The Path to Analytics Maturity—Step 1: Monitoring Critical Assets

While helping hundreds of customers get started in monitoring, GE has identified a natural maturity model that describes a customer’s progression on their way to leveraging the full potential of analytics and big data. For the next four weeks, I will cover the path to analytics maturity. 

shutterstock_118589488The first step in this journey begins with basic monitoring of critical assets. Customers typically begin by instrumenting equipment with the sensors and controls networks that allow them to gather data from industrial equipment for visual monitoring and control. At the point of control, online systems often include SCADA systems—but they can also include cloud-based monitoring systems. To gain understanding of the equipment, you first need to be able to see how the equipment is running. Basic monitoring may include operations as well as facilities. In fact, tying the two together can often drive substantial cost savings.

For example, GE helped one customer tie together their operations and facilities monitoring systems —and what they saw surprised them. The customer realized that ancillary systems like lighting, compressed systems, ovens and water were operating well after production shifts had ended. Because of their ability to identify this, they were able to reduce energy costs by linking facilities to their operations, turning the lights on minutes before the first shift. Not stopping there, the customer also tied HVAC to the solution, bringing up the heat in time for operations to begin, and shutting down the heat when production ended.

Similarly, they linked operational equipment startup. Knowing it took two hours to bring the furnaces up on a paint line to full efficiency, they timed the start to two hours and five minutes before production began. The overall savings paid for itself within six months and was so effective that they rolled out this marriage of facilities and operations monitoring to all plants across their extended enterprise, saving millions of dollars by avoiding needless waste.

Stay tuned for the next post in this four-part weekly series exploring analytics maturity.

Brian Courtney

A recent transplant to the MidWest, Brian thinks Big Data “rocks.” He’s recently taken the Analytics piece of GE’s business under his wing, so if you have thoughts on any of these – MidWest, Big Data or Predictive Analytics – even rocks – follow Brian on Twitter @brianscourtney.

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