Artificial Intelligence (AI) and machine learning concepts have been around since the 1950s, but they have not seen the concepts reach fruition as they have in the modern age. With today\u2019s computing power and huge amounts of data at your fingertips, the potential of AI has evolved by leaps and bounds. Even the concept itself has become less complicated.<\/p>\n\n\n\n
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Remember the days when AI concepts revolved around neural\nnets and fancy algorithms? While those days are still with us, now we can have\nan AI in a children\u2019s toy. The knowledge required to take advantage of AI is\nborn out in everyday market applications. And since the concept of artificial\nintelligence is simply the ability of a machine to mimic a human, almost\nanything is possible. <\/p>\n\n\n\n
Digging Deeper into AI<\/strong><\/p>\n\n\n\n Artificial Intelligence is not, in itself, intelligence. It\nreally represents a set of capabilities a computer needs to do something a\nhuman would do. It could also supplant processes it would take too long for a\nhuman to do. Whether it be conducting a thorough analysis of a complicated\nprocess or operate rows of robotic machines, an AI can do it in a fraction of\nthe time. This frees up time for people to do higher level activities. <\/p>\n\n\n\n An AI is essentially a purpose-built brain of a particular\nstrength. It can be designed to learn and conduct specific tasks and learn over\ntime through the integration of multiple data sources. This is where the term\n\u201cmachine learning\u201d comes in. Still, there are a lot of limitations to AI. AI\ncannot adequately equal a human by any measure. <\/p>\n\n\n\n Nor is AI a fix-all for the world\u2019s problems. It is driven\nmainly by the quality of the data it is trained upon. If an AI is provided\nexcellent, workable training data, it will product strong results. Still, it is\nnot a push button, turnkey solution. It requires some input. There is also a\nmisconception out there that artificial intelligence removes the need for human\ninvolvement. <\/p>\n\n\n\n Most AIs in development require a significant amount of\ntraining. The human knowledge, whatever it may be, needs to be digitized and\nplaced into a format the machine can understand. The machine actually depends\nquite a bit on a person playing a role. The question now is what kind of role\nAI technologies can play in the trucking sector. <\/p>\n\n\n\n Customer Interaction Using AI<\/strong><\/p>\n\n\n\n Consider AI for common back office tasks. Mundane activities\nsuch as handling customer email requests can be quickly and easily handled\nusing AI-enhanced technologies. Simple queries regarding the location of a load\ncan be answered using AI. When the system knows the intent of the question in\nreal-time, it can formulate a fast reply. <\/p>\n\n\n\n Fast, accurate answers regarding load locations creates a\nbetter customer experience between the trucking companies and shippers who hire\nthem. It also creates a better work environment by removing mundane tasks that\nhumans no longer must do. The humans in the situation can then be proactive and\nwork through other parts of the customer relationship. <\/p>\n\n\n\n Another way innovative trucking companies use AI is for\ndetermining timeframes. Whether it comes to route planning or answering a quick\nquestion over email, AI systems can make assessments based on human activity.\nThey can also analyze recorded calls to determine what successful sales reps\ndo. When sales reps spend more time discussing market insights with those they\nare selling, they find that sales happen faster and easier. <\/p>\n\n\n\n Creating Greater Efficiencies<\/strong><\/p>\n\n\n\n AI systems can also assist in creating greater efficiencies\nwithin organizations. AI systems offer greater potential for load matching.\nAutomated load-matching apps use AI systems to ensure loads are matched\n\u201csmarter\u201d than the last loads. Companies are even coming out with \u201cBook Now\u201d\noptions for certain load boards to match carriers with freight. <\/p>\n\n\n\n AI can also be used to help shippers provide better loads to third-party carriers. The machine learning algorithms are used by adding feedback on the surface loads customers prefer. The feedback is used to improve decision-making within the machine. When a load is matched, it should be the most relevant, affordable, and of course fit the wheelhouse on offer. <\/p>\n\n\n\n [wp_ad_camp_2] <\/p>\n\n\n\n <\/p>\n\n\n\n Logistics processes can also be sped up using AI systems.