[session] Analyzing #IoT Data | @ThingsExpo #M2M #BigData #Analytics
from [session] Analyzing #IoT Data | @ThingsExpo #M2M #BigData #Analytics
An IoT product’s log files speak volumes about what’s happening with your products in the field, pinpointing current and potential issues, and enabling you to predict failures and save millions of dollars in inventory. But until recently, no one knew how to listen. In his session at @ThingsExpo, Dan Gettens, Chief Research Officer at OnProcess, will discuss recent research by Massachusetts Institute of Technology and OnProcess Technology, where MIT created a new, breakthrough analytics model for spare parts forecasting and inventory planning using connected-product failure predictability. He’ll show how, by analyzing IoT data, you can slash both costly inventory stock and stock-outs, and meet heightened customer expectations for repairs and uptime.
An IoT product’s log files speak volumes about what’s happening with your products in the field, pinpointing current and potential issues, and enabling you to predict failures and save millions of dollars in inventory. But until recently, no one knew how to listen. In his session at @ThingsExpo, Dan Gettens, Chief Research Officer at OnProcess, will discuss recent research by Massachusetts Institute of Technology and OnProcess Technology, where MIT created a new, breakthrough analytics model for spare parts forecasting and inventory planning using connected-product failure predictability. He’ll show how, by analyzing IoT data, you can slash both costly inventory stock and stock-outs, and meet heightened customer expectations for repairs and uptime.
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