LNS Research

[Conrad]

A very good article that demonstrates the danger of OEE...
http://www.industryweek.com/continuous-improvement/when-you-cant-count-numbers

[Jaidyn Moore]

Damn informative!! Thanks for sharing it. OEE is not a bad metric, but one should know how to use it. One can also use “Thrive” a very effective tool for OEE calculation professionals are recommending it. For more glimpse: – https://www.downtimecollectionsolutions.com

[Iguti]

Very good article Conrad, excellent as always!
I liked very much the link you've made with TOC and "The goal"