Machine learning applications for large-scale industrial assets, such as turbines and jet engines, hold powerful potential with the promise to generate massive impact in high-risk scenarios where failures can result in life-or-death situations . Therefore, it is not surprising that new-age machine learning technology is rapidly re-shaping the future for General Electric (GE), a widely diversified manufacturing conglomerate with businesses across sectors, such as energy, aviation, and healthcare.
THE FOURTH INDUSTRIAL REVOLUTION IS THE NEW NORMAL
The world has previously witnessed three industrial revolutions – starting with the advent of the steam engine to the more recent computers and automated systems – that drove transformational changes . Today, we face another inflection point amidst the fourth industrial revolution. Coined ‘Industry 4.0’, this phenomenon connects the digital and physical realms through sensors and the internet of things (IoT), collecting and analyzing big data to drive “autonomous, de-centralized decisions, with the aim of increasing industrial eﬃciency, productivity, safety, and transparency” . For example, as shown in Figure 1, sensors placed on industrial machinery can collect real-time data, which can then be analyzed using machine learning algorithms to detect abnormalities and enable preventative maintenance, thereby driving efficiency, increasing utilization, minimizing downtime, and generating cost savings . A simple analogy likens this to “personalized medicine” for machines .
GE’S STRATEGY FOR A “DIGITAL INDUSTRIAL” FUTURE
Industry 4.0 represents a massive, yet inevitable, shift, and GE has invested significantly in building and acquiring capabilities to maintain its competitive advantage. At the core of its strategy lies Predix, a proprietary software platform that connects “people, data, and machines” . Predix is enabled by advanced machine learning applications that create a “digital twin”, i.e. a virtual copy, of a physical machine. A digital twin is used to simulate outcomes by applying real-time data transmitted via the cloud from sensors placed on the physical twin. Machine learning algorithms that leverage historical data and pattern recognition are used to predict the impact of on-the-ground conditions on machine performance, enabling early intervention to eliminate system downtime and failures. This can be applied to a single machine and is also scalable to networks of machines .
To date, GE’s roll-out strategy is comprised of a three-pronged approach :
- “GE for GE” (quick win): This involves applying Predix and existing machine learning capabilities in hundreds of GE factories to drive productivity in internal manufacturing processes.
- “GE for Customers” (short term): Perfecting its capabilities based on internal use, GE plans to deliver these capabilities to customers through applications, helping them improve productivity of their assets.
- “GE for the World” (short-medium term): Lastly, GE aspires to leverage Predix to crowd-source cutting-edge applications through an open-innovation model that democratizes access to its platform and drives innovation exponentially.
RECOMMENDATIONS FOR BETTER OUTCOMES
While GE’s strategy outlined above effectively addresses all its stakeholders, separating its implementation into the three distinct phases may prevent GE from unlocking the full potential in the short term. Specifically, the open-innovation model can generate benefit if applied across all three phases through the innovation funnel approach (see Figure 3). By constantly in-sourcing the best ideas from around the world, GE can push the boundaries and design for compelling extreme use-cases and unknown unknowns.
In the short-to-medium term, as GE proves its technology with successful use-cases, the company must also leverage its leading capabilities to address big challenges facing all manufacturers grappling with Industry 4.0. For example, GE can take the lead to invest in building solutions that tackle major cyber security risks inherent to cloud-based technologies, and/or can establish a best-in-class approach for integrating large volumes of disparate data needed for machine learning applications . In doing so, GE can not only create value for itself, but can also emerge as a pioneer that helps raise the industry’s potential.
GE’s UNCERTAIN FUTURE
GE’s recent troubles, with shrinking profitability and a struggling stock price, have resulted in significant investor pressure and sweeping management changes. Given this uncertainty, there is growing speculation about the future of GE’s digital business. Regardless, most would agree that it is difficult to imagine a world where GE can thrive, or even survive, without embracing machine learning, and other Industry 4.0 trends.
To that end, how should GE’s management balance the need for short-term financial gain to satisfy shareholders, with the more long-term value creation outlook needed to invest in R&D for new-age technologies?
Taking a step back, should we even be looking at GE and other incumbents to drive the next generation of innovation, or will new start-ups emerge to conquer this space? (777 words)
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 Harvard Business School RC Technology & Operations Management, “Module 2 to 3 transition” (2018).