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’s 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.
Remember the days when AI concepts revolved around neural nets and fancy algorithms? While those days are still with us, now we can have an AI in a children’s toy. The knowledge required to take advantage of AI is born out in everyday market applications. And since the concept of artificial intelligence is simply the ability of a machine to mimic a human, almost anything is possible.
Digging Deeper into AI
Artificial Intelligence is not, in itself, intelligence. It really represents a set of capabilities a computer needs to do something a human would do. It could also supplant processes it would take too long for a human to do. Whether it be conducting a thorough analysis of a complicated process or operate rows of robotic machines, an AI can do it in a fraction of the time. This frees up time for people to do higher level activities.
An AI is essentially a purpose-built brain of a particular strength. It can be designed to learn and conduct specific tasks and learn over time through the integration of multiple data sources. This is where the term “machine learning” comes in. Still, there are a lot of limitations to AI. AI cannot adequately equal a human by any measure.
Nor is AI a fix-all for the world’s problems. It is driven mainly by the quality of the data it is trained upon. If an AI is provided excellent, workable training data, it will product strong results. Still, it is not a push button, turnkey solution. It requires some input. There is also a misconception out there that artificial intelligence removes the need for human involvement.
Most AIs in development require a significant amount of training. The human knowledge, whatever it may be, needs to be digitized and placed into a format the machine can understand. The machine actually depends quite a bit on a person playing a role. The question now is what kind of role AI technologies can play in the trucking sector.
Customer Interaction Using AI
Consider AI for common back office tasks. Mundane activities such as handling customer email requests can be quickly and easily handled using AI-enhanced technologies. Simple queries regarding the location of a load can be answered using AI. When the system knows the intent of the question in real-time, it can formulate a fast reply.
Fast, accurate answers regarding load locations creates a better customer experience between the trucking companies and shippers who hire them. It also creates a better work environment by removing mundane tasks that humans no longer must do. The humans in the situation can then be proactive and work through other parts of the customer relationship.
Another way innovative trucking companies use AI is for determining timeframes. Whether it comes to route planning or answering a quick question over email, AI systems can make assessments based on human activity. They can also analyze recorded calls to determine what successful sales reps do. When sales reps spend more time discussing market insights with those they are selling, they find that sales happen faster and easier.
Creating Greater Efficiencies
AI systems can also assist in creating greater efficiencies within organizations. AI systems offer greater potential for load matching. Automated load-matching apps use AI systems to ensure loads are matched “smarter” than the last loads. Companies are even coming out with “Book Now” options for certain load boards to match carriers with freight.
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.
Logistics processes can also be sped up using AI systems. Think about the amount of processing that goes into analyzing traffic patterns. Machine learning can be used to help route optimization by better planning for delays or known hazards. Whether it be through logistics, load matching, or route optimization, machine learning is becoming the norm for creating greater organizational efficiencies.
AI and Predictive Maintenance
We spend a lot of time talking about predictive maintenance because we know how important it is. To keep costs down and safety up, preventative maintenance must be an important part of any strategy. AI systems can help fortify and enhance your preventative maintenance program. How? Because a lot of work goes into preventative maintenance as it currently stands.
Information fed into an AI system can be used to help determine when a maintenance event is about to occur. AIs can be trained to look into the future and, depending on the data being provided, predict the likelihood of anything from a blowout to a dead battery. What makes this kind of routine tracking is the size of a fleet.
It is fairly easy for small fleets to track such information, but larger fleets face a more daunting task as the number of units increases. But when you have an AI backing up your decisions and keeping a bird’s eye view on the important aspects of your fleet, it becomes easier to track each vehicle. The AI gets more accurate over time and can manage your units on a per-truck basis.
AI-driven predictive maintenance systems will only get better as the vast numbers of sensors on trucks proliferates even further. Right now, predictive maintenance tells you when the truck breaks down. AI-driven systems give you much more than just when. It also gives you why and how.
Still, there must be a lot of integration before AI-driven predictive maintenance goes from art to science. From fleet management systems to telematics providers and remote diagnostics platforms – there must be a lot of interoperability to get everything to work together. Quality vehicle data must be combined with accurate maintenance records. Predictive analytics take the guesswork out of ensuring your tractors are in good shape.
Better Safety Outcomes
Perhaps the most important part of AI-driven systems is their potential to impact safety. In-cab camera systems have opened up a lot of possibilities where AI is concerned. An AI system could quickly capture what the camera is seeing it and analyze it in real-time. Alerts can be fed to the driver or back to the home office depending on what the AI sees.
AI is particularly good at detection and classification. Computer algorithms analyze through automatic detection certain behaviors and objects. Machine vision acts as a set of smart eyes, which helps remove human error or bias from what may need to be quick decisions. It also prevents fleet managers from having to spend hours manually reviewing reams of video.
On-board AI can be used to detect all sorts of mistakes truck drivers make, from lane departures to following too closely. OEMs are even building AIs that can help truck drivers stay awake or keep from being distracted while behind the wheel.
Given enough data training, AI systems can become quite proficient in determining unsafe situations and alerting the truck driver or flee manager. Machine learning can increasingly discern a car from a tree from a person. Quick decisions are important when a truck driver has to make split decisions.
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.
In the end, AI-driven systems will allow trucking companies to make better decisions, plan better routes, lower overhead costs, and drive greater efficiency. Trucking companies will only continue adopting artificial intelligence.