Why is OEE not enough as your only KPI?

OEE is not sufficient as a standalone KPI because the figure shows that production is losing efficiency, but not why. It says nothing about the cause of the losses, can be improved in ways that harm the business, and cannot be fairly compared between factories.

To create value, OEE needs to be supplemented with loss analysis, quality metrics, changeover times, energy data, and flow metrics. Above all, it needs to be used as a basis for improvement work, not as a score on a screen.

This guide explains why OEE is not enough as a standalone KPI.

We look at the most common misconception about what actually stops production, three concrete reasons why OEE becomes misleading when used in isolation, what the metric needs to be supplemented with, and what is required for OEE to create real value.

If you want the basics first, feel free to read the article "What is OEE and how is it calculated?".

Is production stoppage primarily a machine problem?

No. Data from factories that have worked with structured OEE for many years show that approximately 80% of stoppage causes are not directly machine-related. They are about material shortages, changeovers, order delays, quality issues, planning errors, communication between shifts, and a lack of operators. A common assumption is that stoppages are mainly due to machine breakdowns, and that the solution is better maintenance and smarter machines. The reality is quite different.

This has a direct bearing on how OEE should be interpreted. A low OEE figure says nothing in itself about where the problem lies. If 80% of the losses are not due to the machines, a factory that invests only in better machines and more maintenance will be disappointed with the results. The big potential lies elsewhere, in working methods, planning, material flows, and in the operators' knowledge of what is actually happening on the floor.

It also has a bearing on how data should be collected. Automatic collection from machines can show that a stoppage has occurred. It can rarely show why. Operators need to be able to code the cause, quickly and easily, at the very moment the stoppage occurs. Otherwise, the largest share of losses ends up in the "Other" category, making it impossible to work with. At Orkla Nidar, a candy manufacturer in Norway since 1912, operators were able to eliminate the "Other" category from reporting as soon as they received tools that made it easy to code the correct cause directly.

What are the reasons why OEE is not enough as a standalone KPI?

OEE is not enough on its own for three reasons. It says nothing about why the losses occur; it encourages wrong behaviours when it becomes the only metric; and it cannot be compared across different factories. We will go through them one by one.

1. It says nothing about why

An OEE of 62% does not say whether the problem is due to breakdowns, changeovers, quality, or staffing. Without loss analysis, OEE just becomes a thermometer showing a fever without a diagnosis. You know something is wrong, but not what, and therefore not what you should do about it.

The value only arises when the number can be broken down. By stoppage cause, by cause group, by line, by item, by shift, by machine part, and as a trend over time. This makes it possible to answer the questions that actually lead to action. Why does item A perform worse on line 1 than on line 2? Which stoppage causes have increased in the past month? Which losses are frequent but short? Which are rare but expensive? It is in the breakdown, not in the total figure, that the improvement work finds its direction.

2. It encourages wrong behaviours

If OEE becomes the only metric, factories start optimising the figure instead of the operations. This rarely happens intentionally, but it happens.

A common example is subtracting time that "should not count" until the figure looks good, without anything actually having improved. Another is running machines that really should not be run, just to maintain availability. A third is reducing the number of changeovers by running longer series. This raises OEE in the short term but lowers flexibility, increases inventory, and ties up capital. The factory looks more efficient on paper while becoming less competitive in practice.

When a metric becomes a goal, it often ceases to be a good metric. OEE is no exception. Therefore, it needs to be balanced against metrics for flow, delivery precision, inventory, and quality.

3. It cannot be compared between factories

OEE values depend on how "planned production time", "ideal cycle time", and stoppage causes have been defined. Two factories that calculate differently can have the same actual efficiency but completely different OEE figures, or the same figure despite major differences in actual performance.

Comparing OEE values from different factories, therefore, almost always leads to an error. It can also damage the culture of improvement. When factories or shifts are compared on a single figure, incentives arise to make the figure look good rather than to improve what lies behind it. Competition over the number replaces collaboration on improvements. For a group with multiple facilities, this is an important insight. Follow-up at the corporate level should focus on development over time within each unit and on sharing lessons learned, rather than ranking units against each other.

What should OEE be supplemented with?

OEE should be supplemented with loss analysis per cause, quality metrics, changeover times, energy consumption per unit, and flow metrics. OEE should not be abandoned, but supplemented, so that the picture becomes complete and difficult to manipulate.

Loss analysis per cause. The very foundation for making OEE actionable. Without it, OEE is a figure without an address.

Quality metrics. Scrap, rework, and quality deviations, preferably linked to the item and cause. Quality is not just a part of OEE, but a separate dimension worth tracking.

Changeover times. One of the most hidden areas of loss, and often one of the largest levers for increased capacity.

Energy consumption per unit. An increasingly important dimension for both costs and sustainability reporting. Energy per unit produced decreases as OEE rises, indicating the metrics are closely linked.