\nThink about the amount of processing that goes into analyzing traffic patterns.\nMachine learning can be used to help route optimization by better planning for\ndelays or known hazards. Whether it be through logistics, load matching, or\nroute optimization, machine learning is becoming the norm for creating greater\norganizational efficiencies.<\/p>\n\n\n\n AI and Predictive Maintenance<\/strong><\/p>\n\n\n\n We spend a lot of time talking about predictive maintenance\nbecause we know how important it is. To keep costs down and safety up,\npreventative maintenance must be an important part of any strategy. AI systems\ncan help fortify and enhance your preventative maintenance program. How?\nBecause a lot of work goes into preventative maintenance as it currently\nstands. <\/p>\n\n\n\n Information fed into an AI system can be used to help\ndetermine when a maintenance event is about to occur. AIs can be trained to\nlook into the future and, depending on the data being provided, predict the\nlikelihood of anything from a blowout to a dead battery. What makes this kind\nof routine tracking is the size of a fleet. <\/p>\n\n\n\n It is fairly easy for small fleets to track such\ninformation, but larger fleets face a more daunting task as the number of units\nincreases. But when you have an AI backing up your decisions and keeping a\nbird\u2019s eye view on the important aspects of your fleet, it becomes easier to\ntrack each vehicle. The AI gets more accurate over time and can manage your\nunits on a per-truck basis. <\/p>\n\n\n\n AI-driven predictive maintenance systems will only get\nbetter as the vast numbers of sensors on trucks proliferates even further.\nRight now, predictive maintenance tells you when the truck breaks down.\nAI-driven systems give you much more than just when. It also gives you why and\nhow. <\/p>\n\n\n\n Still, there must be a lot of integration before AI-driven\npredictive maintenance goes from art to science. From fleet management systems\nto telematics providers and remote diagnostics platforms \u2013 there must be a lot\nof interoperability to get everything to work together. Quality vehicle data\nmust be combined with accurate maintenance records. Predictive analytics take\nthe guesswork out of ensuring your tractors are in good shape.<\/p>\n\n\n\n Better Safety Outcomes<\/strong><\/p>\n\n\n\n Perhaps the most important part of AI-driven systems is\ntheir potential to impact safety. In-cab camera systems have opened up a lot of\npossibilities where AI is concerned. An AI system could quickly capture what\nthe camera is seeing it and analyze it in real-time. Alerts can be fed to the\ndriver or back to the home office depending on what the AI sees. <\/p>\n\n\n\n AI is particularly good at detection and classification. Computer\nalgorithms analyze through automatic detection certain behaviors and objects.\nMachine vision acts as a set of smart eyes, which helps remove human error or\nbias from what may need to be quick decisions. It also prevents fleet managers\nfrom having to spend hours manually reviewing reams of video. <\/p>\n\n\n\n On-board AI can be used to detect all sorts of mistakes\ntruck drivers make, from lane departures to following too closely. OEMs are\neven building AIs that can help truck drivers stay awake or keep from being\ndistracted while behind the wheel. <\/p>\n\n\n\n Given enough data training, AI systems can become quite\nproficient in determining unsafe situations and alerting the truck driver or\nflee manager. Machine learning can increasingly discern a car from a tree from\na person. Quick decisions are important when a truck driver has to make split\ndecisions. <\/p>\n\n\n\n The holy grail of any trucking organization is productivity and AI is now being used to address that. From driver-assisted systems to adaptive cruise control and on-board assistance, AI systems are becoming evermore complex. <\/p>\n\n\n\n [wp_ad_camp_2] <\/p>\n\n\n\n <\/p>\n\n\n\n In the end, AI-driven systems will allow trucking companies\nto make better decisions, plan better routes, lower overhead costs, and drive\ngreater efficiency. Trucking companies will only continue adopting artificial\nintelligence. <\/p>\n","protected":false},"excerpt":{"rendered":"