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Predictive analytics can help keep trucks on the road. Navistar has been piloting a new OnCommand Connection feature called Live Action Plans, which predicts when a part is going to fail before it does.<\/strong> Photo: Navistar<\/em><\/p>\n When you hear the words artificial intelligence or machine learning, you may think of IBM\u2019s Watson, or the 2001 Steven Spielberg flick about a robotic boy. When you hear blockchain, you may think bitcoin. Yet these and other emerging data technologies are already being employed in trucking and logistics.<\/p>\n \u201cI\u2019ve been in the trucking world for a long time. Normally, trucking has not adopted things as fast\u201d when it comes to technology, says Tim Leonard, chief technology officer and executive vice president at TMW Systems. But when it comes to things like deep learning analytics and blockchain, he says, \u201cthe transportation world is one of the leaders.\u201d<\/p>\n We\u2019ve been hearing for years now about the idea of predictive maintenance rather than preventive maintenance \u2013 the ability to analyze data and predict when failures are about to happen, allowing fleets to replace or repair components before they lead to a breakdown on the road.<\/p>\n Some fleets have already been using data from their own fleets to do predictive maintenance. As Randy Obermeyer, terminal manager in charge of equipment and maintenance for Batesville\u2019s private fleet in Batesville, Indiana, explains, \u201cThere\u2019s a lot of data that will tell you if you have an issue with that system before it becomes a major problem that\u2019s going to leave the guy sitting on the side of the road.\u201d<\/p>\n For instance, he says, \u201cIf you know the starter lasts for two years, you can proactively put a new one on every two years and not worry about it breaking down at 25 months and causing unnecessary downtime.\u201d<\/p>\n But the next step brings in data beyond a fleet\u2019s own maintenance records.<\/p>\n The amount of data is key, explains Renaldo Adler, TMW Systems\u2019 principal of asset maintenance, fleets and service centers. The more historical information gathered, the better. A fleet of 1,000 trucks and lots of miles will have better information than a fleet with 20 or 30 trucks, he says.<\/p>\n That\u2019s why big data is the key behind TMW Systems\u2019 new predictive maintenance application, TMT Predict.Fault Code, the first tool to be offered under the company\u2019s new TMT Predict Series of maintenance analytics solutions.<\/p>\n The system uses predictive models created using data from 80,000 vehicles that have traveled over 1 million miles. The data was analyzed by a team of scientists to identify patterns that occur before a part fails.<\/p>\n \u201cOEMs are teaming up with us and finding hidden intelligence in maintenance expense, parts, labor, lost productivity,\u201d Leonard explains. \u201cSo we can, within a 90% threshold, tell you vehicles that are going to have problems in the next few months.\u201d Its research in developing the product discovered unreported fault codes, from low coolant to a serious problem with the antilock braking system.<\/p>\n While truck systems throw out hundreds of fault codes, Leonard says, traditional diagnostics systems don\u2019t analyze all the data. \u201cIn the old world, no one could look at the thousands of data elements being read,\u201d he explains. \u201cWe read the entire [CANbus] system, and match it to history of other trucks of those same miles. When you look at what we\u2019re trying to do, with several hundred terabytes of data, we can look at all aspects of the error codes in brand new ways.\u201d<\/p>\n Using the PeopleNet Mobile Gateway, TMW\u2019s new application gathers 80 performance variables from the engine and aftertreatment system. Those variables are then transmitted to the cloud, where they are fed into eight models, which were developed by Vusion (a sister Trimble company), and then analyzed for indicators of possible failures.<\/p>\n When fault codes and other vehicle data indicate an increased probability of failure, a dashboard alert appears within the user\u2019s TMT Fleet Maintenance software. The alert identifies the fleet\u2019s assigned equipment number, vehicle identification number, probability of failure, diagnostic trouble code and description, the performance variables triggering the probability, and other key information, so fleets can decide on a course of action.<\/p>\n TMW is not the only company working on better predictive maintenance tools. For instance, Navistar has been piloting a new OnCommand Connection feature called Live Action Plans, which predicts when a part is going to fail before it does. Live Action Plans uses prognostic models that were developed using Navistar\u2019s field service intelligence and algorithms based on big data analytics. The result is that when certain adverse conditions are identified on a vehicle, OnCommand Connection can give customer alerts about potential corrective actions, potential repair, the parts needed, and the training required to make the repair.<\/p>\n And maintenance isn\u2019t the only arena where predictive analytics is coming into play.<\/p>\n The Journal of Commerce reports that chassis leasing companies, academics, and technology providers are developing predictive analytics to forecast intermodal chassis demand, which could mean fewer chassis shortages at ports. And TMW is working on developing such systems for driver management and freight network management.