A paper manufacturer got more out of its AI sensors with a simple administrative fix
Domtar improved the efficiency of its AI-assisted vibration sensors by implementing better administrative data management processes. By hiring reliability engineers to bridge the gap between raw sensor data and actionable insights, the company successfully reduced machine downtime.
Why it matters
This highlights the 'human-in-the-loop' necessity for industrial AI, showing that technology alone is insufficient without proper organizational workflows.
In Domtar's war room, workers can review the trove of data collected by its AI-assisted Waites sensors. Courtesy of Domtar Domtar's AI sensors help predict equipment issues to prevent costly machine failures. A Domtar reliability engineer kick-started an in-depth collaboration with the sensor company. Domtar's collaboration with Waites led to better data utilization for reducing machine downtime. Early in Matthew McLaughlin's tenure as a reliability engineer at Domtar, a motor at the paper manufacturer's Kingsport, Tennessee, mill failed. McLaughlin said his manager asked him to review the entire day's sensor data, collected from 450 sensors, and recommend a fix for the motor — but he knew that was an impossible task. "It would take 32 weeks for me to analyze all the data we were getting in one day," McLaughlin, who joined the company in 2024, told Business Insider.
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