Gopakumar (CONT’D FROM PAGE 20)
machines to monitor ongoing operations. The second is historical downtime data – listing the start and end times of previous planned and unplanned disruptions – along with explanations and descriptions of the problems that were encountered. Initial studies have shown that it takes about eight to twelve weeks’ worth of data collection for the machine learning mechanism to function optimally. Helios’ data sci- entists work with customers to input this information in or- der to get the predictive algorithm fully operational. Once the systems are in place, the predictive power of Helios’ machine learning gets more refined over time. “The corrugated industry has been booming, and new machines are going online every day,” said Gopakumar. “The rise of e-commerce has brought rapid growth and increased demand means it’s more important than ever to make sure equipment is operating as efficiently as possible. It’s critical to harness the power of information. Machine learning is revolutionizing how box plants under- stand, predict, and maintain their corrugated converting equipment, and Helios’ new failure prediction capabilities place it at the forefront of the industry. The sooner you adopt this powerful new set of tools for understanding and maintaining your equipment, the greater the rewards you’ll reap down the road.” Visit gohelios.us for more information.
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22 October 17, 2022
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