At the end of his 16-year reign as CEO of General Electric (GE) in 2017, Jeff Immelt wrote that he was leaving GE as a “125-year-old start-up.” A year earlier, the company had invested $4 billion to strengthen its analytics and machine learning capabilities that was powering the historic conglomerate’s transformation from a manufacturer of jet engines, gas turbines, wind turbines and locomotives into a digital industrial giant . Its clients recognized the value of the data coming from the GE industrial equipment to drive process improvements, and GE set out with the goal of automating data driven decision making to drive gains in gas turbine fuel efficiency, improve wind turbine output, decrease power plant downtimes and increase heavy service equipment intervals .
GE had an inherent head start in the Industry 4.0 transformation since they had been collecting data from sensor on their industrial equipment for decades. In 2012, they created the cloud-based Predix software platform to provide a means to collect sensor data to help machine operators and maintenance engineers improve machine efficiency, schedule maintenance checks and reduce downtime . Using the data collected from these smart machines and domain knowledge, engineers create a “digital twin” of a machine (ex. gas turbine) or a system (ex. gas burning power plant) to model the state of a customer’s asset. Advanced machine learning algorithms allow these digital models of assets to continuously monitor, validate and update in real time, thus allowing for automatic detection of any deviations in system performance [4, 5]. In a gas fired powerplant, for example, the Predix system machine learning solutions give companies the ability to monitor the health and automatically detect anomalies in their gas turbines real-time to reduce downtime, improve turbine efficiency through nonlinear optimizations of combustion processes, and optimize the dispatch of energy to the grid based on supply/demand .
GE’s management has recognized that machine learning techniques are a key enabler to using their hardware more efficiently, and can be one of their key drivers of growth as they look to innovate in mature industrial industries . In an attempt to continue to grow its expertise in the field of machine learning and create synergies between business, management has grown GE Digital’s through acquisition. In 2016, GE Digital purchased startups Meriduim (equipment management software) and ServiceMax (industrial field service worker management software) for a combined $1.4billion . It also added startups such Wise.io , Tamr  and IQP  to its portfolio to continue strengthen its competencies in artificial intelligence and finding effective ways to apply it to GE’s existing businesses. In July 2018, GE announced a partnership with Microsoft to deploy Predix on Microsoft’s Azure cloud infrastructure and to co-develop machine learning applications for digital industry applications .
However, while in 2011 GE Digital was one of the few players in the industrial digital field, the landscape has drastically changed. Industrial analytics solutions from industrial giants like such as Siemens, Honeywell and ABB and cloud-computing experts such as Google and Amazon pose real threats to GE Digital’s Predix platform and challenge for GE Digital place as a leader in digital industry .
Perhaps the largest challenge faced by GE Digital and its ability to continue to strengthen position in the digital industrial revolution is the strength of the company. With the ousting of John Flannery as CEO, an ardent supporter of GE’s digital transformation , and large budget cuts at GE Digital [13, 7], it is unclear what is in store for the arm of the conglomerate. Predix and its machine learning capabilities has demonstrated to be a key product in GE’s portfolio the past decade, and the company must continue to leverage its capabilities as the industry moves faster towards Industry 4.0. GE has the unique advantage of having a century of domain knowledge in the design and analysis of the industrial systems being monitored and analyzed with the artificial intelligence techniques. GE’s management team should thus continue to push the integration of machine learning earlier into the product development process. GE being able to create a focused Industry 4.0 product portfolio that integrates hardware, data capture, analysis, monitoring and adaptive optimization will be a key enabler fuel the growth of the new General Electric.
How does a struggling industrial giant continue to invest in and grow their machine learning competencies in times of restructuring? Should a company like GE be outsourcing the development of these tools to external experts and focus on hardware/system integration or continue to develop them in-house?
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