A paper manufacturer got more out of its AI sensors with a simple administrative fix

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.

As manufacturing companies adopt promising new AI technologies, they often face the challenge of learning to do things differently, like implementing new tools and processes to get the most out of their technological investments.

In the years prior to McLaughlin's arrival, Domtar implemented AI-assisted sensors from Waites Sensor Technologies to continuously monitor the vibrations coming from equipment — which signal a piece of machinery's health — and provide that data to prevent breakdowns.

Domtar, armed with more data than it had before, needed a new way to manage it all so it could make the most of the Waites sensors' capabilities.

That's where reliability engineers like McLaughlin come in: They are tasked with improving a company's technology and digital resources through data analytics, risk management, engineering expertise, and communication skills.

As AI systems are increasingly integrated into workflows, reliability engineers like McLaughlin must develop new methods that bring humans and technology together.

"This was the line-in-the-sand moment where we needed to do something different," said McLaughlin.

He added, "It couldn't be treated like a legacy predictive maintenance program; it needed to be treated like the advanced system that it is." Drilling into the data on weekly partner calls To move away from manual analysis and make better use of Waites' machine-learning technology, McLaughlin said he decided to lean on the tech company for more detailed and consistent support.