Top Three Reasons You Can’t Fully Rely on OEE as a KPI - Stefanini

Top Three Reasons You Can’t Fully Rely On OEE As A KPI

OEE is often used to measure plant productivity. Yet, when it’s the only metric manufacturers rely on, the data it produces can become skewed. How so? Read our Trends blog for the answer!

When it comes to manufacturing, it is a common practice to measure plant productivity by studying Overall Equipment Effectiveness (OEE). As we’ve written about before, OEE is measured by looking at how effective a plant is running in the areas of availability, performance, and quality. Yet, holding OEE as the primary success for a plant can be problematic – and sometimes, even short-sighted.

How is this possible? Read on for the answer.

Not totally sure what OEE is? Check out our recent blog post for the answer!

Relying On OEE Can Become Problematic

According to Industry Week, over many years, OEE has become a key metric for measuring productivity improvement for manufacturers. However, when we set OEE as the one success metric, OEE numbers are elevated to unreasonable and artificial heights. At the expense of overall plant efficiency, manufacturers are trying to hit “world-class” percentage numbers. By trying to look perfect, they are focused only on what is working, ignoring areas that need improvement in the process.

This approach leads to a circle of inefficiencies. The pressure to reach high, over-improving OEE numbers motivates workers and plant managers to only report positive data or over-inflate efficiency results. When workers are motivated only to reach high OEE percentages, this does not mean they are necessarily improving operations. Workers might tend to justify not properly reporting discount line downtime or all production stoppages. Further, there’s no accountability for solving problems.

Rather, manufacturers need to shift focus to finding problems quickly and solving them. When you focus on improvement, you empower workers to look for inefficiencies in production and make corrections, instead of asking them to ignore problems in order to achieve a high OEE. When it adopts a mindset that is constantly seeking to improve, plant management can maintain a focus on transparency and accuracy, which leads to more reliable data. This improved data allows managers to truly solve problems by scheduling preventative maintenance, reducing downtime, and justifying needed capital expenditures.

How can data be used to drive decisions in manufacturing? Watch the replay of our most recent Smart Manufacturing webinar!

The Top Three Dangers of OEE

While OEE is a useful metric in a number of ways, relying simply on OEE to improve business processes may not be the answer. How so? According to Manufacturing Operations Management Talk, here are three reasons a company shouldn’t only look to OEE:

1)     OEE does not relate to the company’s true business goals

OEE might be a decent metric for a business if the company got paid to run machines at full capacity all day. For instance, OEE might relate to your bottom line if you run an electric power company or a chemical processing plant. Unfortunately, this is not the reality for most manufacturers. Companies that manufacture discrete products to fulfill customer orders have business goals related to the things that influence customer buying decisions: price/cost, schedule, and quality. Their KPIs should align with their business goals and OEE, which measures how well equipment in a plant are utilized in relation to their full potential, does not relate to such factors.

2)     OEE does not address the real constraints of production

According to business management guru Eliyahu Goldratt’s “The Goal” and his Theory of Constraints (TOC) principles, 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 choke production rate and affect plant rhythm. The Theory of Constraints takes the entire plant into account in its holistic view of plant operations. While OEE is focused on optimizing each work center, the goal is optimization of the entire production system. OEE estimates that the goal is to keep each work center busy and producing at 100% capacity all the time. However, when taking into account the context of the entire production system, it could be acceptable to have areas of low utilization. The reality is that the goal is not to keep every piece of equipment and work center busy all the time, but to get product out on time to match demand, at high quality with a low cost. The company’s metrics should be directly related to the real business goals that lead to the ultimate goal that most manufacturers have: higher profits. Manufacturers use the Theory of Constraints to identify and improve/eventually eliminate their production bottlenecks. Since not all resources in a plant are potential bottlenecks, only resources that are (or have the potential to become) constraints to production should be closely watched and optimized for the company to achieve its real manufacturing goals. If companies focus on “fixing” work centers with the lowest OEE numbers, it might be under optimizing the overall macro manufacturing process.

3)     OEE is an aggregate metric that can complicate instead of clarify areas for improvement

Underlying issues might be hidden by aggregate measures like OEE. Of course, each component of OEE in and of itself (availability, quality, and performance) provides better visibility into the organization’s performance. When the sub-metrics are multiplied by each other, which is the case with OEE, the resulting number can end up hiding the areas that have the most problems. For instance, an area might have high utilization and availability numbers, but a low quality number, but because all of the numbers are multiplied together, the low quality number is hidden and, as a result, not addressed. Further, in addition to hiding underlying issues, OEE also can make things confusing when it comes to determining areas for improvement. OEE assumes that each of the sub-metrics contain equal importance, but for many companies, a 1% labor performance loss is not as important as a 1% quality loss. For example, quality can easily be increased by increasing cost. Yet, the real trick is to reduce cost while increasing quality. An area with 90 percent quality and 70 percent performance has a different problem than an area with 70 percent quality and 90 percent performance, but they both may have the same OEE rate.

What types of manufacturing solutions do we offer? Check them out here!

So, all of these points bring us to the question – do OEE numbers really tell us anything important or useful about our business? Can they lead organizations to more sales and profit or do they potentially misguide improvement prioritization efforts? Along with the dangers listed above, using OEE as a way to benchmark the business against others is not a great idea unless we are comparing across very similar types of businesses. It is unsound to have a benchmark goal for OEE of 85 percent across the industry; rather, it should be 95 percent for one type of process and perhaps 70 percent is great for another type of process.

Smart Manufacturing with Stefanini

At Stefanini, we provide end-to-end support to plan, execute, sustain, and innovate your lean manufacturing operations. By evaluating your factory’s unique environment, we can provide custom OT and IT service integration. Our priorities reflect your objectives like increased efficiency, increased OEE, asset reliability, decreased O&M cost, and reduced operational risk.

Interested in learning more? Let’s chat! Visit our website to reach out to us.

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