COSMO Predictive Maintenance | Predictive Maintenance Software
Data-driven predictive forecasting to avoid downtime
My Challenges
The day started off so well, and then one of the machines suddenly breaks down. This can have far-reaching consequences, as any machine or system failure can lead to costly downtime and repairs. Unexpected vehicle breakdowns are also frustrating, and you need to react quickly: You need a replacement vehicle, or you need to reroute on the fly.
All of this can result in unpredictable disruptions to customer service, and orders for spare parts and materials may be delayed. Wouldn't it be nice to be prepared for such situations? Or better yet, to prevent them from happening in the first place?
That's where well-founded forecasts come in.
How I will be supported
With predictive maintenance software, you could identify patterns early on in the future that indicate imminent failures. Sensors and algorithms continuously collect and analyze data for predictive maintenance.
By evaluating historical data, the condition of machines and systems can be monitored in real time. If a failure is imminent, you can react quickly and thus prevent major damage.
My benefit
Predictive maintenance is a reactive maintenance strategy that also enables predictive planning of maintenance work and spare parts deliveries. By predicting failures, maintenance measures can be planned and carried out in a timely manner before an unexpected failure occurs. This avoids expensive downtime and reduces the cost of repairs and spare parts.
Data science enables intelligent decisions based on real information. Your company has an intelligent early warning system that allows you to reduce maintenance costs.
Gain various product insights
Differentiation of reactive, plannend, predictive and prescriptive Maintenance
Early warning system
- Failures can be predicted
- Maintenance measures and repairs can be planned
- Predictable use of replacement vehicles
Prediction of disturbances
Data-driven intelligence
- Identification of important variables
- Data-driven learning
- Intelligent decisions in maintenance
Intelligent decisions based on as-built data
Economy
- More cost-effective than reactive or planned maintenance
- Significantly more efficient maintenance management is possible
- Optimum machine and vehicle availability
Reducing maintenance costs through data science
System Requirements
This app requires Microsoft Azure.
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