How AI And Machine Learning Are Helping Drive The GE Digital Transformation – Forbes

General Electric CEO Jeff Immelt speaks during a groundbreaking ceremony at the site of GE’s new headquarters as Massachusetts Gov. Charlie Baker, left, and Boston Mayor Marty Walsh look on, Monday, May 8, 2017, in Boston. (AP Photo/Michael Dwyer)

By Randy Bean and Thomas H. Davenport

General Electric (GE) was co-founded in 1897 by Thomas Edison. Today, 120 years later, GE is the single company with the longest continual presence in the Dow Jones Industrial Average, and is undergoing one of the most dramatic transformation initiatives of any major company.  Mainstream legacy businesses should take note. In a matter of only a few years, GE has migrated from being an industrial and consumer products and financial services firm to a “digital industrial” company with a strong focus on the “Industrial Internet” and $7 billion in software sales in 2016.

This is the story of how GE has accomplished this digital transformation by leveraging AI and machine learning fueled by the power of Big Data.

Undertaking the Digital Transformation

The GE transformation is an effort that is still in progress, but one which is increasingly looking like a success story, as chronicled in the 2016 MIT Sloan Management Review story GE’s Big Bet on Data and Analytics. GE’s software offering, Predix, has become well-established. Less well-understood is GE’s focus on analytics and AI to make sense of the massive volumes of Big Data that are being captured by its industrial devices. Bill Ruh, the CEO of GE Digital and the company’s Chief Digital Officer, emphasizes the role and importance of data and analytics in the company’s transformation. In a recent blog post on the GE site, Ruh wrote about Waking up as a Software and Analytics Company. Ruh observes, “It’s not enough to connect machines. You have to make your machines smarter. You need to figure out the best ways for embedding intelligence into machines and devices. Then you need to develop the best techniques for collecting the data generated by those machines and devices, analyzing that data and generating usable insights that will enable you to run your equipment more efficiently and optimize your operations and supply chains.” This is how companies become data-driven organizations.

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Leveraging AI and Machine Learning to Create a “Digital Twin”

In a recent interview that we conducted with Ruh, he emphasized the importance of machine learning as one approach that has been particularly beneficial in helping GE leverage the power of Big Data and the Internet of Things (IoT). Machine learning technology, according to Ruh, is critical to making the “digital twin” concept successful. A digital twin is a digital replica, or data-based representation of an industrial machine. When sensors in those machines — for example, a jet engine, a gas turbine or a windmill — gather data on the machine’s attributes (heat, vibration, noise and the like), the data is collected in the “cloud” and organized into a model “twin” that allows analysis that replicates the machine’s performance. The digital twin model can then be used to diagnose faults and predict the need for maintenance, ultimately reducing or eliminating unplanned downtime in that machine. The digital twin concept can be extended to aggregations of machines — a plant or fleet can be digitally twinned as well.

The data never stops flowing into these digital twin models, which can be populated by many unique variables. Because there may also be changes over time relative to which variables and models best predict the need for required maintenance, machine learning represents the best technology approach to addressing these requirements. Machine learning approaches make it possible to learn from new data and to modify predictive models over time. Ruh points out that machine learning makes it possible to identify anomalies, signatures and trends in machine performance and develop understanding of patterns of behavior. In addition, Machine Learning can be applied to help identify efficiencies within a machine and use this as a best practice for other machines. Ruh notes that GE already has about 750,000 digital twins and is rapidly adding more.

Building a Digital Organization

Like any mainstream legacy company, GE didn’t start out with the required expertise it needed to leverage Artificial Intelligence and machine learning.  So, GE went out and acquired startups that possessed the requisite skills and expertise — in some cases beginning this process several years back. A precursor to GE Digital was the company’s Intelligent Platforms business, which acquired a company called SmartSignal in 2011 to provide supervised learning models for remote diagnostics. In 2016, GE acquired a firm named Wise.io for its unsupervised deep learning capabilities and to bring in house the data scientists who understood this field. GE has successfully integrated both companies’ people and software into its GE Digital business. In particular, the Wise.io unsupervised learning capabilities have become very helpful in identifying anomalies and trends in industrial sensor data without having to create a substantial volume of labeled data. GE is also employing machine learning technology from a third AI acquisition, Bit Stew (also acquired in 2016) to integrate data from multiple sensor sources in industrial equipment. As an example, this solution is particularly helpful in assembling and organizing data coming from a variety of machines in a plant.

Using Machine Learning to Integrate Supplier Data

Not all of GE’s activities with AI are restricted to industrial equipment. GE also employs machine learning to integrate business data. It has partnered with the Cambridge, MA based data curation firm, TAMR, co-founded by MIT Professor Michael Stonebraker and entrepreneur and former industry CIO Andy Palmer. In a recent article, GE Saved Millions by Using this Data Start-Ups Software, Emily Galt, vice president of technical product management for GE Digital Thread, discussed how the company has leveraged machine learning to achieve savings. GE’s Ruh notes, “The supplier data integration was a big win.  It’s easy for suppliers to charge different prices for the same product when you can’t compare them across business units. We might spend $250 million a year on nuts and bolts, but that only becomes salient when you look across business units and see if they’re coming from the same suppliers.  If they are, you are in a much better position to negotiate.” GE claims that the TAMR machine learning software has enabled GE to save $80 million over the past few years.

A “Future Ready” Software and Artificial Intelligence Company

Speaking at the MIT Sloan CIO Symposium held in Boston last month, GE CIO Jim Fowler observed how GE has evolved into a “future ready” company, where “the technology is going to become the process” and where employees will work in “mission-based teams” that form to solve specific business problems and then disband to go and find and solve new business problems.

Today, GE has become one of the world’s leading users and vendors of machine learning for industrial data. GE certainly knew when it embarked upon its “digital industrial” strategy that it would be in the software business, but it may not have counted on also being in the Artificial Intelligence business.

Randy Bean is an industry thought-leader and author, and CEO of NewVantage Partners, a strategic advisory and management consulting firm which he founded in 2001.  He is a contributor to Forbes, Harvard Business Review, MIT Sloan Management Review, and The Wall Street Journal.  You can follow him at @RandyBeanNVP.

Thomas H. Davenport is the President’s Distinguished Professor in Management and Information Technology at Babson College, a research fellow at the MIT Initiative on the Digital Economy, and a senior adviser at Deloitte Analytics.  Author of over a dozen management books, his latest is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. You can follow him at @tdav

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