April 09, 2024

Overall Equipment Effectiveness (OEE) and its optimization options

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Efficiency and productivity are essential factors for success in a competitive production environment — and the performance of machinery plays a decisive role in this. There is an unbeatable standard for evaluating and maximizing this: Overall Equipment Effectiveness (OEE) .
What is behind the powerful key figure?
How is it calculated and how can you increase it to bring your production processes to the next level?

What is Overall Equipment Effectiveness (OEE)?

Overall Equipment Effectiveness (OEE) is a key figure that measures the effectiveness of a production plant or machine. The metric provides information about the relationship between actual production time and ideal production time, taking into account losses due to downtime, scrap and lower machine output.

Why is the OEE key figure important for companies?

OEE is important because it helps companies to measure the effectiveness of their production facilities, identify bottlenecks and uncover potential for improvement. By optimizing OEE, companies can reduce their production costs, improve the quality of their products and increase their competitiveness.

How is OEE calculated?

Illustration of how OEE (Overall Equipment Effectiveness) is calculated
OEE is calculated by multiplying availability, power efficiency, and quality factor. The availability factor measures the operating time of the plant in relation to the planned operating time. The power efficiency compares the actual performance with the maximum possible output and the quality factor takes into account the proportion of defect-free products.

Availability factor: The availability factor measures the operating time of a plant in relation to the planned production time. Plant failures and set-up times impair this factor. A high percentage means continuous production.

Power factor: The power factor takes into account the effective running time compared to the maximum possible running time. Small stops and reduced speeds can affect performance. A high value indicates efficient use of the system.

Quality factor: The quality factor looks at the quality of the parts produced. Scrap and reworked parts reduce this value, and process errors and previous issues can affect quality. A high percentage, on the other hand, indicates error-free production.

An OEE of 100% means optimal plant utilization, maximum output and error-free production. The continuous monitoring and optimization of these dimensions is crucial for the efficiency of production processes.
Machine failures, material shortages and unforeseen disruptions can have a significant negative impact on OEE. By implementing a Predictive Maintenance plan, training teams in troubleshooting techniques, and using real-time monitoring systems such as our maintenance assistant, companies can reduce downtime and maximize production time. Improving OEE always requires an effective analysis of production processes to identify bottlenecks, downtime and quality problems.

The role of our AI-powered maintenance assistant in OEE improvement

aiomatic's AI-based software acts as a digital maintenance assistant and plays a decisive role in OEE improvement: Discrepancies in machine data (so-called “anomalies”) are identified before they lead to problems. Automated live monitoring provides precise insight into machines and their individual components so that the exact cause of the malfunction can be quickly identified. In addition, real-time alerts warn maintenance teams to abnormal behavior of their machines. The result: Maintenance work can be planned as needed, maintenance specialists can be deployed more efficiently and downtimes can be avoided. The availability of systems is increased and the OEE indicator is improved.

Success stories

Our customer Canyon Bicycles in Koblenz provides a good example of how the use of real-time monitoring systems can optimize the efficiency of production lines and thus also the OEE indicator. Costly failures in conveyor belt geared motors in 2014 prompted the company to look for innovative AI-based maintenance software. With our solution, Canyon also wanted to meet the enormous challenges of the shortage of skilled workers in maintenance. By implementing automated real-time monitoring instead of reactive or preventive maintenance of the gearmotors, Canyon wants to use both, financial and human resources more efficiently.
Engineer operating a machine
Thanks to aiomatic's AI-based software solution, Canyon is opening up new perspectives in dealing with the shortage of skilled workers in maintenance. The proactive response to maintenance requirements saves personnel resources and increases the efficiency of production lines. In our use case portfolio, you will find further success stories from our customers.
Overall equipment effectiveness (OEE) is a powerful indicator that helps companies to optimize their production processes and increase their competitiveness. By systematically analyzing downtimes, implementing Predictive Maintenance plans and using technologies such as digital maintenance assistants, companies can increase OEE and achieve more efficient production.

FAQ

The main factors that influence OEE are availability, power efficiency, and quality score.
Efficient, demand-based maintenance of systems is crucial for OEE improvement, as it helps to minimize downtime and maximize plant availability. Automated real-time monitoring of machines is necessary to be able to implement Predictive Maintenance measures.
The aiomatic software acts as a digital maintenance assistant. It can help companies improve OEE by identifying deviations (“abnormalities”) in machine data, pointing out potential faults at an early stage and thus simplifying the implementation of predictive maintenance plans.

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