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In order to uncover potential in manufacturing, producing companies need insight into the quality and effectiveness of their systems. The OEE metric is suitable for this, which is calculated from availability, product quality, and performance. Today, OEE is used in many companies to determine the effectiveness of production lines.
However, the metric alone has little significance if it is not made adequately visible and integrated into production management.
In this article, we will show you some visualization options for the OEE metric and its components.
The dashboards presented are suitable for both continuous monitoring and retrospective summaries of the effectiveness of your production lines.
More on the topic: 5 dashboards that no production should be without
Which tool is suitable for creating OEE dashboards?
"A well-designed dashboard allows a manager to answer a question in eight seconds or less." - Stephen Few, author of the books 'Information Dashboard Design' and 'Show Me the Numbers'
This is more true than ever in the digital age. Therefore, it is important to use the right software with high functionality and ease of use when creating a dashboard.
1. Excel
Ideal for beginners with limited technical resources. It is widespread, and hardly any company operates without Excel. However, it requires significant manual effort to maintain current data and calculate OEE effectively.
Here you can get a free Excel template for OEE calculation.
2. PowerBI
A robust solution for those who have already invested in Microsoft's ecosystem. It offers sophisticated visualization options and is compatible with ENLYZE, allowing users to access real-time data quickly and seamlessly.
3. Grafana
Best suited for advanced users who already have extensive data sources and want to conduct complex time series analyses. Grafana supports dynamic dashboards that are ideal for displaying OEE data in real-time, especially in conjunction with ENLYZE.
4. ENLYZE
Those who want to remove the tedious and time-consuming OEE calculation from the workflow and also want access to easy-to-understand KPI visualizations will find ENLYZE to be the best option.
Today, at least four different MDE solutions need to be integrated to utilize machine data. This integration takes time and money.
We solve this problem at ENLYZE by bundling the four products into one product. This way, you don't have a lengthy IT project with high costs, but a product that works from day one.
What should OEE dashboards look like?
After selecting the right software, the question remains how to visualize OEE in the most understandable way. Therefore, we present four dashboards with the most important KPIs and visualization methods below.
1. OEE Overview
(Example: ENLYZE App)
The OEE alone is already a good source of information about the status of production. However, the three components performance, quality, and availability, along with the time context, are relevant for a detailed assessment of productivity and deriving recommendations for action.
The four most important KPIs that this dashboard should display are
The current OEE, as well as the underlying factors
Availability
Performance
Quality
For a status inquiry, it is sufficient to display these four values in simple text boxes. For a detailed representation of OEE development, line charts are useful to show the OEE for individual locations or systems over a specific period to see whether a drastic change in OEE stems from the entire production or only from a part.
If you find the above dashboard too cluttered and only need a snapshot of the OEE, we recommend the following OEE overview, which presents the metric and its underlying factors per system and with selectable calculation period. This dashboard is ideal for getting a quick overview of the status of your production.
(Example: ENLYZE PowerBI)
2. Downtime
(Example: ENLYZE App)
Downtime affects the availability factor of the OEE. Therefore, this dashboard should illustrate,
why availability is particularly high or low
whether any fluctuations in the factor can be attributed to all or only a few machines.
Unlike monitoring dashboards, which display downtimes on timelines or focus on the current status of the system, this one is intended to visualize what the potential OEE fluctuations are due to.
For this, a summary of downtimes is needed first. This can be well done using a pie chart that proportionately depicts production time and downtime.
For the absolute downtimes, the total duration of downtimes in the selected period and the number of downtimes serve as status indicators. This creates an intuitive overview of the frequency and severity or duration of downtimes.
Since the causes of recorded downtimes are particularly important for effective evaluation, it is worthwhile to also display the number of downtimes without a recorded reason. If the availability factor is very low, the primary question is which reason for downtime caused the most time losses.
To answer this, a bar chart with reasons for downtime on the x-axis and the total duration of downtimes with that reason on the y-axis is practical and provides insights into possible changes in the availability factor in combination with a list of the five longest downtimes.
3. Performance
(Example: ENLYZE Grafana)
To summarize the performance of your own production understandably, a dashboard that shows both the current performance of all machines and the time course of performance at each machine is advisable. This way, action needs can be easily assessed and the machines that perform best or worst can be identified.
To further determine the causes of performance losses, it is also practical to list the products with the lowest performance factor. It is important to also display the system where the product was manufactured since the same product can be manufactured at different systems at different speeds.
Finally, we want to quantify the improvement potential by listing the total time lost due to inefficient production. This is calculated from the difference between the actual production speed and the Maximum Demonstrated Speed (MDS), that is, the maximum possible production speed per product and system.
4. Quality
(Example: ENLYZE Grafana)
For a dashboard regarding product quality, a similar structure can be chosen as for the performance factor. The quality factor significantly depends on the amount of scrap, the total value of which over a freely selectable period is interesting in order to get an overview of the extent of quality losses.
The ranking of systems by amount of scrap in a bar chart serves to identify the system with the highest need for action, and displaying quality per system over time helps in further analysis and validation of the insights from the other visualizations.
As in the other dashboards, it is important to visualize a combination of absolute values (scrap in kilograms) and relative values (quality factor in percent) to act accordingly based on your own production goals.
If you want to reduce costs in the form of scrap, you focus more on the bar chart with the absolute values, and if the focus is on high quality across the entire production, you should rather work with the relative values. Ultimately, however, only the interplay of all values can accurately represent reality.
Templates for OEE Dashboards
To make it easier for you to create your own OEE dashboards, we have put together some templates for you:
With our OEE calculator, you can quickly and easily determine the OEE for your systems and identify potentials for your production.
There is also an OEE calculator template for PowerBI, which allows similar calculation and visualization of the OEE as our Excel template.
Since Grafana is not primarily used for OEE dashboards, we do not offer a template here, but recommend working with the ENLYZE Grafana server, which provides you with the use of our OEE software and professional support for setting up your own dashboards.
Become an OEE expert with our OEE series
Here you will learn how to calculate and improve OEE in the long term.
Performance losses: Why doesn’t the machine always operate at maximum speed?
Recording OEE manually - it’s like playing darts in the dark
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