AI in Trucking: Digital Twins, Scenario Simulations, & Benchmarks

Even if your trucking business operates as usual, as a manager, you might wonder, “What if I chose different orders or sent my drivers through different routes?” There is always an opportunity for more efficiency and higher profits. How can you ensure that your business takes steps in the right direction?

Some technologies have been entering the scene to break the mold for a while now, and digital-twin-powered scenario planning is one of them.

Think of a digital twin as a virtual copy of a real-world object or system. For example, imagine a digital clone of your city — everything from roads and traffic patterns to your customer locations and delivery time windows. Then, you input truck speed, capacity, and fuel data to simulate different routes. Scenario planning tools can read this information and help you pinpoint the shortest or fastest routes based on congestion and schedules. Or, by analyzing different load combinations, it could help you maximize the capacity of each truck.

Does it sound like something you’d like to know more about? Here’s how it works and what you need to get started.

Trucking Digital Twins: The Ins and Outs

Digital twins help logistics planners create virtual versions of their processes, organizations, bottlenecks, and assets — and produce multiple simulations of how decisions could play out. 

Here’s a simplified breakdown of the steps to get them set up:

1. Data Collection: AI sensors gather real-time data from the physical object (like temperature, pressure, or location).
2. Digital Representation: This data is fed into a digital model, creating a virtual representation of the object.
3. Simulation: Digital twins use this data to simulate how the physical object behaves under different conditions.
4. Analysis: The virtual model is analyzed (often with integrated AI-powered predictive analytics) to identify potential issues, optimize performance, or predict future outcomes.
5. Feedback Loop: Insights from the digital twin are used to improve the physical object or system.

For example, trucking digital twins could simulate different driving conditions, helping maintenance teams identify potential mechanical issues. It does this by looking at your maintenance records, engine data, road terrain, and truck model. Logistics teams can then simulate different outcomes based on truck speed and driver behavior.

It could also help support logistics planners with route planning and driver preparation. By simulating the impacts of driver behavior on the routes available, scenario planning tools can help teams select the lowest-risk route or train drivers to adjust their driving style based on road conditions.

What These Systems Need From You

High-quality trucking digital twins hinge on a golden dataset to perfectly model logistics operations. The basic operational data consists of shipment details, inventory levels, vehicle specifics, and warehouse capacities. This data builds up the digital twin, making it a lifelike reflection.

In each case, some background gives context where necessary: a blend of geography (origins and destinations), customer data (previous orders and preferences), and financial information. Transportation networks, weather patterns, and other geopolitical conditions also influence logistics decisions. Optimizing operations and predicting future trends require understanding the intersections between these critical data points.

Finally, market and risk data give you strategic inferences. Logistics providers have the ability to discover potential challenges and opportunities as they review industry/market trends, historical incidents — and the controls in place for political impasses at ports. Together with all the details about operations, geography, and your customers, this gives you an authoritative data set to build reliable trucking digital twins.

Accurate Benchmarks for Better Performance

Let’s say you have a mediocre and a tremendous driver in your fleet. As a fleet manager, wouldn’t it be great if you could support your drivers to all be the best? With more insights into their driving behavior, you can.

Telematics and in-hub sensors can supply data on your team’s various driving styles. You can simulate and compare the driving techniques and see what each member can do to improve themselves to the best level. 

The same goes for sales agents and different truck brands. By benchmarking their strengths and capabilities, you could then simulate the optimum fleet or team performance to see what results could be achieved in different scenarios.

You might think, “This all sounds great, but I’m not sure how my team will feel about the level of surveillance.” Let the data be anonymous, and send the reports and training recommendations directly to the individuals. A holistic view of improvements and the percentage of drivers implementing their training will help you identify the success rate.

AI tools are quickly becoming logistics planners’ best copilots. With proper oversight, good data, and strategic simulation creations, trucking digital twins can significantly reduce some of your biggest burdens.