<\/p>\n Omnitracs offers predictive analytics around driver safety and retention issues. These tools look at patterns in data associated with drivers \u2013 not just safe-driving data such as speed or hard stops, but also the hours they\u2019re working, are they happy in their work, hours of service, and more.<\/p>\n \u201cPredictive analytics starts to pull all these things together and then make recommendations based on the patterns,\u201d explains Brad Taylor, Omnitracs vice president of data\/Internet of Things, identifying drivers likely to have a crash or to quit the company and giving the fleet suggestions for how to address those issues in conversations with the drivers.<\/p>\n Taking predictive analytics a step further is the notion of prescriptive analytics, explains Jonathan May, director of business intelligence at McLeod Software.<\/p>\n \u201cPredictive analytics is looking at historical trends and starts predicting out based on a level of confidence what\u2019s going to happen,\u201d he explains. \u201cCustomers love that \u2014 \u2018Am I going to be able to buy some more trucks or trailers this year, or what\u2019s my projected cash flow?\u2019 Based on historical trends and where we are on a predictive model, it\u2019s going to give you that answer.<\/p>\n \u201cOnce you have that, the next thing on the spectrum is, how can we get there? What do we have to do? We\u2019re not there yet, but we\u2019re trying to get there.\u201d<\/p>\n Prescriptive analytics not only anticipates what will happen and when it will happen, but also suggests decision options to address the predicted issue and shows the implication of each decision option.<\/p>\n So in the area of maintenance, for example, predictive analytics may tell you that a certain component on a certain model of truck is likely to fail at a certain mileage. It\u2019s up to the fleet manager to decide what to do with that information. Prescriptive analytics will tell you what to do about it.<\/p>\n The same concept applies to areas such as freight contracts and equipment investment.<\/p>\n For instance, Ben Wiesen, vice president, products and services, Carrier Logistics Inc., points out that today\u2019s costing systems spit out a report telling you which customers you\u2019re losing money on, and it\u2019s up to the people at a carrier to decide to whether to \u201cfire\u201d that customer. \u201cTomorrow maybe what the systems will do is automatically start declining that customer\u2019s load tenders,\u201d he says.<\/p>\n May says McLeod is working on building a tool that would allow customers to ask questions such as, \u2018What would be the impact if I were to buy five more trucks next year?\u201d<\/p>\n \u201cI kind of picture it as a slider on a report,\u201d May says, which would then provide the expected impact on revenue, costs, lane coverage, and the like. \u201cThat\u2019s something we\u2018re looking at building in, that \u2018What if?\u2019 capability.\u201d<\/p>\n Chris Scharaswak, senior director of product development and innovation at Ryder, says many companies are pretty good at what he calls \u201cpublishing the news, which is, what does this data tell me, and creating reports. But our next step is to make sure we\u2019re accumulating all this data and it\u2019s normalized to a certain degree so we can do predictive and prescriptive analytics. We can predict based on trends and mathematical models if there\u2019s going to be a failure on a component on a tractor, or predict certain times of the year that weather patterns in the Northeast cause certain delays, or when capacity is going to tighten in certain lanes and certain kinds of industries. So it\u2019s those kind of things we can create predictive models around and then plan for.\u201d<\/p>\n Predictive analytics, he says, defines what could happen, based on historical and forecast data. \u201cPrescriptive really starts to define the best alternative solution to those central risks or the things that could happen. In certain industries, such as aerospace, they already have models like this. It\u2019s something we\u2019re definitely making investments in, and we know it will have huge impact on our ability to service our customers and mitigate risks and drive improvements in transportation networks and supply chains.\u201d<\/p>\n One emerging technology that tracks data in a different way may help drive deep learning and predictive analytics further.<\/p>\n Blockchain is a combination of technologies that allow transactions between parties via a trusted, shared ledger. Each transaction is coded into a block, which becomes part of a chain of \u201cblocks.\u201d Entries or changes to the chain cannot be made without authorization of all participating members.<\/p>\n TMW, which plans to unveil a blockchain product this year, says integrating blockchain into freight contracts opens up a host of data. Leonard explains that once you create a blockchain ledger, both shipper and carrier can track in real time the commitments the shipper made, when the carrier delivers the freight, and more, offering \u201ctotal transparency,\u201d he says.<\/p>\n TMW has been running tests of its blockchain programs with select carriers, such as Dart, and says it has cut down to seven days from 21 days the time it takes to execute a transaction. Leonard says carriers could be paid almost immediately through smart contracts and blockchain.