Predictive Maintenance for small and medium-sized enterprises
KMO provides free recourses to help you learn more about predictive maintenance and our company.
How Does It Work?
The main goal is to predict the technical condition of the equipment by developing and using predictive models, for example, for turbo generator.

Regardless of the place of manufacturer and turbo generator specialization, it works on the same physical principles. So we investigate them and develop relevant models.
Web based access
Easy to use & program
Fully integrated with ERP & MES systems
Simple GUI
Asset tracking
Simple to understand metricsSimple GUI
Articles
How can predictive maintenance reduce costs?
How AI and Data Scientists can help your company?
3 Important Questions to Answer When you Implement Predictive Maintenance
How can predictive maintenance reduce costs?
How can predictive maintenance reduce costs?
It is easy to see how predictive maintenance platforms can help the company with making operations more efficient, but what about the economic value of implementation? Maintenance is a key area where company can find major cost savings and production value around the world. We've studied various reports and market research shows that with implementation you can:
The implementation gives company an opportunity to save money on maintenance, downtime and at the same time to extend the life of the equipment. It seems that enterprises finally realize that there are many ways to capitalize on the digital revolution.

Our solution gives an opportunity to power up decision-making process with big data analytics. Nowadays almost everything leaves digital trace and it Is quite unreasonable not to use these traces to make better predictions and analyses. To understand all the benefits more clearly you can visit Benefits page.
14%
safety, health, environment, and quality risks reduction
20%
lifetime extension of an aging asset
12%
cost reduction
9%
uptime improvement
How AI and Data Scientists can help your company?
How AI and Data Scientists can help your company?
First, there was "preventive maintenance" - the premature change of spare parts. It is still the dominating strategy in most industries when it comes to maintenance. But, as people say, times are changing and the need for improvement is unarguable. "Predictive maintenance" is the next logical step where all the assets can be used for a longer time with the help of accurate prediction. The question is – how?
AI and Data Science. These technologies can give you realistic and reliable estimation of the health status of your assets. Imagine that massive amounts of data is collected and translated into real insights. It can help the company with aggregation of the data that leads directly to clear interpretations. Simply said, AI and Data Science can actually support employees in drawing conclusions from historical data.
By using AI and Data Science, we collect knowledge and experience and create resource base for further predictions. If you want to revolutionize your business, read about our solution. All the available information:
White Paper
FAQ
Documentation
3 Important Questions to Answer When you Implement Predictive Maintenance
3 Important Questions to Answer When you Implement Predictive Maintenance
#1. What exactly do you want to predict?

It is extremely important to understand the scope of the implementation. Of course, in the modern digitalized world it would be good if everything was automated and predicted, but there is always a starting point.
#2. What data do you need? What is the quality of this data?

In the next step you should see what historical data you have. It's essential to assess the usability and quality of this data.
#3. How to interpret the data collected?

Processes relevant data will show you actionable insights, projections, and plans. The system will be able to predict when the next failure is likely to occur and will propose the ideal time to perform specific tasks.
Every single one of this questions can be easily answered when you decide to implement our solution. To know more about the benefits and specifics read our White Paper and Benefits.
How to drive innovation in capital-intensive companies?
What are the challenges in implementation period?
How to drive innovation in capital-intensive companies?
How to drive innovation in capital-intensive companies?
Previously the main driver for innovation used to be profitability. But lately the focus has changed. Now sustainability is in the center of attention. It can be explained not only by society's need for conscious decisions-making but also by new "green" business approaches.
Internally within the company, people are willing to speed up research and development as well as related processes. Innovative solutions such as predictive maintenance platforms allow you no only to reduce ecological footprint which appears due to mistakes or late repairs, but also to increase profits. The increase can be easily explained by the fact that error fines can completely cut.

Our solution is perfect for everyone who understands that it is important not only to raise profits but to be ethical when it comes to our planet and ecology.
What are the challenges in implementation period?
What are the challenges in implementation period?
Challenge #1. Being unaware how to do predictive maintenance

New technology requires not only financial investments, but also the investments of time. Data Scientists and analysts need to go through the steps of learning how the platform works. To make this process easier, our solution comes with 24/7 online support and the help of professional experts who are willing to explain and show the platform's functions during deployment.
Challenge #2. Having a lack of data to create proper predictions

Because of the fact that predictive maintenance platforms rely on machine learning algorithms, the success of the model mostly depends on the way how data is logged. Our experts help the company to go through this process with different options making the transition easy.
Challenge #3. Having poor data quality

Usually poor quality of data means poor decision-making process. It's important to assess and test the usability of data collected and if its quality is satisfactory. Our solution allows you to do the assessment in the fastest way possible to be able to do the best predictions. To learn more about the benefits, click here.
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