Quick Transport Solutions Inc.

How Machine Learning And Big Data Will Shape The Future Of Trucking

Let’s face it, there’s a sea change underway. As digital technologies and advanced computing begin to reshape our world, trucking will not be immune to the evolution.

The fact is, things like machine learning are already shaping themselves through the daily applications of our lives. After all, your Netflix account recommends movies to you and Facebook can automatically tag our friends and family members.

The algorithms and statistical models used to run daily aspects of our lives have become so common that we mostly take them for granted. There are so many different ways that a number of different real-time analyses take place and discover patterns throughout our lives at all times. This is how we get instant recommendations in ads specifically targeted at us.

But what many don’t realize is that these technologies are finding their way into trucking. Fueled by an incessant need to analyze more and more data, fleet management systems have been evolving. Let’s take a deeper look at the future of this corner of our industry.

Its Not Skynet

Don’t worry, just because we are getting smarter at creating smarter machines, we aren’t in danger of inventing the next Terminator. When you think of machine learning, think on a smaller scale. The electronic payment systems we use are themselves versions of machine learning.

Imagine a truck driver using a company fuel card to purchase gas. What if said driver decides to buy premium instead of regular grade gas, or perhaps are fueling up at an expensive gas station.

Whereas you would previously have a number of number crunchers studying spreadsheets to find these patterns, now the back office can get real time alerts and actionable information on certain action items previously programmed into the system.

Using Big Data and Fuel

Almost a year ago a large operator of fuel card and payment systems formed a specific analytics team to find out how the company could create products and services based on analyzing the transaction data of said fuel purchases.

Today, the system captures millions of transactions every week. They track the data across a network of nationwide, local and specific OTR fleet card programs. They have proprietary systems that can manage and provide useful analysis on this data day-in and day-out.

The company has even gone so far as to incorporate machine learning into the exceptions that are found by their company algorithms. They can identify and share these results real-time with their customers.

Many systems now utilize online, web-based interactive dashboards that allow their users to monitor and control the flow of information coming into the back office. They can set up specific categories in these systems, such as:

  • Gallons purchases
  • Odometer readings
  • Miles per gallon readings
  • Non-regular fuel purchases
  • Vehicle tank capacity

When you combine such a large number of data sets, the information can be combined and analyzed in a number of different ways. You will be able to track and manage usage in a number of ways.

Telematics To the Rescue

By using telematics data and real time analysis, you can find mismatches between service station locations and the locations of your commercial motor vehicles when they are fueling up. Knowing the truck driver’s home location is also valuable.

Imagine algorithms that can determine if an operator took their vehicle home and then fueled up shortly thereafter. Telematics data can be used to determine where the vehicle was when the fuel purchase was made. In other words, if your employee used his company fuel card to fuel up his personal passenger vehicle, you’ll know.

It’s a brave new world out there, but by properly utilizing advances technologies to further the cause of your fleet’s operations, you can find efficiencies where there were none, and discover deficiencies where you knew none existed.

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[…] order to figure out how well your fleet is doing at meeting the goals, it will be important to collect the data and determine a method for analyzing it. You want at least 30 days of control data, regardless of […]

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