Big Data and Predictive Maintenance: Fast tracking past the Trough of Disillusionment


Last week Rotterdam School of Management organized their annual “Leadership Summit”. This year’s topic was Big data – what’s in it for me? Many examples were presented on how data with high velocity, variety, and volume can bring added value to organizations. What was left somewhat uncovered was how much benefit can big data bring. What is the monetary gain of collecting, cleaning and analyzing the vast amount of data points? General Electric recently held their “Minds + Machines” event presenting their vision on how data will change organizations. They organized a customer panel to provide insights on how they have benefited from GE’s software. Interestingly, GE’s customer cases happened to almost coincide with ones that Jens-Peter Seick, Vice President Product Management and Development at Fujitsu presented on stage, but provided some additional information on how much value can be gained. Let’s take a look how these two companies are adding value to other organizations through big data.

In his presentation Jens-Peter Seick from Fujitsu explained how big data is already being used for predictive maintenance purposes. Predictive maintenance allows machinery and equipment to be maintained based on their condition instead of a time-based schedule. This allows to save on costs as maintenance is only done when it is required. The condition is monitored using sensors and data logged by information systems and is analyzed using statistical techniques to plan and predict maintenance operations. These sensors form a part of the often talked about phenomenon “Internet of Things”, or Industrial Internet as GE likes to refer to it. Mr. Seick used the example of jet airplanes collecting gigabytes of engine data to relay to maintenance personnel in order to predict fleet malfunctions and be prepared with the correct parts available. As a GE customer, AirAsia used data collected from the GE engines in their fleet to route their planes on more efficient routes saving up to $10 million in fuel costs.

Mr. Seick talked about how offshore wind farms can relay information on their condition observed by sensors. It can then be combined with weather and other external data sets to predict failure points and find the right time to send out a boat for a maintenance operation. GE provided the example of an offshore oil rig that saved 7,5 million dollars by predicting a parts failure and allowing preventative maintenance to be done. Energy company E.ON has benefited from GE’s assistance with the data gathered from its wind farms, generating 4% more power in the turbines than previously.

By saving millions for its customers, GE’s annual revenues from its big data analytics efforts already tops $1 billion and they are continuing to invest heavily into the field. In addition to GE and Fujitsu other players in the field include all the big names from IBM to Microsoft. Professor Eric van Heck mentioned the Gartner Hype Cycle in his presentation at the Summit and pointed out the position of big data on the verge of falling into the trough of disillusionment. With so much interest and added value already brought to companies I can’t see big data staying in that valley for too long.

Are there any areas you know where big data is already being used effectively?

Sources:

GE’s Customer Panel at “Minds + Machines”

Jens-Peter Seick at the RSM Leadership Summit 2014

http://www.businessweek.com/news/2014-10-09/ge-sees-1-billion-in-sales-from-industrial-internet
http://fortune.com/2014/10/10/ge-data-robotics-sensors/

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One response to “Big Data and Predictive Maintenance: Fast tracking past the Trough of Disillusionment”

  1. 347588or says :

    For our Digital Transformation Project, my team and I analyzed a Big Data strategy for a large grocery retailer. To answer your question, yes, Big Data is used effectively in this industry already. Only five percent of the industry has an implemented and fully functional Big Data strategy, but its results are very valuable and can be noticed all along the value chain.

    The value to marketing is very obvious, examples are promotion analysis, personalized offerings and pricing optimization. More accurate sale forecasts and inventory management are examples that provide value to logistics. On the operations side loss prevention, store layout optimization and basket analysis have proven to be valuable. Finally, on the organizational level, value could be provided to a retailer by better and more efficient overall tracking of performance of strategies.

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