\u201cAs a result of CSXT interline intermodal service changes, Union Pacific is no longer able to offer a number of interline intermodal services to the eastern U.S. Due to the complexity of CSXT\u2019s intermodal service changes, Union Pacific is providing a matrix to assist you in identifying any effects to your interline business with [us], as well as presenting adaptive solutions.<\/p><\/blockquote>\n
Now, this particular paragraph set me to thinking:\u00a0 How could Machine Learning and Predictive Analytics have helped Union Pacific to mitigate the effects of these service changes?\u00a0 Is there something that could have been done?<\/p>\n
Absolutely.<\/p>\n
Predictive Analytics and Machine Learning live for complex intermodal service changes, and in fact could have alerted Union Pacific as to the possibility of this scenario occurring well<\/em> before it occurred, and thus they would be able to provide more than a matrix to their customers, they would have been able to provide options<\/strong><\/em>.<\/p>\nHow could AI have helped Union Pacific Railroad? Here\u2019s how:<\/h2>\nFrom the article:<\/p>\n
\u201cIntermodal accounts for 44% of CSX\u2019s volume, but in 2017 its intermodal revenue per unit of $633 was the lowest of the railroad\u2019s business units. In contrast, UP\u2019s domestic intermodal business increased 7% in 2Q18, due chiefly to tight truckload capacity.\u201d<\/p><\/blockquote>\n
This type of scenario is actually a textbook case on how ML\/PA can work for you to avert awkward moments with your client.\u00a0 On the historical side of things, a process could be monitoring the usage of various intermodal facilities.\u00a0 CSX made a decision that was driven by profit \u2014 in this case flat revenue, and that is something that is relatively easy to detect.\u00a0 Thus a system could have been tracking this usage over time and comparing that with Union Pacific\u2019s usage.\u00a0 A decline in business units at CSX intermodal facilities versus an increase in intermodal business at Union Pacific?\u00a0 That\u2019s a problem in the making, and relatively simple to determine.\u00a0 Once predicted, it could have taken two steps to mitigate future disruption:<\/p>\n
\n- Realizing the increasing historical discrepancy, the system could have alerted Union Pacific so they would have been aware that the possibility for service changes may exist.\u00a0 Further, it could continue to monitor the various intermodal schedules to predict exactly which routes may be affected, and to what extent.\u00a0 It would accomplish this using scheduling data and manifests from the various centers of operation.<\/li>\n
- On the future side of things, the system could start running scenarios to determine the best options for moving forward.\u00a0 It could seek out alternate modes of transport, transfer facilities.\u00a0 In the absence of existing facilities, it could determine the ROI of purchasing commercial real estate to fulfill service requirements.\u00a0 It could seek out new supply-chain partners and simulate negotiations to determine the most cost-effective pathways forward.\u00a0 The system would then be able to present these various options to department heads as the problem became more apparent.\u00a0 By the time it would actually manifest, alternatives and\/or replacements would already be waiting in the queue.<\/li>\n<\/ol>\n
Then, when the actual end-of-life dates arrived, Union Pacific would be ready with new intermodal solutions to the service changes.<\/p>\n
\u00a0<\/p>\n<\/body>","protected":false},"excerpt":{"rendered":"
A colleague of mine in the railroad industry recently shared this article with me.\u00a0 As I was reading through, I came across this paragraph: \u201cAs a result of CSXT interline intermodal service changes, Union Pacific is no longer able to offer a number of interline intermodal services to the eastern U.S. Due to the complexity […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"pagelayer_contact_templates":[],"_pagelayer_content":"","footnotes":""},"categories":[],"tags":[],"class_list":["post-2071","post","type-post","status-publish","format-standard","hentry"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/posts\/2071","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/comments?post=2071"}],"version-history":[{"count":0,"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/posts\/2071\/revisions"}],"wp:attachment":[{"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/media?parent=2071"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/categories?post=2071"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/capture.club\/portal\/wp-json\/wp\/v2\/tags?post=2071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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