The vehicle logistics industry is grappling with a significant shortage of skilled labor. According to the World Road Transport Organization (IRU), over 3 million truck driver positions are currently unfilled globally, This shortage is projected to double by 2028 without significant intervention.
The logistics sector faces broader labor challenges. A Descartes study found that 37% of organizations are experiencing high labor shortages, with 61% reporting transportation disruptions due to understaffing.
The challenge is no longer simply about finding qualified personnel — it's about preserving the knowledge of those who are leaving and enabling the next generation to succeed faster. From port terminals to vehicle yards and workshops, much of today’s operational fluency resides in the heads of experienced dispatchers, planners, technicians, and yard managers. When they retire or leave, they take decades of insights and process flow knowledge with them
But rather than accept declining productivity or elongated training curves as inevitable, many logistics organizations are turning to artificial intelligence and decision intelligence technologies to preserve this valuable knowledge. These systems don’t just automate — they think. And more importantly, they learn from, replicate, and extend expert decision-making to support new, less experienced staff in real time.
The human cost of complexity
Vehicle logistics is inherently complex. Every car moved from plant to dealer may touch several transport modes, pass through multiple processing steps, have the dealer location or customer status changed, and face dynamically changing yard conditions. Skilled operators develop a mental model over the years — knowing how to reroute around delays, how to prioritize vehicles under tight SLAs (service level agreements), and how to coordinate terminal movements with minimal idle time.
As fleet owners and third-party logistics (3PLs) struggle with labor availability and retention, this tribal knowledge becomes a bottleneck. Many younger recruits entering logistical roles are unfamiliar with the nuances of compound logistics or vehicle processing center (VPC) task sequencing. Without a way to close the knowledge gap quickly, onboarding timelines stretch, errors rise, and service levels drop.
From “gut feeling” to decision intelligence
This is where artificial intelligence (AI) comes in. Today, logistics and supply chain for automotive emphasizes the critical role of automation and digitalization in addressing labor shortages. AI and robotics are being leveraged to enhance efficiency, improve quality, and reduce dependency on manual labor.
Unlike traditional process automation or ERP systems that simply document actions, decision intelligence platforms use AI, optimization algorithms, and rules-based reasoning to guide decision-making dynamically.
Take the example of yard management. In busy maritime RoRo (roll-on/roll-off) terminals with thousands of vehicles and limited space, assigning the “right” parking spot isn’t just about availability — it requires understanding delivery priority, accessibility for VPC work, staging timelines, transport modes, and loading plans. AI-driven yard management systems now calculate all these variables in real-time, giving operators optimized movement plans that reduce shuffling, improve throughput, and increase visibility down to the dealer and customer level.
Onboarding through optimization
Critically, these systems are not just about efficiency — they’re powerful onboarding tools. In effect, they function like digital mentors: providing task-specific guidance based on best-practice logic. AI can assist in scheduling and resource allocation, ensuring that tasks are prioritized effectively, and technicians are utilized optimally.
This not only improves productivity but also serves as an onboarding tool for new employees, guiding them through complex processes with real-time support. For example, a new team member in workshop management can be supported by a system that automatically prioritizes jobs, balances technician loads, and schedules parts availability — all based on AI-informed planning models.
In practice, this reduces the dependency on experience alone. One logistics center operator described the impact as a “20-30 times improvement in coordination speed” after implementing intelligent control over vehicle movements, repairs, and team planning.
Replicating expertise at scale
Decision intelligence also plays a critical role in strategic planning. Develop a network optimization solution to simulate multiple scenarios based on cost, carrier capacity, emissions, and other key SLAs like delivery times, providing the best and second-best routing strategies across possible carrier-route combinations.
The outcome? Annual cost reduction, improved service predictability, and better alignment with sustainability goals — all while navigating persistent labor shortages and supply volatility.
These scenarios demonstrate the broader power of AI in logistics: it captures expert logic, simulates high-stake decisions, and provides a data-backed assistant to human operators — not to replace them, but to empower them.
From static software to thinking systems
The leap forward comes from moving beyond static dashboards or manually updated spreadsheets to adaptive, learning systems. Take a vehicle logistics platform, for example, which combines classic optimization models with algorithms by machine learning, and even large language models (LLMs) to deliver not just insight, but context-aware decision recommendations across the entire finished vehicle lifecycle — from plant to dealer.
Such systems can align priorities based on customer delivery dates, model priority, workshop and parts availability, and then trigger alternative route suggestions, or even auto-generate decision proposals when plans deviate due to disruptions.
A future-proof approach
In a world where labor volatility is becoming the norm, not the exception, futureproofing means building systems that both preserve and build knowledge, accelerate training, and increase resilience. That’s not just a tech challenge — it’s an operational imperative.
AI and decision intelligence aren’t replacing humans in vehicle logistics. They’re extending them — making it possible for the next dispatcher, planner, or yard manager to perform like a veteran, even on Day 1.
