{"id":28884,"date":"2018-11-12T15:56:45","date_gmt":"2018-11-12T20:56:45","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-applications-in-industrial-iot\/"},"modified":"2018-11-12T16:08:49","modified_gmt":"2018-11-12T21:08:49","slug":"predix-at-ge-machine-learning-in-industrial-iot","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/","title":{"rendered":"Predix at GE: Machine Learning in Industrial IoT"},"content":{"rendered":"

Overview<\/strong><\/p>\n

For over 120 years, General Electric has specialized in the manufacture of heavy industrial equipment such as power turbines, jet engines, and medical imaging devices. Consequently, when Jeffrey Immelt, former chief executive of GE, centralized digital components of the company\u2019s business lines into a new division – contending that software would be GE\u2019s driver of growth in the future – it represented a departure from the organization\u2019s core business and a modernization of the firm. [1]<\/p>\n

At the center of this strategy was an application called Predix: a union of GE\u2019s software products intended to serve as an operating system for heavy industry. The idea was to collect information from the industrial \u2018internet of things\u2019 and employ machine learning to optimize maintenance and usage of industrial systems.<\/p>\n

GE had several applications used to monitor physical assets. For example, sensors on turbines might communicate information on wind speed, power output, and motor diagnostic conditions. By centralizing information and building virtual models (\u2018digital twins\u2019) of equipment, GE could employ algorithms to predict breakdowns, optimize maintenance tasks for specific components, or rebalance an entire system and reduce costs. Predix represented tremendous value for the firm internally, as it would allow GE to improve internal asset usage, and externally, as they intended to sell Predix as a data-centric platform for process improvement to industrial customers. [2]<\/p>\n

The Value of Predix <\/em>[3]<\/p>\n

\"\"<\/a><\/p>\n

Growth Challenges<\/strong><\/p>\n

At launch, Immelt envisioned Predix as a foundational pillar for GE\u2019s growth, contending that the system would push GE to become a leading software company by 2020. [4] Predix would enable even less tech-savvy customers to model physical systems with the \u2018software equivalent of Lego blocks\u2019 in an ecosystem backed by GE\u2019s machine learning algorithms for optimization. Spending matched this vision as the company hired over 1500 engineers and spent ~$4B on development in 2016. [5] However, GE Digital was hindered by P&L responsibility as development of Predix represented a huge expense. To achieve profitability, the digital arm of the company began acting as a consulting shop to industrial companies, building specialized programs for factory operations, equipment controls and monitoring that represented short-term digital projects as opposed to new, flexible platforms for modeling and machine learning that would provide long-term value to GE and its clients. [6]<\/p>\n

In 2017, John Flannery, the newly minted chief executive, changed the company\u2019s strategy for Predix, slashing the budget for the product and insisting on a more \u2018focused approach\u2019, before engaging an investment bank to seek buyers for GE\u2019s digital business. [7] The latest GE CEO, Larry Culp, has continued to discuss asset sales as a method for raising capital, and the future of Predix remains uncertain. The company may divest in its digital arm and move away from industrial applications of machine learning as a primary growth driver. [8]<\/p>\n

Future direction<\/strong><\/p>\n

In asset intensive industries, there is a clear value proposition for machine learning. Transportation or energy systems can be optimized to improve utilization and load balancing. Models that pinpoint preventative maintenance of even the smallest component in a single piece of equipment hold the potential to save millions as component failures might otherwise result in system outages. Consequently, it would be a mistake for GE to abandon Predix entirely, but the product does need focus. In the short-term, Predix should be refined for internal use to improve performance of the company\u2019s physical systems as there is some indication that GE divisions (Aviation) struggled with effectively employing Predix. [9] Investment in R&D should ensure that Predix can model \u2018digital twins\u2019 across all of GE\u2019s own asset classes. GE should also continue to form partnerships with customers in industry to expand its platform and model systems outside of its core business lines. Working with clients to build specialized analytics applications is an excellent interim strategy if GE can utilize those projects to generalize and improve its existing models. The long-term goal for the company should be to fashion Predix into a truly flexible AI engine that can be fit to any industrial system for optimization.<\/p>\n

