Using Predictive Analytics to Reduce Equipment Downtime, Improve Product Quality and Decrease Costs in the Age of the Industrial Internet of Things

28th June 2017
Location: Online

Using Predictive Analytics IIoT

New Industrial Internet of Things (IIoT) solutions are helping manufacturers improve product quality, increase productivity, decrease costs, and make smarter business decisions. Already, manufacturing facilities are becoming "smart factories," where vast quantities of sensor data are continuously analyzed to increase productivity and efficiency. For example, IIoT sensors might monitor the temperature of a key piece of equipment, if the temperature begins to rise a predictive maintenance solution can take actions to avoid equipment or product damage and notify staff of the problem.

A major challenge is how to implement and deploy this technology as every factory is different, meaning that a predictive maintenance solution has to be customized for each facility. This involves a complicated set of decisions about everything from how data should be gathered to where data should be analyzed — in the cloud or at the edge of the network. Making these choices can be difficult because IIoT solutions require expertise in both information technology (IT) and shop floor operational technology (OT) — and these two disciplines historically have had little in common.

Join our experts from PrismTech, and IBM as they discuss these challenges, review current approaches and their limitations and show how an IIoT-enabled predictive maintenance solution that incorporates factory-optimized hardware, secure data distribution, and advanced analytics is the way forward.

Simon Collins, Senior Product Manager, PrismTech
Lynn Sweetwood, Senior Technical Solutions Specialist, Watson IoT Analytics, IBM

Brandon Lewis, OpenSystems Media

Further information and registration for the Ecast is available from the OpenSystems Media website at: