For the trucking industry, operational costs have become the most urgent issue of 2024, even more so than issues around driver shortages and driver retention. That’s because while demand has dropped and rates have plummeted, costs have risen significantly since 2022.
As reported by the American Transportation Research Institute (ATRI), every cost element has increased over the past two years, including diesel prices, insurance premiums, driver rates, and trailer and truck payments. Operating costs increased beyond $2.00 per mile for the first time ever in 2022. This trend continued in 2023, with the total marginal cost of operating a truck rising to $2.27 per mile, marking a new record-high cost. At the same time, the average spot rate for a dry van was $2.02 per mile, meaning that trucking companies would lose $0.25 per mile to haul a dry van load at spot rates.
These high costs have placed a significant burden on the operations of trucking companies, challenging their financial sustainability over the last two years. As a result, 2023 saw approximately 8,000 brokers and 88,000 trucking companies cease operations, including some marquee names, such as Yellow Corp. and Convoy, and decades-long businesses, such as Matheson Trucking and Arnold Transportation Services.
More so than ever before, trucking companies need to get better at efficiently using their assets and reducing operational costs. So, what is a trucking company to do? Technology is the answer! Given the nature of the problem, technology-led innovation will be critical to ensure companies can balance rising costs through efficient operations.
One technology that could be the answer to many of the trucking industry’s issues is the concept of digital twins. A digital twin is a virtual model of a real system and simulates the physical state and behavior of the real system. As the physical system changes state, the digital twin keeps up with the real-world changes and provides predictive and decision-making capabilities built on top of the digital model.
DHL, in a 2023 white paper, suggests that—due to the maturation of technologies such as the internet of things (IoT), cloud computing, artificial intelligence (AI), advanced software engineering paradigms, and virtual reality—digital twins have “come of age” and are now viable across multiple sectors, including transportation. We agree with this assessment and believe that digital twins are essential to radically improving the processes of fleet planning and dispatch.
The need to automate
Outside of attaining procurement efficiencies, trucking companies can achieve lower costs by focusing on critical operational levers such as minimizing deadheads, reducing driver dwell time, and maximizing driver and asset utilization.
However, manual methods of planning and dispatch cannot optimally balance these levers to achieve efficiency and cost control. Even when planners work very hard and owners strive to improve processes, optimizing fleet planning is not a problem humans can solve routinely. Planning is a computationally intensive activity. To achieve fleet-level efficiencies, the planner has to consider all possible truck-to-load combinations in real time and solve for many operational constraints such as driver’s hours of service, customer windows, and driver home time, to name just a few. These computations become even more complex when you add in the dynamic nature of real-world conditions such as trucks getting stuck in traffic or breaking down or orders getting delayed. This is not a task humans do best! For these sorts of tasks, technology has the upper hand.
When a company creates a digital twin of its trucking network, it has a real-time model that factors in truck locations, driver hours of services, and loads being executed and planned. Planners can then use this digital model to assess possible decisions and select ones that increase asset utilization, improve customer and driver satisfaction, and lower costs.
For example, a digital twin of the network can offer significant insights and analysis on the state of the network, including exceptions such as delayed pickups and deliveries, unassigned loads, and trucks needing assignments. Backed by AI that takes business rules into account, digital twins can allow companies to optimize their fleet performance by finding the most efficient load assignments and dynamically adjusting in real time to changes in traffic patterns and weather, customer delays, truck issues, and so on.
With a digital twin, carriers can optimize the matching of assets, drivers, and freight. Typically, an investment in this innovative technology results in a 20%+ increase in productive miles per truck, as well as improving driver pay and significantly decreasing driver churn. Drivers get paid by the miles they run, so when they run more, they are able to make more money, resulting in less need to chase the next job in search of better pay.
Additional benefits
Digital twins also combat deadheading, another source of driver dissatisfaction and cost inefficiencies. On average, over-the-road drivers spend 17%–20% of road miles driving empty. Using a digital twin, a company can search across several freight sources to find a load that perfectly matches the deadhead leg without impacting downstream commitments. These additional revenue miles will help drivers to maximize their earnings on the road and carriers to maximize their asset utilization and profitability.
The traditional manual dispatch planning model is becoming increasingly outdated—each planner and fleet manager tasked with overseeing 30 to 40 vehicles. Carriers try to manage this problem by dividing the fleet into manageable chunks, which results in cross-fleet inefficiencies. Such a system isn’t scalable. A digital twin acts as an equalizer for small and mid-sized fleets. It enables carriers to expand by venturing beyond the fixed routes and network they were forced to run out of fear of additional logistical complexity.
A digital twin can also give an organization the transparency and visibility it needs to find and fix inefficiencies. A successful carrier will leverage the technology to learn from the hitches in its operations. While this visibility is beneficial in its own right, it also provides the first step towards a seamless, digitized operation. “Digital revolution” is a buzzword frequently heard at transportation conferences. Yet not too many organizations are dedicated to digitizing their operations past the visibility stage. The end goal should be using decision support systems to automate key elements of the system, thus freeing up planners from their daily rote tasks to focus on problems that only humans can solve.
Finally incorporating a digital twin can also help trucking companies work toward the broader trend of creating greener supply chains. Because they have lower deadhead and dwell times, trucking companies that have adopted a digital twin can be more attractive to shippers that are looking for more efficient operations that meet their environmental, social, and governance (ESG) goals.
The future is here
It is important to note that the benefits described here are not dreams for the future, digital twin technology is already here. In fact, choosing a digital twin can seem daunting because there are already a spectrum of options out there. First and foremost, an organization must ensure that the digital twin it selects aligns with both the goals and the scope of its operation.
Additionally, the ideal digital twin should:
- Operate in near real time. A digital twin should be able to refresh as often as the network changes.
- Be able to factor in specific customer delivery requirements as well as asset- and operator-specific constraints.
- Be computationally efficient and comprehensive as it considers thousands of permutations in milliseconds. The digital twin should be able to reoptimize an entire fleet’s schedule of multi-day routes on the fly.
Before implementing a digital twin, carriers need to make sure that they have robust data management processes in place. Electronic logging devices (ELDs), customers tenders, billing, shipments, and so on are already inundating carriers with a glut of data. However, the manual nature of operations in many carriers leads to poor data quality. Carriers will need to invest in data management approaches to improve data quality to support the generation and use of high-fidelity digital twins. Otherwise, the digital twin will not be representative of reality and companies will run into an issue of “garbage in, garbage out.”
Reinvention and transformation
While data management is critical, change management through the ranks of dispatch operations is often a harder task. In fact, the largest roadblock carriers face when undergoing a digital transformation is the lack of willingness to change, not the technology itself. Many carriers cling to outmoded planning methods. Planners, used to operating based on well-worn business rules and tribal knowledge, could be wary of the technology and resistant to change. They may need to be assured that, while it is true that every trucking network is uniquely complex, digital twins can be set up to model the intricacies of their specific dispatch operations and drive value to the network. A significant amount of time and resources will need to be expended on change management. Otherwise even though trucking companies may invest in cutting-edge technology, they won't be able to fully capitalize on the added value it can provide.
As the truckload industry works through the current freight cycle, it is important to realize that change is inevitable. Carriers will need to reinvent their operations and invest in technologies to ride through the busts and booms of future freight cycles. Recent global events point to the many ways that wrenches can be thrown into global transportation networks, and the fact that such volatility is here to stay. Digital twins can provide companies with the visibility to navigate such changes. But above all, an operation that uses the digital twin to drive decisions can make customers and drivers happy, and help the carriers keep their heads above water during times such as now.