<\/p>\n In addition, the analytics made possible by the data in the blockchain allows for patterns to be discerned that can help shippers and carriers during contract negotiations. Carriers could see in near real time whether freight was weak or strong coming into or out of given markets.<\/p>\n Currently, Leonard says, \u201cTrucking companies build contracts, get awards from shippers, and go to the next contract, and never keep those historical contracts,\u201d at least not in a way that makes them easy to analyze. \u201cIn the big data world you have historical contracts \u2013 \u2018for the last two years here\u2019s everything we\u2019ve done and bid on, and my analytics is telling me I am overpriced in this area, so can we work out a better deal.\u2019\u201d<\/p>\n TMW was a charter member of the Blockchain in Transportation Alliance, made up of members such as shippers, carriers, software and technology companies, is a forum for the development of blockchain technology standards and education for the freight industry.<\/p>\n A more recent BITA member is UPS. \u201cBlockchain has multiple applications in the logistics industry, especially related to supply chains, insurance, payments, audits and customs brokerage,\u201d said Linda Weakland, UPS director of enterprise architecture and innovation, in announcing UPS was joining the alliance. \u201cThe technology has the potential to increase transparency and efficiency among shippers, carriers, brokers, consumers, vendors and other supply chain stakeholders.\u201d<\/p>\n The increasing ability to share data via telematics and cloud computing means that data analytics no longer has to be a back-end office activity. Increasingly, deep learning, machine learning, and the beginning of artificial intelligence are going to allow data analytics in near real-time \u2013 in the vehicle.<\/p>\n Right now, telematics systems such as remote diagnostics or camera-based safety systems upload the data to some central hub for analysis. But with the help of machine learning and artificial intelligence, some say, new technologies will cut out the middleman and provide data analytics right on the truck, or \u201cout on the edge, the place of interaction.\u201d<\/p>\n Adam Kahn compares it to taking photos \u2013 at one time you had to send film out to be developed before you could see the picture. Today the photo is available immediately on your phone.<\/p>\n Kahn is vice president, fleet business, with Netradyne, which is applying edge analytics, deep learning and artificial intelligence concepts to in-cab, camera-based safety systems.<\/p>\n For instance, he explains, take hard braking, traditionally associated with risky driving. Instead of a fleet manager or a third party poring through hard-braking data and video to determine if a particular hard braking event was truly a problem, he explains, Netradyne\u2019s Driveri camera-based system uses multiple inputs, including both sensors and camera data, and can tell on the spot if a driver had a hard brake because he or she was stopping to avoid hitting a pedestrian who just stepped out in front of the truck, rather than from risky behaviors such as following too closely.<\/p>\n \u201cI think you\u2019ll see the devices close to the point of interaction will get smarter and faster and get data from other sources and do their own analytics,\u201d says Eric Witty, vice president of product management at PeopleNet. \u201cDrivers, mechanics and other people not in the office will not only get data, but they\u2019ll get better decision-making,\u201d whether it be warnings, or driving guidance or maintenance guidance.<\/p>\n Another example, Witty says, are lane departure warnings. Right now, if a truck starts wandering from the lane, the driver is likely to get a warning \u2014 perhaps a rumble strip sound in the cab. But if those warnings start coming closer and closer together, they may well be a sign of fatigue. Edge analytics, he says, combine LDW with other available data such as speed, and tell the driver he or she is becoming fatigued and should pull over to rest, as well as alert the back office.<\/p>\n As we discuss many of these emerging technologies, inside of trucking and out, we hear about terms such as deep learning, neural networks, machine learning, and artificial intelligence. But you don\u2019t have to understand these technical terms to benefit from them, says PeopleNet\u2019s Witty.<\/p>\n \u201cWhat it\u2019s really all about is, I don\u2019t have to know everything and do everything. I have to tell you what it is I\u2019m trying to figure out and the problems I\u2019m trying to solve, and the machine will figure it out and be able to provide guidance and the answer to the questions that you need, in near real time.\u201d<\/p>\n by Deborah Lockridge<\/a><\/p>\nEmerging and Future Fleet Data Technologies<\/h3>\n
<\/div>\nPredictive analytics<\/h3>\n
From predictive to prescriptive<\/h3>\n
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Blockchain links it all together<\/h3>\n
Analytics at the edge<\/h3>\n
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I\u00b4m\u00a0a Fleet Management expert, and the manager of\u00a0![\"\"](\"http:\/\/en.advancedfleetmanagementconsulting.com\/wp-content\/uploads\/2019\/02\/Cartel-Valencia-2019-1.jpg\")
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