Attendees at the Product Innovation Congress in Boston last week got a chance to hear about the IoT journey of STERIS, a global leader in infection prevention, contamination control, surgical and critical care technologies. The company co-presented the session with ThingWorx called “Gaining Value through IoT, Data and Proactive Service.”
John Rogers, manager of the ProConnect IoT group, leads a team that is responsible for the development of real-time remote monitoring solutions in manufactured sterilization equipment. The company’s ProConnect IoT Application allows for real-time remote monitoring of STERIS equipment, constituting thousands of devices at over 300 hospitals in the U.S. The ProConnect IoT Application provides a custom alert notification indicating equipment failure and required maintenance. The team develops web and mobile app interfaces for both internal and external customers to take advantage of the monitoring technology.
Rogers, along with ProConnect IoT Lead Architect Will Valencak and PTC’s Mike Fallon, led the engaging session. Valencak is currently leading the development of ProConnect Mashups, a Thingworx-powered IoT platform for collecting and analyzing data from medical device washers and sterilizers installed in the field.
While some companies today struggle with a process to collect data, STERIS put itself ahead of the curve by collecting product operation summary data starting in 2007 and moving to machine-level sensory data in 2011. The massive amount of data held by the company was used for reporting on success metrics for years, but they were unable to analyze and make the data actionable at that time.
Today, STERIS is moving from a reactive to a proactive approach that is enabled by the ThingWorx IoT platform and ThingWorx Machine Learning for advanced and predictive analytics, which provides deeper insight into the company’s data.
Valencak presented an example of how the company is using ThingWorx Machine Learning to predict failures. The goal of the project is to predict the time or number of cycles until the next door switch failure during a PREVAC cycle for a specific sterilizer model. The predictive analytics take into account historical cycle data for the model, along with detailed instrument readings. Once the data is loaded, ThingWorx Machine Learning quickly groups the data and provides a visual insight into predictive behavior over time and as compared to overall averages.
As for what’s next for STERIS, Rogers indicated that the company will continue to identify and investigate various analytics platforms in the industry, as well as to find Data Science talent to assist in their Big Data efforts. The company is also embarking on a cross-business-unit steering committee in addition to discussions at the executive management level to establish and solidify their IoT strategy.
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