By enhancing ServiceNow, assets in operation initiate necessary maintenance actions by recommending appropriate strategies and work orders.
supports the process by providing diagnosis and prognosis results as well as tactical recommendations.
benefits from proposed strategies when a failure of an asset in operation is imminent.
are customized by our service according to the diagnosed condition of the asset.
such as spare parts and service technicians are organized before a failure occurs.
are only assigned to a mission when it is really necessary through the support of our service.
Customized Recommendations for Assets:
The knowledge of the current state of components enables a rapid and tailor-made solution in the case of an failure.
Avoidance of Unplanned Downtimes:
The ability to predict the probability of failure is essential in order to efficiently schedule the timing of maintenance actions.
Understanding the asset’s fault condition and time of failure enables cost-optimized planning of maintenance tasks.
Efficient Part Utilization:
Our tools recommend a strategy to the service agent in order to optimally run a asset in the event of an imminent failure, such as:
Rapid and Smart Decision Making:
Based on the condition of the asset our AI Service recommends appropriate work orders with corresponding time and costs to the service agent.
Everything at a Glance:
Our service enables the condition monitoring of all assets in operation.
Avoidance of costly breaches of SLAs:
Overview of the availability of all assets in operations enables the identification and avoidance of future time periods of low
availability. This allows an intelligent control of maintenance procedures.
The support of common interfaces allows a simple integration of our AI Service to your (cloud) systems (e. g. ServiceNow, SAP S/4HANA, AWS, …)
Reasonable Data Reduction:
The smart reduction of measurement data, such as vibrations, current and temperature, to a health index enables a quick assessment of the component condition.
The intelligent mix of mathematical and data-based models (gray box modelling) enables a robust prediction of the remaining useful life even under varying load scenarios.
Appropriate Uncertainty Management:
Since forecasts are often subject to uncertainties, it is essential to deal correctly with the risks of predictions when planning maintenance actions.