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Our software is well known from
Machine downtimes: This is how expensive they really are
Today's advanced production machines churn out massive amounts of data, posing monitoring challenges for engineers. This often results in unexpected and costly breakdowns of machine parts, sometimes stopping entire production lines. According to the study "The true cost of downtime (2022)", such failures have serious consequences:
On average only 60 % machine availability
≈ 3.3 million lost working hours per year due to unexpected downtime
A machine breakdown costs companies an average of 380,000 €
Advantages of Predictive Maintenance
Predictive maintenance involves continuously monitoring machines to detect and prevent potential failures early on. Unlike traditional reactive maintenance strategies, this preventive approach allows for timely planning and execution of maintenance measures. Real-time analysis of machine data enables companies to implement cost-effective maintenance strategies, reducing downtime and enhancing system efficiency. By prioritizing predictive maintenance, companies can optimize their operations, minimize costs, and maximize productivity.
20 % less machine downtime¹
17 % reduction in maintenance costs²
Ø 10 % increase in turnover³
Predictive Maintenance with AI powered software
Our software solution facilitates the implementation of predictive maintenance measures by serving as a digital maintenance assistant. It seamlessly integrates the expertise of engineers into a powerful AI system, allowing for precise monitoring of machines. Through this innovative approach, predictive maintenance becomes achievable, as potential issues are detected and displayed on a dashboard before they lead to breakdowns.
One solution for all industries & systems
Discover our innovative software solution for predictive maintenance - specially developed for large amounts of data from highly complex machinery in the energy sector, automotive supply, chemical process industry, pet food, packaging industry, tool manufacturing and plastics processing. In our use case portfolio you will find success stories of existing customers from a wide range of industries.
One solution for all industries & systems
Discover our innovative software solution for predictive maintenance - specially developed for large amounts of data from highly complex machinery in the energy sector, automotive supply, chemical process industry, pet food, packaging industry, tool manufacturing and plastics processing. In our use case portfolio you will find success stories of existing customers from a wide range of industries.
5 steps to Predictive Maintenance with ai-omatic
1. Define goals & machines
Goals are e.g. the minimization of breakdowns and maintenance costs as well as the implementation of predictive maintenance measures. You select relevant machines based on our questions.
2. Establish prerequisites
Seamless data integration is important for analyzing your machine data and determining the normal state. Our guidance and plug-and-play hardware will help.
4. Configure software
Your machine is displayed in our digital assistant. We support you with workshops and detailed instructions for the configuration.
5. Live
monitoring
Our AI models are now continuously trained with new data. The software recognizes anomalies in real time and evaluates the condition of the machines from 0 - 100 %.
Four good reasons for the ai-omatic solution
These companies trust our solution
The future of maintenance faces challenges with skilled labor shortages. However, with ai-omatic's AI-based solution, the outlook is much brighter. The team can now allocate their focus to other tasks, as ai-omatic handles monitoring and provides early warning alerts.
Andreas Weber, Canyon Bicycles
1: Zoe Talent Solutions (2023): Reducing downtime with predictive maintenance
2: Duscheck, Frank (2021): Chancen und Herausforderungen von Predictive Maintenance in der Industrie