There are several competitors and eager entrants in industrial IoT. Traditional competitors like Rockwell Automation, tech giants such as Google and Amazon, and start-ups like C3 IoT pose a threat to GE\u2019s Digital business.\u00a0 In some ways, competitors are better positioned both to handle massive amounts of data from industrial systems and to develop generalizable algorithms to improve operations in industry. Moreover, software is at least one step removed from GE\u2019s core competency of product development and manufacture of physical equipment. This set of circumstances raises a few questions. Should GE divest in Predix and concede to competitors in this space? Would the company be better served by focusing on core competencies or does machine learning in industrial IoT truly represent a brighter future for GE?<\/p>\n

(797 words)<\/p>\n

 <\/p>\n

\u00a0<\/em><\/p>\n

\u00a0<\/em><\/p>\n

\u00a0<\/em><\/p>\n

\u00a0<\/strong><\/p>\n

References<\/em><\/strong><\/p>\n

[1] “Creation Of GE Digital”.\u00a0Businesswire.Com<\/em>. September 14, 2015. https:\/\/www.businesswire.com\/news\/home\/20150914006029\/en\/Creation-GE-Digital<\/a>, accessed November 2018.<\/p>\n

[2] Lohr, Steve. “G.E., The 124-Year-Old Software Start-Up”.\u00a0Nytimes.Com<\/em>. August 28, 2016. https:\/\/www.nytimes.com\/2016\/08\/28\/technology\/ge-the-124-year-old-software-start-up.html?_r=0<\/a>, accessed November 2018.<\/p>\n

[3] \u201cPredix: The Industrial IoT Application Platform.\u201d GE Digital Platform Brief. 2018. https:\/\/www.ge.com\/digital\/iiot-platform<\/a> , accessed November 2018.<\/p>\n

[4] Lohr, Steve. “G.E., The 124-Year-Old Software Start-Up”.\u00a0Nytimes.Com<\/em>. August 28, 2016. \u00a0https:\/\/www.nytimes.com\/2016\/08\/28\/technology\/ge-the-124-year-old-software-start-up.html?_r=0<\/a>, accessed November 2018.<\/p>\n

[5] Lohr, Steve. \u201cG.E. Makes a Sharp \u2018Pivot\u2019 on Digital\u201d. Nytimes.com<\/em>. April 19, 2018. https:\/\/www.nytimes.com\/2018\/04\/19\/business\/ge-digital-ambitions.html<\/a>, accessed November 2018.<\/p>\n

[6] Moazed, Alex. \u201cWhy GE Digitial Failed\u201d. Inc.com. <\/em>January 8, 2018. https:\/\/www.inc.com\/alex-moazed\/why-ge-digital-didnt-make-it-big.html<\/a>, accessed November 2018.<\/p>\n

[7] Cimilluca, Dana; Mattioli, Dana and Gryta, Thomas. The Wall Street Journal.<\/em> July 30, 2018. https:\/\/www.wsj.com\/articles\/ge-puts-digital-assets-on-the-block-1532972822<\/a>, accessed November 2018.<\/p>\n

[8] Sheetz, Michael. \u201cGE drops below $8 after CEO Culp says he feels the ‘urgency’ and will sell assets to raise cash\u201d. CNBC.com<\/em>. November 12, 2018. https:\/\/www.cnbc.com\/2018\/11\/12\/ge-ceo-culp-says-he-will-use-asset-sales-to-raise-cash-and-bring-leverage-down-feels-the-urgency.html<\/a>, accessed November 2018.<\/p>\n

[9] Immelt, Jeffrey. \u201cHow I remade GE\u201d. 性视界 Business Review. <\/em>September 2017. https:\/\/hbr.org\/2017\/09\/inside-ges-transformation<\/a>, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

