The three factors availability, performance, and quality as well as OEE are expressed as percentages between 0 and 100%. 

Example: A system has an availability of 90%, a performance of 95%, and a quality of 98%. In this case, the OEE value of the system would be 83.8%.

Due to multiplication, OEE depends on all three factors. This means: Even if, for instance, availability and performance are good, a poor quality factor pulls the OEE down overall. Therefore, it makes sense to look at the factors individually to get closer to the root causes of the problems.

Step 1: Calculate Availability Factor

Your machine availability provides insight into how long the system has actually produced while it was scheduled for production. For the calculation, you therefore need the planned operation time as well as an accurate overview of your downtimes. The time during which your systems are actually running is the operational time. Planned downtimes (e.g., setup, maintenance) and unplanned downtimes (e.g., sudden machine failures requiring unplanned repairs) add up to availability losses, thus reducing the system's availability. 

Which downtimes should be considered in the OEE calculation?

Availability depends on the number and length of downtimes. There are different views on which downtimes should be considered in calculating availability.

Why is a system down? In many cases, it may not be scheduled for production. The system is ready and could produce, but there is no production order because, for example, there are not enough orders or no workers are scheduled for the system. These reasons are outside the responsibility of the operational staff and should therefore be excluded from the OEE calculation, as otherwise the real problems are concealed.

In addition, there are planned and unplanned downtimes. Planned downtimes are part of the production process, such as setup processes or material changes. In some OEE calculations, these planned downtimes are not considered. We strongly advise against this: Often, a detailed look and comparison of planned downtimes is worthwhile, even if they cannot be prevented. Often, different setup times occur for the same products, which can indicate optimization potential.

Unplanned downtimes, on the other hand, undoubtedly belong to the downtimes that reduce availability. These include unexpected machine failures due to equipment damage, unplanned maintenance on machine components to prevent damage, material jams, or sudden worker absences due to illness. However, brief absences such as bathroom breaks and meal breaks are also unplanned downtimes.

Here you can read about how ENLYZE automatically detects and distinguishes downtimes.

How can you increase machine availability?

Get an overview of the most common downtimes. Ideally, using a Pareto analysis of your downtime causes. Initially, focus on one of the most frequent reasons; this is likely where the greatest potential lies. Develop improvement measures and monitor progress. 

Other factors that influence machine availability include:

•     Frequency of disturbances and failures 

•     Duration of maintenance and repair work 

•     Quality of maintenance and repair work 

•     Availability of spare parts 

•     Qualifications and experience of personnel

•     Environmental conditions

  
  

In a second step, you can try to optimize these factors by, for example, ensuring shorter waiting times for spare parts.

Step 2: Calculate Performance Factor

Calculating the performance factor is somewhat more complicated. Performance indicates how much product is produced on the machine in relation to how much could be produced. Thus, the performance factor indicates how far the production is from the maximum manufacturing speed and shows performance losses. The maximum manufacturing speed is used as a performance reference. It is important to associate the performance reference with the product and not to measure all products on the same system against the same performance reference. This is absolutely necessary from our perspective since not all products can be manufactured at the same speed on the system. A detailed explanation of the performance factor can be found here. 

How to Set the Maximum Throughput

In traditional manual OEE measurement, the quantity of produced material per hour is often weighed, recorded, and compared at regular intervals. In practice, the manufacturer’s nameplate capacities are often used for the performance reference; however, these are not product-specific.

At ENLYZE, we use the throughput from machine data for performance determination. The PLC continuously collects speed data, from which the throughput (integral of speed over time) can be calculated. We use this to continuously display the current performance. Additionally, the product-specific performance reference is determined based on this data. This happens automatically and is based on the historically recorded production orders of the product. Our algorithm seeks the maximum stable throughput achieved for each product – we refer to this as MDS (Maximum Demonstrated Speed), which is then documented as the performance reference.

We recommend setting the maximum throughput not based on nameplate capacities but through accurately verified product-specific maximum manufacturing speeds. Only in this way will the real performance losses be reflected in the OEE metric, and only this will allow the greatest levers for improvement to be identified. 

How can you increase performance?

In terms of performance losses, significant production volumes are often lost. However, these losses are often not visible today due to poor data foundations. Once performance is continuously monitored, however, the unpleasant surprise emerges.

The good news is that you can achieve significant performance improvements through simple standardization and communication of the best possible setting parameters for the respective product. 

At ENLYZE, this is possible through our digital setting data sheet in combination with our operator co-pilot.

Step 3: Calculate Quality Factor

Now you still need the third factor for calculating your overall equipment effectiveness: the quality factor. The quality of a product indicates how much end product (= good quantity) is usable, i.e., not scrap. The quality factor makes transparent how much of the produced product can be further processed or sold to the customer. For this purpose, the produced good quantity is compared to the total quantity. 

The total quantity is the amount of raw material that has been processed at the system. It can be easily calculated based on the machine data. Alternatively, it can be weighed manually, either based on the amount of raw material or based on the sum of good quantity (amount of end product) + scrap.

