{"id":32062,"date":"2018-11-13T14:25:36","date_gmt":"2018-11-13T19:25:36","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/its-all-up-from-here-machine-learning-predictive-maintenance-in-elevator-service\/"},"modified":"2018-11-13T14:25:36","modified_gmt":"2018-11-13T19:25:36","slug":"its-all-up-from-here-machine-learning-predictive-maintenance-in-elevator-service","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/its-all-up-from-here-machine-learning-predictive-maintenance-in-elevator-service\/","title":{"rendered":"It\u2019s all up from here! Machine Learning &amp; Predictive Maintenance in Elevator Service"},"content":{"rendered":"<p>What do <em>Inception<\/em>, <em>Elf<\/em>, <em>You\u2019ve Got Mail<\/em>, and <em>Willy Wonka &amp; the Chocolate Factory<\/em> have in common? An iconic elevator scene, of course.<\/p>\n<p>Elevator woes proliferate movies and television because they provide that perfect \u2018wrench in the plan\u2019 for the plot \u2013 from getting trapped inside an elevator or waiting an eternity for one to arrive, to the toil of the last-resort hike up the stairs (usually at the worst possible moment!).<\/p>\n<p>In addition to the drama they stir up, we see these elevator debacles so often because they are common and relatable. In fact, experts estimate: \u201cWith more than 12 million elevators transporting over a billion people each day, elevator maintenance issues cause 190 million hours of downtime annually\u201d [1].<\/p>\n<p>One of the drivers of elevator downtime is the elevator service provider\u2019s struggle to find the right cadence for regular maintenance. ICT Monitor Worldwide notes \u201c\u2026if you take systems offline to do maintenance too often, you reduce your production yields\u201d [2]. The impact of reduced yield is extremely costly, as the nature of elevator service work necessitates expensive, highly skilled labor [3].<\/p>\n<p>On the other hand, ICT poses, \u201cIf you wait until machinery breaks to fix it, you have downtime and unhappy customers\u201d [4]. This downtime for an elevator breakdown can be lengthy and unpredictable &#8211; ranging from hours to days to weeks. The breakdown also causes a slew of labor inefficiencies. For example:<\/p>\n<ul>\n<li><strong>Reduced yield<\/strong>: The technician may abandon an in-progress effort on another machine to address the breakdown, thus wasting the expended labor.<\/li>\n<li><strong>Transportation time<\/strong>: The technician must move to the breakdown from his\/her current location.<\/li>\n<li><strong>Inventory uncertainty<\/strong>: The technician may arrive at the breakdown to find that he\/she does not have the proper part, necessitating another visit.<\/li>\n<li><strong>Safety risks<\/strong>: The pressure to move quickly to a breakdown can make technician missteps more likely.<\/li>\n<\/ul>\n<p>To combat this issue, ThyssenKrupp \u2013 a top elevator provider \u2013 partnered with Microsoft and its Azure machine learning platform to develop the world\u2019s first elevator predictive maintenance system, which they were rolling out by 2016 [5]. In his National Post article, Josh McConnell describes the functionality of the resulting product, \u201cMAX\u201d:<\/p>\n<p><em>\u201cData from Max-connected machines such as door movements, trips, powerups, car calls and error codes are collected from around the world and then sent to the cloud to be analyzed by algorithms and machine learning. From there, operational patterns are picked up and the various components\u2019 remaining lifetimes are calculated so technicians can replace parts before a breakdown occurs. Elevators can then be scheduled for maintenance during off-peak hours to [minimize] disruption and, therefore, increase efficiency.\u201d <\/em>[6]<\/p>\n<p><em>\u00a0<\/em>ThyssenKrupp faced a number of challenges in developing and rolling out the product, from both a technical perspective (e.g. lack of internal analytics experience, software obstacles related to sensors in elevators) and operational perspective (e.g. tweaking technician training and dispatch methods, updating customer contracts in light of new value proposition) [7].<\/p>\n<p>In spite of these challenges, MAX delivered excellent results. According to ThyssenKrupp, \u201cit is capable of cutting elevator downtime in half\u201d [8]. Additionally, MAX increased technician\u2019s ability to fix the machine on the first visit, reduced inventory costs, and improved customer satisfaction [9]. Customers benefit from not only the reduced downtime, but also the increased transparency achieved through data on their elevator\u2019s performance and parts via MAX&#8217;s mobile app [10].<\/p>\n<p>Given the success of the project, ThyssenKrupp plans to further develop MAX\u2019s capabilities. In particular, they hope to \u201ccreate separate predictive models for doors, motors and other elevator components to increase analytical accuracy,\u201d expanding upon general \u2018call\u2019 and door movements that are the focus today [11].<\/p>\n<p>Looking forward, ThyssenKrupp should also focus on incorporating MAX algorithms in parallel as they develop new product lines such as \u201cMulti,\u201d an elevator whose \u201ccabins can go sideways and aren\u2019t limited to one per shaft\u201d [12]. Further, they should consider the extent to which they rely on third-party vendors, diversifying their partners and developing the in-house capabilities to analyze and monitor (if not replace) vendors. As of 2017, they added a partnership with Cyient, predictive analytics expert \u2013 a step in this direction [13]. Finally, there is also an opportunity to quantify and market MAX\u2019s \u2018green\u2019 and safety impact.