Supply chain disruption is no longer an anomaly; it’s a constant. From geopolitical tension and rising fuel costs to climate-related events and shifting regulations, logistics leaders are navigating an environment defined by volatility.
But that’s only part of the story. Rising accident rates and escalating costs are adding further strain: large truck crashes have increased since 2024, despite widespread safety efforts. At the same time, fleets are grappling with tightening regulatory and compliance pressures, from evolving emissions rules, such as the EPA’s proposed heavy-duty vehicle standards, to more stringent Compliance, Safety, Accountability (CSA) scoring.
As a result, the concept of supply chain “resilience” has evolved from a buzzword into an operational necessity. At the centre of that resilience is real-time visibility, not only across shipments and inventories, but across fleet safety and compliance too.
While many organisations have made significant strides in digitising warehouse operations, improving demand forecasting, and modernising port logistics, one area remains critically under-addressed: road transport. Despite being one of the chain’s most vulnerable and variable links, the road remains a blind spot for many. Recent research reflects this gap – more than 70% of respondents admitted their fleets lack real-time visibility into road conditions.
Supply chain leaders are grappling with an acute driver shortage that threatens the backbone of road transport. Across the EU, Norway, and the UK, there is already a shortfall of over 233,000 truck drivers, a gap projected to swell to more than 745,000 by 2028 as older drivers retire without enough new entrants to replace them. In the UK alone, an alarming 55% of HGV drivers are aged between 50 and 65, with an average age of 51, signalling that a significant portion of the workforce may leave within the next decade.
Against this backdrop, supply chain leaders must embrace real-time road intelligence, powered by artificial intelligence and edge-computing vision systems, as a key strategic tool for visibility, adaptability, and risk management.
Road transport: A dynamic environment with limited visibility
These mounting challenges highlight the urgent need for stronger oversight and proactive risk management across fleets. Unlike static warehouse environments or planned shipping schedules, roads are dynamic and unpredictable. They’re impacted by human behaviour, weather conditions, infrastructure quality, and spontaneous events, any of which can delay deliveries or damage goods. Yet visibility into these disruptions often remains alarmingly limited.
A recent survey revealed that while 84% of safety leaders identified fleet safety as a high priority, 60% admitted they have no formal fleet safety technology in place, frequently relying on nothing more than basic GPS tracking. Moreover, 46% of surveyed professionals are still unclear about the full financial impact of accidents on their businesses, underscoring how visibility gaps continue to be a serious liability. Without accurate, real-time data on driver behaviour, vehicle conditions, and external risks, companies are left reactive rather than proactive, a critical threat to supply chain resilience.
Edge-computing vision systems powered by artificial intelligence (AI) address this challenge by collecting and processing road-level data directly at the source in real-time. These systems provide immediate insight into traffic conditions, driver behaviour, and environmental hazards, turning the road from a risk point into a source of actionable intelligence. They also play a crucial role in optimising operational costs, a large fleet of delivery trucks means high expenses, and keeping these under control is a constant challenge, especially for companies managing hundreds of vehicles making multiple deliveries each day.
For instance, when weather patterns shift quickly or congestion builds on a critical route, teams can reallocate resources, reroute vehicles, or update delivery schedules in real-time. This shift from reactive management to proactive planning is one of the key advantages of road intelligence.
Systems capable of analysing 100% of drive time add another layer of value, capturing full journey context to support decision-making, coaching and incident resolution.
AI and risk mitigation
AI is a core enabler of dynamic risk mitigation. Rather than relying on historical averages or static route plans, modern AI-driven systems identify emerging patterns and adapt recommendations based on current conditions.
This includes spotting subtle indicators of risk, such as shifts in driver behaviour that suggest fatigue, or clusters of hard braking in a specific area that might point to a developing road hazard. As foundational models evolve, AI is even being trained to predict the likely movements of drivers and vehicles, enabling earlier intervention to prevent incidents before they occur. With this level of intelligence, logistics teams can anticipate disruptions before they escalate and respond proactively to keep operations on track.
Crucially, advanced driver safety platforms today do far more than just warn of lane departures or potential forward collisions. They continuously analyse driving performance in real time, issuing immediate voice alerts to correct risky actions, turning each potential hazard into a safer outcome on the spot. For example, a driver about to tailgate or showing early signs of drowsiness can receive a prompt to adjust, helping avert accidents before they happen. Many systems also incorporate positive reinforcement, recognising and rewarding safe driving habits to strengthen safety cultures across fleets.
This kind of dynamic responsiveness is essential during peak demand periods, extreme weather events, or disruptions to global trade routes. As pressure mounts to deliver faster and more reliably, the ability to adjust in motion becomes a vital competitive edge.
Building resilience into the last mile
The last mile has become one of the most scrutinised segments of the supply chain, where delays and miscommunication are most visible to customers. It’s also where efficiency and traceability are most challenging to maintain, particularly during external disruptions.
Real-time road intelligence provides the operational agility to protect this final delivery stage. By integrating road-level data into dispatch and routing systems, teams can make micro-adjustments that reduce delays, improve customer communication, and avoid costly rework.
This agility can also help prevent compliance breaches, protect product quality, and reinforce customer trust in temperature-sensitive or high-value logistics. Fleet managers using AI-driven road intelligence platforms have already seen measurable improvements, such as a 50% reduction in road accidents, by combining real-time alerts with proactive coaching sessions.
Closing the gaps: From compliance to ESG
Beyond operational continuity, road intelligence also plays a critical role in helping organisations meet growing regulatory and ESG requirements. Visibility into emissions, idling time, route efficiency, and driver behaviour helps teams identify areas for improvement and demonstrate measurable progress against sustainability goals.
It also supports ethical business practices, ensuring safety is prioritised, risky behaviours are addressed constructively, and drivers are given the tools to perform at their best. This reinforces a safety-first culture contributing to long-term resilience, driver retention, and public trust.
Real-time road data provides the insight and accountability needed to align transport operations with broader environmental and governance commitments.
Looking forward: A strategic asset, not a tactical add-on
Real-time road intelligence isn’t a tactical bolt-on; it’s becoming foundational to building resilient supply chains. By embedding AI-powered insights into core logistics processes, organisations gain the flexibility to respond faster, the foresight to avoid costly disruptions, and the intelligence to meet evolving expectations.
In a world where supply chains must operate precisely in dynamic environments, the ability to see and respond at the edge is crucial.
The road has long been treated as the most unpredictable link in the supply chain. However, with the right intelligence in place, it can become one of the most strategic. AI, when fuelled by scale, speed and visibility, becomes a force for good, reducing accidents, empowering drivers and creating a safer ecosystem for everyone on the road.
- Digital Supply Chain