{"id":11690,"date":"2021-07-03T12:08:59","date_gmt":"2021-07-03T10:08:59","guid":{"rendered":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/?p=11690"},"modified":"2021-07-03T12:12:50","modified_gmt":"2021-07-03T10:12:50","slug":"ai","status":"publish","type":"post","link":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/2021\/07\/03\/ai\/","title":{"rendered":"AI works best with humans, not for humans"},"content":{"rendered":"
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Fleets are finding that artificial intelligence and other predictive analytics are helping humans make more informed and faster decisions, making them more productive and profitable.<\/div>\n
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Predictive analytics is already transforming the transportation world. Artificial intelligence (AI) and machine learning (ML) have the power to make fleets more productive and profitable. But it still takes people to get the most of these emerging technologies.<\/p>\n

That was the overreaching message of a recent Trimble in.sight<\/a> virtual panel discussion on new supply chain trends and how some businesses embrace AI technology to improve efficiencies and make better decisions.<\/p>\n

A few years ago, the buzz in the transportation industry was that \u201cAI was going to solve everything,\u201d according to David Dunst, IT manager at Paper Transport<\/a>. He expects AI to take off over the next five years\u2014but it won\u2019t take over the trucking industry.<\/p>\n

\u201cAI really adds value when it\u2019s augmented intelligence,\u201d Dunst explained. \u201cIt\u2019s about providing guidance to the human, who then makes better, faster, more informed decisions. When we take all the knowledge we have as humans and apply AI\u2014when we put those together\u2014we become so much more intelligent and so much better at what we do as businesses.\u201d<\/p>\n

AI and ML \u201cfall under the umbrella of predictive analytics, which predict what the future will look like,\u201d said Peter Covach, industry solutions adviser for Trimble, who moderated the June 24 panel. \u201cMore interestingly, AI and ML have a more immediate use in predicting what the computer is seeing.\u201d<\/p>\n

Covach said this could be as simple as a social media site automatically identifying and tagging a person in a photograph or as complex as a self-driving vehicle predicting when it should apply its brakes. \u201cThe computer is interpreting the situation based on the data you give it,\u201d Covach explained. \u201cBy using advanced mathematics and historical data, data scientists are able to make those predictions with a high degree of certainty. These same concepts that power self-driving cars or recognize faces can be used to fuel business objectives to the next level.\u201d<\/p>\n

Better business decisions<\/h3>\n

Transportation and logistics company Covenant Transport Services<\/a> uses Domo, a business intelligence and analytics tool that transforms data into live visualizations and real-time metrics. Matt Mullins, VP of program management for Covenant, said the AI solution was easy to implement in his fleet.<\/p>\n

Domo<\/a> provides Covenant workers feedback to help make more informed decisions throughout the day, Mullins said. \u201cThis could apply to transportation for resource utilization for a specific market; it could be the need to move resources around better for better utilization of picking and warehouses\u2014we\u2019re 3PL (third-party logistics) as well,\u201d he said. \u201cSo maybe we need to determine if we want to change a slotting of a specific SKU (stock-keeping unit), based on trends, for picking.\u201d<\/p>\n

Mullins explained that the Domo program helps Covenant \u201csee across the warehouses\u201d to help the fleet slot freight and equipment across its supply chain. He said the company plans to expand the AI usage that comes with the business tool. \u201cRight now, we use it as an analytical, slightly predictive tool. Then we\u2019ll get to the point where we\u2019re much more predictive. Then, we\u2019ll add some machine learning along the way at some point.\u201d<\/p>\n

For now, Covenant\u2019s people are still making the big decisions. \u201cBut there are several areas that we\u2019re allowing it to make decisions based on those more mundane tasks,\u201d Mullins said.<\/p>\n

Robotic process automation<\/h3>\n

Some of those mundane tasks are completed using robotic process automation (RPA) for repetitive, labor-intensive needs so humans can \u201cutilize soft skills, which humans are meant to be doing,\u201d Mullins added.<\/p>\n

For-hire truckload carrier Paper Transport has also embraced RPA. \u201cWe found that we had a bunch of processes that users were just consistently doing the same thing day in and day out,\u201d explained Dunst, the fleet\u2019s IT manager.<\/p>\n

\u201cWe know we have the information on our systems on what they have to do,\u201d Dunst said of planners at Paper Transport. \u201cSo rather than having them going to these websites and setting appointments, or searching for available loads to backfill our network, we use RPA tools to actually go into our customers\u2019 sites and set the appointment that we want. We can search as broker sites and find what loads are available and then bring all that information back to the user who needs that information or push that data to the customer or to our systems to further the delivery of the load.\u201d<\/p>\n

