{"id":2122,"date":"2019-07-16T17:18:59","date_gmt":"2019-07-16T17:18:59","guid":{"rendered":"https:\/\/quicktransportsolutions.com\/blog\/?p=2122"},"modified":"2019-07-16T17:19:01","modified_gmt":"2019-07-16T17:19:01","slug":"how-machine-learning-is-going-to-completely-reshape-the-trucking-industry","status":"publish","type":"post","link":"https:\/\/quicktransportsolutions.com\/blog\/how-machine-learning-is-going-to-completely-reshape-the-trucking-industry\/","title":{"rendered":"How Machine Learning is Going to Completely Reshape the Trucking Industry"},"content":{"rendered":"\n<p>You know how much we love being at the forefront of technological reporting here at the <a href=\"https:\/\/quicktransportsolutions.com\/blog\/an-intro-into-preventative-maintenance-for-trucking-companies\/\">QuickTSI Blog<\/a>, and why not? The future of trucking is increasingly technological in nature. Whether it be using semi-autonomous technology, advanced safety systems, online training for new truck drivers, or otherwise, the trucking and transportation sector simply cannot escape it. <\/p>\n\n\n\n<p>                                  [wp_ad_camp_2]                      <\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>Did you know that the world generates <a href=\"https:\/\/www.socialmediatoday.com\/news\/how-much-data-is-generated-every-minute-infographic-1\/525692\/\">2.5\nquintillion bytes<\/a> of data every single day? That is a mind-boggling number!\nOne of the sectors that will only further the rise of this number is\ntransportation. Telematics and connected cars continue to be an increasing part\nof the sources that generate this massive onslaught of data. It is expected\nthat by 2020, 10% of all data generated will come from vehicles that have\nembedded technologies. <\/p>\n\n\n\n<p>By the same time, it is expected that there will be 152\nmillion connected cars on the road and nearly 2 billion by 2025. One of the\nreasons for this massive increase in data is because of telematics. Telematics\nmonitors both vehicles and drivers and collects a wealth of data, such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Vehicle location<\/li><li>Vehicle status<\/li><li>Fuel usage<\/li><li>Oil pressure<\/li><li>Engine temperature<\/li><li>Trip duration<\/li><li>Odometer reading<\/li><li>\u2026 and more<\/li><\/ul>\n\n\n\n<p>Yet, even though this seems like a huge amount of data, it\nis really just the tip of the iceberg. As vehicle technology continues to\nevolve, the types of data being collected will evolve with it. The question for\nfleets is: What do we do with all this data? <\/p>\n\n\n\n<p>As we have talked about before, data is only useful if you\nknow what to do with it. In fact, data for data\u2019s sake can hamper a company,\noverloading the back office and confusing fleet managers. Motor carriers must\nutilize effective reporting to prioritize and analyze the data they are\ncollecting. <\/p>\n\n\n\n<p>How Reporting Impacts the Data<\/p>\n\n\n\n<p>Telematics reporting has traditionally meant that a\nconnected device collects vehicle and driver data then transmits it back to a\ndata collection point. The fleet can set up specific categories that the data\ncan be grouped into, whether it be the average speed of a vehicle or daily\nvehicle movements. Data can also be assigned to a specific truck driver and\nthen sent on to the fleet manager for review. <\/p>\n\n\n\n<p>Normally, a person will sift through and make sense of all that data. Unfortunately, this is a very time-consuming and error-prone way to sort through and evaluate large amounts of data. And even after the data has been put into specific categories, extra care needs to be put into retrieving the data when it is required. <\/p>\n\n\n\n<p>                                  [wp_ad_camp_2]                      <\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>Hand-managed data is also often quite goals-based. This\nmeans that all available data is collected and sent on to determine whether the\nfleet\u2019s metrics are being met. This is also a scenario where it is up to the\nfleet manager to decide what data is applicable and not applicable, yet again\nputting them in a situation where simple human error could throw a wrench into\nthe mix. With machine learning, however, that paradigm is changing. <\/p>\n\n\n\n<p><strong>Machine Learning\nChanges Data Reporting<\/strong><\/p>\n\n\n\n<p>We will eventually move to a system where it will not be all\nup to the fleet manager to collate and decide on what the right data is.\nAdvances in artificial technology and machine learning will vastly improve how\ndata is gathered and acted upon. The research processes behind machine learning\nand implementation are intimidating, but the concepts are quite simple. Even\nmore, companies are stepping into the void to ensure traditional industries\nlike trucking understand how to use these new, advanced tools. <\/p>\n\n\n\n<p>You may be asking yourself: What is the difference between\nartificial intelligence and machine learning? It is helpful to consider that\nyou cannot have one without the other. Machine learning is a core component of\nartificial intelligence. It is what gives the AI the ability to learn and\nimprove from experience, which is also a critical component of human evolution.\nMachine learning allows an AI to act on new data without having to be\nprogrammed to do so. <\/p>\n\n\n\n<p>AIs use machine learning to search for patterns in data,\nwhich then influences their future decisions. The more an AI analyzes and\nobserves the data it is receiving the smarter it becomes. Now, take that\nconcept and apply it to the manual job that fleet managers must do to ensure\ndata integrity. Obviously, a machine could analyze the data, find patterns, and\nlearn from it much faster. <\/p>\n\n\n\n<p>Machine learning will also open new avenues in reporting and\ndata management. Fleet managers will be able to focus on the most important\ndata for the company\u2019s needs. Motor carriers using these technologies will be\nable to see only the data that is most necessary for their needs. As the\ntechnology absorbs more data, metrics will need to be changed, but the overall\ndata development cycle will continue. <\/p>\n\n\n\n<p><strong>How Machine Learning\nWill Impact Maintenance<\/strong><\/p>\n\n\n\n<p>One of the most critical aspects of operating an efficient\nfleet is ensuring the fleet is maintained. <a href=\"https:\/\/quicktransportsolutions.com\/blog\/an-intro-into-preventative-maintenance-for-trucking-companies\/\">Predictive\nand preventative maintenance<\/a> are at the core of those efforts. Technologies\nutilizing machine learning can measure precise changes in a vehicle\u2019s engine\nand then notify an individual or designate a maintenance schedule. This can be\ndone before a serious breakdown occurs. <\/p>\n\n\n\n<p>Unexpected engine, or other technical failures, can cripple\na fleet and seriously cut into the bottom line. With predictive analytics tied\ninto machine learning, fleets can reduce downtime without seriously impacting\nmanpower or established processes. Shop technicians will no longer have to rely\nsolely on a schedule for maintenance. The problem with scheduled maintenance is\nthat parts may be ordered that don\u2019t end up being needed, which increases\ncosts.