Uptake, a provider of industrial intelligence software-as-a-service (SaaS), announced the availability of several major new capabilities in Uptake Fleet, its comprehensive predictive maintenance solution for the transportation industry. Leading the additions is an expanded Work Order Analytics capability that enables users to predict vehicle failures down to the component level for more detailed and accurate decision-making.
Also included in Uptake Fleet’s enhancements is support for a number of popular sensor analytics endpoints. Through the addition of new Application Programming Interface (API) and Electrical System Rating (ESR) connectors, fleets can leverage data from these endpoints to optimize their maintenance scheduling and further reduce vehicle breakdowns.
“From supply chain disruptions to parts shortages, the transportation industry has been hit hard in recent years. Maintenance and repair costs are up by over 18%,” said Kayne Grau, CEO, Uptake. “The new capabilities of Uptake Fleet are built with our customers’ evolving needs in mind to help increase vehicle uptime, save money, and maximize efficiency across an entire fleet.”
Uptake Fleet’s new analytics capabilities are now available to all users of the platform.
Work Order Analytics
With its new capabilities, Uptake Fleet’s Work Order Analytics allows users to move beyond time- and odometer-based measurements for their maintenance decision making. Using up to ten years of existing internal work order data, the tool provides component-level survival curves that indicate where a truck’s assets are in the equipment lifecycle, as well as aggregate and individual unit component analytics. It also filters by data fields, allowing users to assess both enterprise-level and vehicle-specific performance.
In its new configuration, Uptake Fleet users can set survival curve thresholds for specific vehicle components. When these thresholds are crossed, Uptake Fleet will create an alert that influences the truck’s risk score and flags the potential for component failure. As a result, users are able to plan more accurately for replacements and prepare stock accordingly.