<\/p>\n<p>Beyond ThyssenKrupp, MAX raises questions for the elevator service industry at-large:<\/p>\n<ul>\n<li>How will the development of predictive technologies impact the competitive landscape? Will only players with the greatest scale be positioned to make the sizable capital investments required for this technology?<\/li>\n<li>What level of in-house capabilities (vs 3<sup>rd<\/sup> party support) is most efficient to support machine learning? How will that evolve?<\/li>\n<li>After optimizing frequency of parts replacement, will the next step be further improving the quality of the parts themselves or even 3D-printing replacements on the spot?<\/li>\n<\/ul>\n<p>Time will tell, but in the meantime, we can enjoy more elevator drama from Tinsel town. \u00a0(Word Count: 788)<\/p>\n<p>&nbsp;<\/p>\n<p>________________________<\/p>\n<p><strong>Sources (MLA): <\/strong><\/p>\n<p><u>\u00a0<\/u><\/p>\n<p><u>Photo courtesy of<\/u>: Bourbeau, Jeremy Dean. \u201cBusinessman In Glass Elevator Going Up.\u201d Burst.Shopify.com, 0AD, burst.shopify.com\/photos\/businessman-in-glass-elevator-going-up?q=elevator.<\/p>\n<p>&nbsp;<\/p>\n<p>[1] <em>Dr. Martin Hoelz &#8211; CIO and Head, Group Processes &amp; Information Technology, ThyssenKrupp AG.\u00a0<\/em>Boardroom Insiders, Inc, San Francisco, 2017<em>. ProQuest<\/em>, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[2] &#8220;9 IT Projects Primed for Machine Learning.&#8221; ICT Monitor Worldwide, Oct 13, 2017. ProQuest, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1950517394?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1950517394?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[3] <em>Dr. Martin Hoelz &#8211; CIO and Head, Group Processes &amp; Information Technology, ThyssenKrupp AG.\u00a0<\/em>Boardroom Insiders, Inc, San Francisco, 2017<em>. ProQuest<\/em>, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[4] &#8220;9 IT Projects Primed for Machine Learning.&#8221; ICT Monitor Worldwide, Oct 13, 2017. ProQuest, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1950517394?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1950517394?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[5] &#8220;Thyssenkrupp Rolls Out MAX in Germany: World&#8217;s First Predictive Elevator Maintenance Service.&#8221; M2 Presswire, Apr 26, 2016. ProQuest, http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1784105787?accountid=11311.<\/p>\n<p>&nbsp;<\/p>\n<p>[6] McConnell, Josh. &#8220;The Future of Elevators; Moving People Sideways and Data to the Cloud.&#8221; National Post, Aug 08, 2017. ProQuest, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1927105305?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1927105305?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[7] <em>Dr. Martin Hoelz &#8211; CIO and Head, Group Processes &amp; Information Technology, ThyssenKrupp AG.\u00a0<\/em>Boardroom Insiders, Inc, San Francisco, 2017<em>. ProQuest<\/em>, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[8] Ibid.<\/p>\n<p>&nbsp;<\/p>\n<p>[9] &#8220;9 IT Projects Primed for Machine Learning.&#8221; ICT Monitor Worldwide, Oct 13, 2017. ProQuest, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1950517394?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1950517394?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[10] &#8220;Thyssenkrupp Rolls Out MAX in Germany: World&#8217;s First Predictive Elevator Maintenance Service.&#8221; M2 Presswire, Apr 26, 2016. ProQuest, http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1784105787?accountid=11311.<\/p>\n<p>&nbsp;<\/p>\n<p>[11] <em>Dr. Martin Hoelz &#8211; CIO and Head, Group Processes &amp; Information Technology, ThyssenKrupp AG.\u00a0<\/em>Boardroom Insiders, Inc, San Francisco, 2017<em>. ProQuest<\/em>, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[12] McConnell, Josh. &#8220;The Future of Elevators; Moving People Sideways and Data to the Cloud.&#8221; National Post, Aug 08, 2017. ProQuest, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1927105305?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1927105305?accountid=11311<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>[13] <em>Dr. Martin Hoelz &#8211; CIO and Head, Group Processes &amp; Information Technology, ThyssenKrupp AG.\u00a0<\/em>Boardroom Insiders, Inc, San Francisco, 2017<em>. ProQuest<\/em>, <a href=\"http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311\">http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1932882578?accountid=11311<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>ThyssenKrupp\u2019s MAX is on the rise<\/p>\n","protected":false},"author":11324,"featured_media":32063,"comment_status":"open","ping_status":"closed","template":"","categories":[841,346,2629],"class_list":["post-32062","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-elevators","category-machine-learning","category-predictive-maintenance","hck-taxonomy-industry-industrial-products"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>It\u2019s all up from here! Machine Learning &amp; Predictive Maintenance in Elevator Service - 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\/its-all-up-from-here-machine-learning-predictive-maintenance-in-elevator-service\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"It\u2019s all up from here! 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