Use data to improve your OEE

Overall Equipment Effectiveness is calculated through the availability, performance, and quality of your production. Availability is affected when unplanned downtime occurs. By taking advantage of your historical data, predictive maintenance can help significantly improve availability, by creating warnings and transparency to when an asset can be proactively maintained. Performance can be affected if planning is not right, and can be improved by the help of better forecasting models. Quality is impacted by the process you use to manufacture and this can be significantly improved by monitoring the process in real-time with machine learning.

Reduce unplanned downtime

By utilizing the possibilities within predictive maintenance, you can improve equipment availability, equipment safety and reliability, while reducing mean-time-between-failure and mean-time-to-repair.

Reduce costs on discarded products

By having insight into equipment's remaining useful life and probability of function failure, you can discard less batches due to unplanned downtime when having products with narrow control limits.

Reduce costs on maintenance

With predictive maintenance, machines are maintained when necessary, reducing the total cost of maintenance both in time and materials.

Improve customer satisfaction

With a more reliable equipment, you can get predictable delivery to customers, and be able to deliver the given order right-on-time!

Automate quality inspection

By using image recognition and machine learning, you can automatically inspect whether your products are produced in the right quality. E.g. whether it is the right colour or the right angle.

Make efficient production planning

Plan your production based on historical data from your company. This way you get input on the quantity of raw material to order, the number of employees required, and utilization of your production lines.

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Detect breakdowns before they occur and improve uptime

Get health-status in real-time from your critical components, and handle possible breakdowns, before they delay your production. All manufacturing companies rely on assets such as bearings and electric motors or more complex assets such as processing machines and conveyor belts. They are key for your production to run and we can help keep them running. By using operational data from the asset, vibration sensors or edge computers with cameras we can help you monitor and get you started with predictive maintenance. Predictive maintenance is about being proactive about maintenance in your production and catching failures before they happen. This helps you digitize your maintenance and make it data driven which helps your maintenance crew better plan services as they have more information to work with. It does not matter where you are on this journey as we help companies in all stages including selecting the right sensors, how to store and process data, and creating machine learning models for predictive maintenance.

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Detect defective products earlier and reduce operational costs

Delivering your products in the right quality to your customers is one of the key drives to a successful business. By installing sensors or small cameras in the manufacturing processes, it is possible to have an automatic quality control that can help detect defective products due to unwanted process parameters. It can help your quality to monitor the manufacturing process in key areas to give you insight into key metrics such as temperatures or speed. This will make it easier for you to perform quality assurance and with the data logged you can start connecting good quality with the right set of parameters for production. Leading to better quality and lower operational costs in your production.

Start your Machine Learning Journey and optimize your business!

Do not worry we will do it together with you.

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    Campsite

    AI Camp

    Your Machine Learning journey starts by getting a feeling of Right Data over Big Data, Machine Learning and statistical concept. Throughout the AI Camp you will learn how to use these concepts to improve your business. Together we will select a potential pilot project and do a readiness assessment to ensure its feasibility in your company and on your data.
    Want to know more? Read more on our AI Camp page.

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    Astronaut helmet

    Pilot project

    A good way to learn as an organization is to try. A pilot project will give you the opportunity to see what machine learning can do in your company. Together we analyze data, investigate possible solutions, and create a tailored prototype for your company. We develop the solution in close collaboration with you, so we ensure that it fits your needs, people, and company. The end of a good pilot project is to test the model in production to see that it works. To read more about some of our pilot projects check out our customer cases.

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    Stable production

    Reliable Production

    A successful pilot project will naturally lead to an implementation and integration of the solution into your company. It is about take the prototype to production. You will now have a reliable, consistent, and efficient production where you can use data to make decisions. You are capable of operating the solution, and we leave you to do what you do best.

Here's what our customers are saying about us

“The AI Camp gave us insight into what Machine Learning is, and how it can create value. During the process we gained the knowlegde required to begin a machine learning project in our company.”

Jens Rishøj Skov

Affaldvarme Aarhus

Contact us today to start your journey!

+45 71 99 31 03
hi@neurospace.io

Møllevangs Allé 142, 8200 Aarhus N, Denmark

We are always happy to talk about your next project over a cup of coffee. It is, and always will be, free of charge!