One of the critical responsibilities of a dispatcher is to uncover and book good freight opportunities for carriers. Whether they’re scouring load boards for hours on end, calling up their broker contacts, or leveraging existing relationships with local shippers, dispatchers are always wheelin’ and dealin’ to keep their drivers moving. And if that weren’t enough, finding freight is only a fraction of their responsibilities, exponentially so if they’re an owner-operator. This is the way it’s been done for decades across carriers of all sizes, and, with seemingly no change in sight, many see this as the way it will be done for years to come.
Enter machine learning.
The technical definition of machine learning is the use of computer systems, algorithms, and statistical models to infer and predict outcomes from large sets of data. But what does that mean in a practical sense, and how can it help dispatchers today?
Dispatchers naturally look for freight opportunities that meet specific sets of criteria for their drivers. What lanes or corridors do they prefer? How do they get home without deadheading the truck? Which docks are the easiest to pick up from? Or drop-off to? There are seemingly countless variables that factor into what loads are ultimately booked, and maximizing carrier growth and profitability is one variable that is often preceded by other priorities.
This is where the power of machine learning truly shines. Xpress Technologies has built a freight platform and carrier community with machine learning at its core. The Xpress Technologies model takes all the criteria that dispatchers and owner-operators consider—learning from each of the micro-decisions they are making when searching for good freight—and suggests freight opportunities that match those criteria and maximize profitability.
Machine learning takes the guessing out of booking good freight and repeats this to every carrier in the community. It acts as the unseen dispatcher that’s truly changing the logistics industry.
Dr. Nathan Cahill, Director of Machine Learning at Xpress Technologies, is leading the effort to bring these technological advances to the transportation industry. He says, ”The truckload segment of the American transportation industry is an $800B data science problem that is being solved presently by free-market forces and largely outdated technology. Distributed cloud solutions along with machine learning can generate efficiencies that more fully leverage the network, reducing the cost to transport goods and unnecessary carbon emissions simultaneously.”
Aside from images of robots falling over repeatedly or expensive robot dogs walking around, many are unaware of what machine learning truly means or the impact it has on the logistics industry. More often than not, complex technology is presented as some futuristic, impractical idea that’s only available to the largest and wealthiest among us. In the case of machine learning within logistics, finding freight through Xpress Technologies is a perfect way to illustrate how powerful this concept can be for any carrier, regardless of size or wealth.
There are other practical examples of machine learning woven within daily life. Netflix and YouTube recommend videos based on viewing likes and dislikes. Amazon and other retailers recommend additional products based on purchasing habits. These recommendations are based on specific preferences, habits, likes, and dislikes that can each be considered points of data. Each of these data points informs a machine learning model that outputs relevant, personalized recommendations for everyone in that community.
What’s unique about the machine learning built into the Xpress Technologies freight platform is that it is designed to maximize independent carrier profitability and to bring efficiencies to the role of dispatching, not to recommend something that is consumed or purchased. Until now, the practical business use of machine learning has primarily been available only to the largest and wealthiest in the logistics industry. Xpress Technologies is reimagining the freight marketplace by providing new opportunities to carriers and shippers, large and small, democratizing technologies—like machine learning—and is building a logistics community that recognizes the importance of hard work, grit, and loyalty.
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