\nThe world has previously witnessed three industrial revolutions \u2013 starting with the advent of the steam engine to the more recent computers and automated systems \u2013 that drove transformational changes [2]. Today, we face another inflection point amidst the fourth industrial revolution. Coined \u2018Industry 4.0\u2019, this phenomenon connects the digital and physical realms through sensors and the internet of things (IoT), collecting and analyzing big data to drive \u201cautonomous, de-centralized decisions, with the aim of increasing industrial e\ufb03ciency, productivity, safety, and transparency\u201d [3]. 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 [4]. A simple analogy likens this to \u201cpersonalized medicine\u201d for machines [5].<\/p>\n
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 \u201cpeople, data, and machines\u201d [6]. \u00a0Predix is enabled by advanced machine learning applications that create a \u201cdigital twin\u201d, 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 [7].<\/p>\n RECOMMENDATIONS FOR BETTER OUTCOMES<\/strong><\/p>\n While GE\u2019s 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.<\/p>\n 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 [9]. In doing so, GE can not only create value for itself, but can also emerge as a pioneer that helps raise the industry\u2019s potential.<\/p>\n GE\u2019s 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\u2019s 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.<\/p>\n To that end, how should GE\u2019s 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?<\/p>\n 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)<\/p>\n <\/p>\n SOURCES<\/strong><\/p>\n [1] GE Website. \u201cEverything you need to know about the Industrial Internet of Things\u201d. Web. Accessed Nov 10, 2018.\u00a0https:\/\/www.ge.com\/digital\/blog\/everything-you-need-know-about-industrial-internet-things<\/a><\/p>\n [2] Carvalho, Nubia et al. \u201cManufacturing in the fourth industrial revolution: A positive prospect in Sustainable Manufacturing\u201d. Procedia Manufacturing<\/em>, Volume 21 (2018). Web. Accessed Nov 10, 2018.\u00a0https:\/\/ac.els-cdn.com\/S2351978918302105\/1-s2.0-S2351978918302105-main.pdf?_tid=d372b7b7-8069-4ce8-aefd-175ccf4dd79e&acdnat=1542090531_346d5d8c93f9405cf862e16e72dc561b<\/a><\/p>\n [3] Boyles, Hugh et al. \u201cThe industrial internet of things (IIoT): An analysis framework\u201d. Computers in Industry<\/em>, Volume 101 (2018). Web. Accessed Nov 10, 2018. https:\/\/reader.elsevier.com\/reader\/sd\/pii\/S0166361517307285?token=9640180C99D32AC6DA475E7A546A0E10BEFE586171E355E8014790274FEA9638010A27B9857AB7CA2B818A39074C8E79<\/a><\/p>\n [4] McKinsey & Co. \u201cSmartening up with Artificial Intelligence (AI) – What\u2019s in it for Germany and its Industrial Sector?\u201d (2017). Web. Accessed Nov 10, 2018.\u00a0\u00a0https:\/\/www.mckinsey.com\/~\/media\/McKinsey\/Industries\/Semiconductors\/Our%20Insights\/Smartening%20up%20with% 20artificial%20intelligence\/Smartening-up-with-artificial-intelligence.ashx<\/a><\/p>\n [5] NY Times. \u201cG.E., the 124-Year-Old Software Start-Up\u201d (2016). Web. Accessed Nov 10, 2018.\u00a0https:\/\/www.nytimes.com\/2016\/08\/28\/technology\/ge-the-124-year-old-software-start-up.html<\/a><\/p>\n [6] GE Reports. \u201cEverything You Always Wanted to Know About Predix, But Were Afraid to Ask\u201d (2014). Web. Accessed Nov 10, 2018.\u00a0https:\/\/www.ge.com\/reports\/post\/99494485070\/everything-you-always-wanted-to-know-about-predix-2\/<\/a><\/p>\n [7] Forbes. \u201cHow AI And Machine Learning Are Helping Drive The GE Digital Transformation\u201d (2017). Web. Accessed Nov 10, 2018.\u00a0https:\/\/www.forbes.com\/sites\/ciocentral\/2017\/06\/07\/how-ai-and-machine-learning-are-helping-drive-the-ge-digital-transformation\/#6cf21361686b<\/a><\/p>\n [8] GE Whitepaper. \u201cGE\u2019s Digital Industrial Transformation Playbook\u201d (2017). Web. Accessed Nov 10, 2018.\u00a0https:\/\/www.ge.com\/uk\/sites\/www.ge.com.uk\/files\/ge-digital-industrial-transformation-playbook-whitepaper.pdf<\/a><\/p>\n![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/figure-1-3.png\")
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