Nowadays, cutting-edge digital transformations and the need to adopt scalable solutions for any business makes the race to implement Industry 4.0 both competitive and fierce.

Capturing the right values and parameters related to your machines’ operations means measuring their performance to get more and have better production. Mckinsey speaks of OEE as the “true value” for Industry 4.0  –  the optimal parameter for monitoring status, energy, and costs.

What is OEE?

Overall Equipment Effectiveness is a measurement factor that allows you to detect the quantitative data of a company’s productivity, based on factors relating to availability, performance, and quality.

With the introduction of this value, it is possible to measure the production parameters and distinguish between efficiency and effectiveness.

It’s a fact efficiency refers to the quantitative measure of the actual technical production process, while effectiveness is concerned with the correspondence between the final product and the initial theoretical project. Overall Equipment effectiveness produces an average between these values ​​and takes this index as a result of the overall and final performance from the entire process.

It can therefore be used both as a benchmark, for comparing the performance of a given production asset with industry standards, or as a baseline for monitoring the progress of time and eliminating waste from a given production asset.

In identifying the percentage of planned production time that was actually used for production: an OEE score of 100%, for example, represents perfect production in an ideal scenario where only good parts were produced as fast as possible and without downtime.

For SMEs that have just started on their industry 4.0 path to monitor and improve production efficiency, it is normal to get a 40% OEE score. A very low index, to be sure, but one which, in most cases, can be easily improved through simple measures (for example, by monitoring the causes of downtime).

Why OEE in industry 4.0 ?

Today’s digital factory is constantly evolving, constantly keeping up with the changes that new technologies bring to the market. In fact, manufacturers can choose different solutions ranging from data analysis to artificial intelligence algorithms, applicable in all sectors. Industry 4.0 offers production efficiency and monitoring techniques and methodologies that allow you to improve the performance of your machinery and control all parameters in real-time, strategically. However, adequate measurements are needed to “capture its true value”.

OEE is therefore proposed as an indispensable parameter for effective monitoring of production. It is essential to define implementation strategies that are based on the actual study of data extracted from machine operation. In this way, it is possible to create scalable and optimized production with respect to the actual behavior of your machinery.

Overall Equipment Effectiveness allows companies to monitor their production efficiency and determine whether the overall performance is adequate or it is compromised with respect to availability, performance, and quality. In this way, it is easy to intervene correctly with adequate measures and quickly correct errors as you optimize the overall machinery efficiency and enable it for industry 4.0.

The Zerynth Industrial IoT APP

There is no single way to calculate the OEE, but this varies according to the needs and software tools used by a company.

The Zerynth IoT Platform offers Industrial APPs, tools that can monitor the actual value of the production OEE, such as the Machine Insights APP (Fig 1).

Let’s look at an example of a dashboard for a machine in the production phase. The upper right section show the calculation of the OEE, understood as the product between Availability × Performance × Quality.

Figure 1. APP Machine Insights Dashboard.


The availability measure indicates, as a percentage, how much the machine was actually available compared with the planned production time. It is the first component of the OEE and is calculated as the ratio between run time and planned production time. We consider, in fact, a planned production time that grows over time depending on the moment in which we are looking at the dashboard during the operation of the machinery, and a run time such as the time in which the machine has actually been switched on, productive or in standby.

Availability = Run Time / Planned Production Time


The performance measure is calculated as (Ideal Cycle Time × Total Piece Count) / Run Time. In this case, the ideal cycle time is the minimum cycle time that is reached by the machine in the last month during the production of good pieces, but if desired, it can be set as fixed if the ideal production time for a piece or product is known. Performance tells us how fast the machine is going: 100% performance means that the machine is running at its maximum capacity and, therefore, the real cycle time is equal to the ideal cycle time. Performance = (Ideal Cycle Time × Total Piece Count) / Run Time


Finally, quality tells us how many good pieces we have produced compared to the total number of pieces. It will then be equal to Quality = Good Count / Total Count

Fig 2. Production Effectiveness – Detail of Dashboard

Once these three parameters have been defined (Fig 2), we can then calculate the OEE as a product of the three components. It is important to remember that the OEE is a product of percentages and that therefore a small deviation from the ideal in each of the percentages is enough to make the OEE drop very quickly.

To understand what the inefficiencies are and why, it is not enough to calculate only the OEE, but it is also necessary to study and analyze the three measures, so one knows where it is necessary to intervene.

If you are interested in learning more about this topic and understanding how Zerynth’s Industrial IoT APPs work and how they can help your business optimize production and enable industry 4.0 for you, watch our on-demand webinar or contact us for a demo!

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About the Author: Daniele Mazzei

Daniele Mazzei
Daniele is the CPO and co-founder of Zerynth. His strong interest in the interaction between people and intelligent objects led him to co-found Zerynth and to design connected devices and Industrial IoT applications. After earning a PhD in Bioengineering and Biomedical Engineering, he is now an Associate Professor at the Department of Computer Science at the University of Pisa.

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