<\/p>\n\n\n\n<p>With integrated telematics incorporated with machine\nlearning, maintenance can be completed when it is needed based on the parts\navailable. With systems actively monitoring when a particular part is about to\ngo out or is in need of active maintenance, the guesswork is removed from the\nequation. <\/p>\n\n\n\n<p>Utilizing AI and machine learning for maintenance provides\nadditional benefits, such as finding a remaining list of vehicles, determining\nmaintenance \u201cwhat if\u201d scenarios. This will go even farther by allowing\ntechnicians to better maximize vehicle life by precisely handling maintenance\nneeds on-the-spot. <\/p>\n\n\n\n<p>All this is done through the ability to use the truck driver\nand vehicle to collect environmental data and combine it with historical facts,\nwhich allows for pattern recognition. This is called predictive analytics. And\nwhile predictive analytics does not definitively tell you what will happen in\nthe future, it can accurately forecast future possibilities. The potential use\nfor predictive data analytics is huge. <\/p>\n\n\n\n<p><strong>The Myriad Ways\nPredictive Analytics Helps Fleets<\/strong><\/p>\n\n\n\n<p>It isn\u2019t just a maintenance failure that can cause serious\ncost problems for a fleet. Predictive analytics combined with machine learning\ncan also help reduce overall fuel consumption. One example of how it does this\nis through monitoring tire pressure. When the tire pressure on a vehicle drops\nbelow a predetermined level, the shop can be notified. <\/p>\n\n\n\n<p>AI-driven predictive analytics can also help determine what\nthe best routes are. Whether it be to avoid high-traffic areas, construction,\nor roads or highways that are not properly maintained, all this helps to\nimprove fuel efficiency and prevent waste. Even such factors as how much load\nis suitable for a particular vehicle or what behaviors truck drivers are\nexhibiting that waste fuel can have a big impact. <\/p>\n\n\n\n<p>Machine learning can also help fleets paint a clearer\npicture of dangerous truck driver behaviors. Whether it be through excessive\nspeeding or harsh acceleration, there is a risk posed to fleet safety when truck\ndrivers don\u2019t follow the rules of the road or the rules of the fleet. <\/p>\n\n\n\n<p>Using the staggering amounts of data collected, motor\ncarriers can predict future truck driver behavior. If truck drivers exhibited a\ncertain behavior in the past, analytics allows fleet managers to give the\nappropriate level of training or corrective coaching. The goal is to improve how\nthe truck driver operates the vehicle, rather than punitively punish them. One\nor two infractions does not make a truck driver a threat to fleet safety.\nAnalyzing the data allows fleet managers to make the proper determination. <\/p>\n\n\n\n<p>Consider that punishing a truck driver or sending them to training over something that may or may not be inherently their fault would be both ineffective and demotivating. Machine learning and predictive analytics can mitigate this potentially disastrous result. <\/p>\n\n\n\n<p>                                  [wp_ad_camp_2]                      <\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>The fact is this: We are entering a future where artificial\nintelligence and machine learning will open new avenues to addressing some of\nthe most vexing problems fleets deal with. A greater variety of analytics and\ndata processing tools only makes fleet stronger. As more companies move towards\nmodels that allow them to process data into actionable chunks faster and more\nefficiently, those not on the technology bandwagon will likely be left behind\nin a competitive marketplace. Will that be you?<\/p>\n","protected":false},"excerpt":{"rendered":"<p>You know how much we love being at the forefront of technological reporting here at the QuickTSI Blog, and why not? The future of trucking is increasingly technological in nature. Whether it be using semi-autonomous technology, advanced safety systems, online training for new truck drivers, or otherwise, the trucking and transportation sector simply cannot escape &#8230; <a title=\"How Machine Learning is Going to Completely Reshape the Trucking Industry\" class=\"read-more\" href=\"https:\/\/quicktransportsolutions.com\/blog\/how-machine-learning-is-going-to-completely-reshape-the-trucking-industry\/\">Read more<span class=\"screen-reader-text\">How Machine Learning is Going to Completely Reshape the Trucking Industry<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":2123,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2122","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-trucking"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.4.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How Machine Learning is Going to Completely Reshape the Trucking Industry - Quick Transport Solutions Trucking Blog<\/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:\/\/quicktransportsolutions.com\/blog\/how-machine-learning-is-going-to-completely-reshape-the-trucking-industry\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Machine Learning is Going to Completely Reshape the Trucking Industry - Quick Transport Solutions Trucking Blog\" \/>\n<meta property=\"og:description\" content=\"You know how much we love being at the forefront of technological reporting here at the QuickTSI Blog, and why not? 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