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Dunst added that RPA helped Paper Transport grow as a carrier without hiring more planners and office personal. \u201cThe process we had to before\u2014where people were having to do all this manual work\u2014meant we would just have to keep hiring and hiring and hiring to keep up. That\u2019s not sustainable if you want to grow as an organization. We found that RPA really took away those tasks that people didn\u2019t want to have to do and open them up to take on some more meaningful and valuable work.\u201d<\/p>\n

AI and humans<\/h3>\n

Paper Transport first \u201cdipped its toe into AI\u201d to better understand which drivers in its fleet are more accident-prone on the road, Dunst said. \u201cIt allowed us to build a model that helps us understand who are the drivers that might be at risk and how do we change our behaviors with those drivers to ensure that those drivers don\u2019t have an accident or an incident,\u201d he noted. \u201cThat was the first thing we ever did with AI.\u201d<\/p>\n

Dunst said that its first foray into AI helped the fleet reduce significant vehicle accidents because it led fleet managers to engage more with drivers. Along with making the Paper Transport fleet safer, it helped with driver retention \u201cbecause drivers felt more personally engaged with the company,\u201d he said. \u201cWe were directly reaching out to some of those drivers and saying, \u2018Hey, how\u2019s it going? What\u2019s going on with you?\u2019\u201d<\/p>\n

That work with AI, led Paper Transport to Trimble\u2019s Dispatch Adviser<\/a>, an optimized dispatch solution for the transportation industry that prescriptively suggests matches between loads, drivers, and equipment.<\/p>\n

While the accident prevention work was \u201cmuch more on our shoulders\u201d as a fleet, Dunst noted, the dispatch solution was \u201cmore of a partnership, where Trimble brought a bunch of data scientists, resources, and knowledge to the project.\u201d<\/p>\n

Trimble Dispatch Advisor reviews all available matches by looking at variables, including location, drivers\u2019 hours of service data, time windows, and more, such as the effects of selecting a specific load on future orders. The system then recommends a primary match, along with reasons for the recommendation. Other matches are also suggested, including an estimated impact on the company should they choose any other options rather than the first or primary match.<\/p>\n

This, Dunst said, made \u201ca huge difference from a profitability standpoint because the Dispatch Advisor was bringing up loads in front of our planner that they didn\u2019t even realize would be a good fit for that driver.\u201d<\/p>\n

He said his planners embraced the guidance of the AI product. The Trimble solution led the fleet\u2019s planners to take less time to pick a load. The loads they were selecting reduced the miles drivers needed to cover to pick up or deliver freight. \u201cThat really positioned our drivers to get home on time. And it cut down on some of the additional costs that we were seeing previously,\u201d Dunst said.<\/p>\n

The more fleet data available, the more powerful and accurate AI-driver solutions become, said Chris Orban, VP of data science at Trimble. He also noted that AI solutions are there to assist humans in decision making, not necessarily make all the decisions.<\/p>\n

\u201cAs powerful and as cool as these terms AI and machine learning are, we still as humans might know something that the computer doesn\u2019t,\u201d Orban said. \u201cThe computer can process huge amounts of information very, very quickly. But our human brains might know that a particular driver really hates to run in Pennsylvania, or they\u2019ve never driven in snow before. So before I send them over Donner Pass in the winter, let\u2019s figure some other plan out. Those are the kinds of things that we need to be able to supplement with our human knowledge even as we get more advanced on the machine learning side of things.\u201d<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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By Josh Fisher<\/a><\/span><\/p>\n<\/div>\n

Source: https:\/\/www.fleetowner.com\/<\/a><\/span><\/p>\n

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Fleets are finding that artificial intelligence and other predictive analytics are helping humans make more informed and faster decisions, making them more productive and profitable. Predictive analytics is already transforming the transportation world. Artificial intelligence (AI) and machine learning (ML) have the power to make fleets more productive and profitable. But it still takes people…<\/p>\n","protected":false},"author":3,"featured_media":11691,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[18],"tags":[142],"_links":{"self":[{"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/posts\/11690"}],"collection":[{"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/comments?post=11690"}],"version-history":[{"count":2,"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/posts\/11690\/revisions"}],"predecessor-version":[{"id":11696,"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/posts\/11690\/revisions\/11696"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/media\/11691"}],"wp:attachment":[{"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/media?parent=11690"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/categories?post=11690"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/advancedfleetmanagementconsulting.com\/eng\/wp-json\/wp\/v2\/tags?post=11690"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}