More information on the topic of quality and scrap can be found in this article .

How can you reduce scrap?

Here, we recommend a similar approach as with downtimes: every scrap should be recorded with a reason. A Pareto analysis helps you identify the top reasons for scrap. Here, too, you can achieve continuous improvement once you focus on the top reasons and monitor them.

Step 4: Calculate OEE (Overall Equipment Effectiveness)

Now you have determined the three factors machine availability, performance, and quality of your production. Would you like to calculate the OEE manually in Excel using a formula? Then download our free OEE Excel template here. 

This is time-consuming, no question about it, and ties up resources that you would surely prefer to use elsewhere. Have you ever considered an OEE software that automatically calculates the OEE from your machine data? 

ENLYZE was specifically developed for continuous manufacturing. Process data is automated and linked with orders and provides you with KPIs such as OEE accurately and in real time. 

What is a good OEE value? Why this question is wrong, and how you evaluate your result

Are you wondering what overall equipment effectiveness you should actually aim for? In fact, this is not so easy to answer. A good OEE is considered a value of ~80 percent, which also often holds true for our customers. An average OEE is considered to be a value of 60 to 70 percent. However, OEE is not an absolute number, which is unfortunately often misunderstood in practice, especially when the metric is manipulated to meet unrealistic targets: In such cases, the reference values for performance and availability are chosen too low, and an OEE value of 100 percent or more is achieved. 

Don't make the mistake of comparing the OEE of different systems or even companies! Manufacturing processes and systems in the industry are simply too different, and there are also different approaches to OEE calculation. Such comparisons make little sense for OEE analysis. The calculated OEE derives from your manufacturing and primarily serves as a benchmark for yourself, with the goal of continuously increasing it over time. 

Measured against carefully chosen indicators, you obtain a reliable index for productivity in manufacturing. Therefore, the OEE evaluation helps you reliably identify efficiency losses or other sources of loss, and you can adjust the right levers to optimize your processes.

What matters is not the absolute level of overall equipment effectiveness (OEE) but its development over time. Whether the OEE improves can be seen from the changes over time and can be measured in percentage points. That is, as a goal, no OEE target value should be set, such as 'We want to achieve 85% OEE!', but rather we want to increase the OEE by 5 percentage points compared to today over the next 6 months.

Is an OEE of 100% possible?

Short answer: Yes, but unlikely. And many companies “cheat” when calculating such an OEE.

At ENLYZE, we place more value on calculating the correct OEE and thereby revealing realistic improvement potentials. Such integration of OEE into your production allows identifying real problems in manufacturing and continuously steering productivity towards a site-specific maximum.

What does OEE bring you for Lean Manufacturing?

Lean Manufacturing and overall equipment effectiveness (OEE) are closely related. Lean Manufacturing is a methodology for increasing efficiency in production. By applying Lean principles, companies can improve their OEE by optimizing processes, reducing waste, and increasing equipment efficiency. A higher OEE value indicates that production facilities are being utilized more effectively, leading to increases in productivity and profitability.

Moreover, OEE creates transparency in Lean Manufacturing by prioritizing the largest problems in manufacturing and, consequently, the most significant potentials for improvements. Companies can use OEE to determine where most machine downtime occurs and what causes it. This way, measures can be taken to reduce the failure rate and thus increase manufacturing efficiency.

The Six Big Losses in Manufacturing

In manufacturing, there are six big losses (Engl. Six Big Losses) that negatively impact OEE. To improve overall equipment effectiveness, it is essential to systematically minimize these losses. They can be very well categorized according to the three factors of OEE:

Availability:

1. Planned downtimes: Process-related downtimes, such as setup or reconfiguration, scheduled maintenance, etc.

2. Unplanned downtimes: Equipment failures, jams, material breaks, etc. 

Performance:

3. Short stops: Short stops (a few seconds) are not relevant to availability in continuous manufacturing, as they do not occur due to the inertia of the processes, and therefore downtimes are always charged to availability losses. 

4. Reduced speeds: The system runs slower than it should – typical performance drop.

Quality:

5. Startup scrap: Process-related scrap is often produced when starting up systems until the system really warms up.

6. Running scrap: For example, unexpected process errors can cause damage to the product.

Would you like to delve deeper into the topic? Learn here how to find the biggest optimization levers on the shop floor using the Six Big Losses. 

How to implement OEE in manufacturing

Calculating Overall Equipment Effectiveness (OEE) is a complex task that presents numerous challenges. First, the data needed for the calculation is collected and processed. This is often time-consuming and tedious, especially if you do it manually, as data often comes in different formats and systems.

Moreover, the reliability of the OEE, an important KPI for production, depends on how detailed the data is recorded. How do you ensure that the actual reasons for downtimes (stoppages) are always accurately provided? Why is the system currently running slower than usual, and can the worker identify the correct data for this?

To accurately determine the OEE, you should keep in mind several points. What those are, you will learn in the following section.