Flow metrics. Lead time, work in progress, and delivery precision. They capture things that OEE misses, especially the risk of achieving a high OEE at the expense of flow.

Together, they provide a picture that is hard to cheat. That is the point. When multiple metrics point in the same direction, the development can be trusted.

What is required for OEE to create value?

Five things determine whether OEE creates value: data quality, operator involvement, daily management, in-depth loss analysis, and a clear implementation process. Experience from more than 300 factories shows a clear pattern. Technology is rarely the bottleneck.

Data quality. If operators do not trust the figure, they will not use it in their improvement work. This requires a consistent definition of planned production time, correct handling of changeovers and planned stops, and clear stoppage cause codes that operators understand.

Operator involvement. Since 80% of stoppage causes are not machine-related, operators must be able to code them. This requires simple interfaces on the shop floor, not reports to be filled in the following day.

Daily management. OEE only becomes valuable when we use it in daily pulse meetings, shift handovers, and improvement meetings. A figure in a monthly report changes nothing.

In-depth loss analysis. Being able to break down OEE by line, item, shift, stoppage cause, and time is the difference between reporting and acting. Sibbhultsverken increased OEE by 19,4% and reduced technical stoppages by 73% by working systematically over the course of a year, not through a single effort.

Clear implementation process. Implementing an OEE system is not just about installing software. It is about defining what to measure, ensuring that signals from machines and operators are reliable, and building working methods that actually use the data. Without this, the system becomes just another information source that nobody looks at.

How does OEE go from a metric to improvement work?

OEE shifts from a metric to improvement work when it is used in a recurring cycle of measurement, analysis, prioritisation, action, and follow-up. The common thread in all of this is that OEE is a tool, not a goal. The goal is to reduce production losses and costs and to increase factory productivity. OEE is one of the best tools we have for seeing where the losses are, but the figure itself changes nothing.

That is why OEE belongs in the context of continuous improvement, which in lean is called kaizen. Measure, analyse, prioritise, act, follow up, and start over. OEE and loss analysis provide the factual basis. Improvement work provides the results. A factory that measures OEE but does not work systematically with improvements gets a nice dashboard without the car moving forward.

How does Good Solutions work with this?

The platform from Good Solutions is built to help OEE move from reporting to action. It is not a measurement tool that produces a number, but a tool to drive improvement work in daily operations. The purpose is concrete. To reduce production losses and costs by increasing factory productivity.

The platform combines machine connectivity, operator tools, dashboards, timeline, reports, quality management, maintenance, andon, energy, and operational implementation in one coherent tool. Operators, production managers, improvement leads, and management work from the same facts, each in a view that fits their role. Loss analysis ensures that the right efforts are prioritised. The energy module enables tracking of cost and sustainability on the same platform as production.

Just as important as the software is the operational implementation. A proven process, led by experts with production experience, ensures that data quality is maintained and working methods are established. A support organisation and a dedicated Customer Success Manager provide ongoing support to the customer.

The results speak for themselves.

Bostik improved OEE by 40% and shortened changeover times by 70%. Barilla Wasa increased net production by 15% while reducing CO₂ emissions by 28%. Kavli produced 5 000 tons more in one year, without extra shifts or more machines. In all cases, the results came from improvement work, with OEE as one of several metrics pointing the way.

Read more about how others have increased their factory productivity

FAQ

Is OEE sufficient as the sole KPI for production?
No. OEE is powerful but says nothing about why losses occur. It should be supplemented with loss analysis per cause, quality metrics, changeover times, energy consumption per unit, and flow metrics. Monitoring only OEE often leads the factory to optimise the figure rather than the operations.

Why can't we compare OEE between our factories?
OEE values depend on how each factory defines planned production time, ideal cycle time, and causes of stoppages. Two factories that calculate differently can have the same actual efficiency but different figures. Instead, compare each factory's progress over time, and use the differences to share lessons learned, not to rank them.

What does it mean that 80% of stoppages are not machine-related?
The majority of production losses are due to factors other than machine breakdowns. Material shortages, changeovers, waiting for orders, quality issues, planning, and staffing. This means that a factory that only invests in better machines misses the greatest potential, and that operators' coding of stoppage causes is crucial to resolving losses.

How do we avoid personnel optimising figures rather than operations?
By never letting OEE stand alone. When the figure is balanced against quality, changeover time, flow, and delivery precision, it becomes difficult to improve OEE without harming the whole. Equally important is using OEE as a basis for improvement, not as a grade for shifts or individuals.

How do we get started using OEE for improvement, not just reporting?
Start by ensuring data quality and providing operators with simple tools to code stoppages. Then bring OEE and loss analysis into daily management, i.e., morning meetings, shift handovers, and weekly improvement meetings. Prioritise a few losses at a time, act, and follow up. It is in this recurring rhythm that OEE goes from report to result.