AI-Powered Fleet Management Delivered

Written By: Jen Neville
November 27, 2018

AI Fleet Management

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Industrial AI and IoT software leader, Uptake, and fleet telematics provider, Geotab, have joined forces to enable fleets to predict failures before they occur.

Historically, fleet managers used telematics to understand basic facts about their fleets, such as location and drive time. Now, fleets have access to AI-powered predictive analytics. By combining Geotab hardware, which captures data from approximately 100 different truck sensors, with Uptake’s best-in-class AI software, this solution helps provide fleets with precise insights into which components will fail and when.

“For the first time, fleets can go beyond basic monitoring to proactively predict and prevent breakdowns,” stated Hoyoung Pak, head of transportation and logistics at Uptake. “Pairing Geotab’s capabilities to effectively access and rapidly transmit sensor data with Uptake’s ability to turn massive amounts of data into actionable insights leads to undisputed outcomes. Those can include reduced fuel cost, decreased unplanned maintenance and increased fleet productivity. The market has been asking for this for a long time.”

“Uptake’s ability to predict equipment failures with an incredibly high level of precision, even down to the sub-component level, has not been possible before in this industry,” added Scott Sutarik, Associate VP of Commercial Vehicle Solutions at Geotab. “By aligning with Uptake, Geotab customers can access a new level of detailed, actionable data to help prevent unplanned downtime, improve their bottom line and stay competitive.”

Geotab’s devices use a patented efficiency algorithm that enables them to rapidly transmit data from sensors on a truck into the cloud. Uptake analyzes this massive amount of data in its proprietary AI engines, applies contextual information like weather and historical data, and delivers data science-backed insights that keep the entire fleet running at peak efficiency.

The software prevents data overload by only presenting alerts when trucks are at risk of breaking down. For example, it can identify voltage fluctuations that indicate a pending alternator failure, or that the diesel particulate filter needs to be cleaned sooner than expected. This enables fleets to optimize their maintenance cadence and maximize vehicle availability while ensuring they are neither under- nor over-maintaining their fleet.

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