INFORM showcases how artificial intelligence technology can help quarrying and aggregates operations tackle the driver shortage and support their supply chain.
The construction supply chain depends on the timely delivery of heavy and time-sensitive materials such as ready-mix, aggregates, and asphalt. Yet across many regions, the logistics sector faces an acute and growing shortage of qualified truck drivers. This shortfall is no longer a distant concern. It directly impacts project timelines, cost structures, and overall efficiency within our industry.
The Reality of the Driver Shortage
The driver shortage has become a defining challenge for the transport sector. The symptoms are widely visible: delayed deliveries, increased freight rates, and mounting operational pressures on dispatchers and logistics managers. While demand for transport capacity continues to grow, the available pool of professional drivers has stagnated, or, in some regions, sharply declined.
Interestingly, the problem is not simply a lack of licensed drivers. Many countries maintain a significant number of individuals with valid commercial driving licenses who have chosen to leave the profession. The reasons are multifaceted: physically demanding work, long and unpredictable hours, limited family time, and insufficient pay relative to the workload. Moreover, generational change is exacerbating the issue. As many experienced drivers from the baby boomer generation retire, the industry struggles to attract younger talent. The result is a widening gap between transport demand and driver availability.
Automation: Promise and Limitations
When discussing solutions, autonomous trucks often dominate the conversation. Self-driving technology is frequently portrayed as the ultimate fix for the driver shortage. Indeed, autonomous vehicles represent one of the most transformative prospects in logistics history. However, the industry remains far from widespread deployment.
Despite remarkable advances in perception, navigation, and safety systems, numerous technological, legal, and ethical questions remain unresolved. The road to autonomous trucking is long, and at present, commercial-scale driverless operations on open roads are not yet feasible.
That said, autonomous haulage is already becoming a reality in controlled off-road environments. Mining and quarry operations, for example, are successfully deploying autonomous dump trucks and loaders within clearly defined sites. These vehicles operate under consistent conditions, allowing for the safe and efficient automation of repetitive transport tasks such as hauling aggregate from pit to crusher. A promising glimpse into what automation can achieve when variables are limited and infrastructure is purpose-built.
Even as technology matures, its use in open-road trucking will likely remain limited in sectors such as building materials logistics. In this domain, delivery tasks often require human expertise beyond vehicle operation, including site coordination, concrete slump checks, and direct interaction with customers. Fully autonomous systems are better suited to predictable long-haul routes, not to the dynamic and safety-critical “last mile” that characterises aggregate and ready-mix deliveries.
Where AI Is Already Making an Impact
While autonomous driving remains on the horizon, artificial intelligence is already transforming another critical seat in the cab: the dispatcher’s. Modern AI-powered transport planning solutions, such as the Transport Optimisation software from INFORM, have been quietly revolutionising logistics planning for decades.
AI algorithms can now optimise complex, multi-constraint delivery schedules in real time, taking into account factors such as order priorities, vehicle capacities, loading times, traffic conditions, and customer time windows. By continuously recalculating routes and order assignments, these systems can adapt to last-minute changes and disruptions with a level of responsiveness impossible for manual planning.
The results are measurable. AI-driven scheduling can reduce the size of active truck fleets by up to 30%, meaning fewer vehicles. And, by extension, fewer drivers are needed to meet the same transport demand. This efficiency gain directly alleviates the pressure caused by the driver shortage, allowing companies to deliver more with less.
Beyond numerical optimisation, AI also supports better human collaboration. Automated planning frees dispatchers from repetitive data handling and manual rescheduling, enabling them to focus on higher-value tasks such as driver communication, customer support, and proactive problem-solving. The combination of AI precision and human judgment creates a more resilient and adaptive transport operation.
This “hands-free” approach to transport scheduling does not replace the human dispatcher; rather, it augments their capabilities. As AI systems handle the computational complexity, humans retain decision authority and contextual understanding. Together, they can achieve a balance between operational efficiency and on-the-ground flexibility that manual methods cannot match.
Looking Ahead
As the logistics industry navigates a challenging decade, the driver shortage will remain a persistent issue. While long-term automation may eventually reshape parts of the transport sector, the most immediate and practical gains come from integrating AI into dispatching and fleet management today.
For building materials suppliers, the message is clear: investing in intelligent transport planning systems is not just about cutting costs. It’s about securing delivery reliability, improving workforce satisfaction, and maintaining competitiveness in an environment where human drivers are increasingly scarce.
By leveraging AI to enhance the efficiency of every dispatched truck, companies can transform the driver shortage from a structural weakness into an opportunity for innovation, ensuring that every journey, and ton of material delivered, moves the industry forward.