Predix is GE's attempt to harvest data from the industrial 'internet of things' and employ machine learning algorithms to optimize industrial systems.<\/p>\n","protected":false},"author":11581,"featured_media":28889,"comment_status":"open","ping_status":"closed","template":"","categories":[1091,346,2677],"class_list":["post-28884","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-general-electric","category-machine-learning","category-predictive-analytics","hck-taxonomy-organization-general-electric","hck-taxonomy-industry-industrial-products","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nPredix at GE: Machine Learning in Industrial IoT - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predix at GE: Machine Learning in Industrial IoT - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Predix is GE's attempt to harvest data from the industrial 'internet of things' and employ machine learning algorithms to optimize industrial systems.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/\" \/>\n<meta property=\"og:site_name\" content=\"Technology and Operations Management\" \/>\n<meta property=\"article:modified_time\" content=\"2018-11-12T21:08:49+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/GE-Digital.png\" \/>\n\t<meta property=\"og:image:width\" content=\"400\" \/>\n\t<meta property=\"og:image:height\" content=\"400\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predix-at-ge-machine-learning-in-industrial-iot\\\/\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predix-at-ge-machine-learning-in-industrial-iot\\\/\",\"name\":\"Predix at GE: Machine Learning in Industrial IoT - Technology and Operations Management\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predix-at-ge-machine-learning-in-industrial-iot\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predix-at-ge-machine-learning-in-industrial-iot\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/GE-Digital.png\",\"datePublished\":\"2018-11-12T20:56:45+00:00\",\"dateModified\":\"2018-11-12T21:08:49+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predix-at-ge-machine-learning-in-industrial-iot\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predix-at-ge-machine-learning-in-industrial-iot\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predix-at-ge-machine-learning-in-industrial-iot\\\/#primaryimage\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/GE-Digital.png\",\"contentUrl\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/GE-Digital.png\",\"width\":400,\"height\":400},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/predix-at-ge-machine-learning-in-industrial-iot\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Submissions\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Predix at GE: Machine Learning in Industrial IoT\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\",\"name\":\"Technology and Operations Management\",\"description\":\"MBA Student Perspectives\",\"potentialAction\":[{\"@type\":\"性视界Action\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Predix at GE: Machine Learning in Industrial IoT - Technology and Operations Management","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/","og_locale":"en_US","og_type":"article","og_title":"Predix at GE: Machine Learning in Industrial IoT - Technology and Operations Management","og_description":"Predix is GE's attempt to harvest data from the industrial 'internet of things' and employ machine learning algorithms to optimize industrial systems.","og_url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/","og_site_name":"Technology and Operations Management","article_modified_time":"2018-11-12T21:08:49+00:00","og_image":[{"width":400,"height":400,"url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/GE-Digital.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/","url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/","name":"Predix at GE: Machine Learning in Industrial IoT - Technology and Operations Management","isPartOf":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website"},"primaryImageOfPage":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/#primaryimage"},"image":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/#primaryimage"},"thumbnailUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/GE-Digital.png","datePublished":"2018-11-12T20:56:45+00:00","dateModified":"2018-11-12T21:08:49+00:00","breadcrumb":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/#primaryimage","url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/GE-Digital.png","contentUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/GE-Digital.png","width":400,"height":400},{"@type":"BreadcrumbList","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predix-at-ge-machine-learning-in-industrial-iot\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/d3.harvard.edu\/platform-rctom\/"},{"@type":"ListItem","position":2,"name":"Submissions","item":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/"},{"@type":"ListItem","position":3,"name":"Predix at GE: Machine Learning in Industrial IoT"}]},{"@type":"WebSite","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website","url":"https:\/\/d3.harvard.edu\/platform-rctom\/","name":"Technology and Operations Management","description":"MBA Student Perspectives","potentialAction":[{"@type":"性视界Action","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/d3.harvard.edu\/platform-rctom\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/28884","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission"}],"about":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/types\/hck-submission"}],"author":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/users\/11581"}],"replies":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/comments?post=28884"}],"version-history":[{"count":0,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/28884\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media\/28889"}],"wp:attachment":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media?parent=28884"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/categories?post=28884"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}