It’s crunch time. The logistics manager index (LMI) for April 2026 showed US trucking capacity at the second-lowest level in the history of the index—and prices at their second-highest level in that time. Additionally, this year in the US and Europe, there are around half a million unfilled driving jobs.
Shifting regulations have added more stress to hiring and retention, and it’s not just drivers. Warehouse staff are also hard to hire and retain in 2026, which has an immediate impact on transportation capacity: if nobody is there to load or unload, your truck is bound to be heavily delayed.
The labor market isn’t always going to be quite this tight, but this issue rears its head time and time again. It’s something transportation teams need to have a plan for, regardless of whether they’re operating a private/dedicated fleet or utilizing logistics service providers (LSPs) and for-hire carriers. So, what can they do to tackle the capacity crunch?
Utility and efficiency
Maximum utilization is the maxim for transportation teams when capacity is under strain. Every truck, driver and load has to be as efficient as possible. Running half-full is cost and emissions-heavy, and not financially viable in many cases anyway. With fuel costs rising too, teams are aiming for full truckloads as much of the time as possible. That means finding backhauls, minimizing route distance, optimizing plans, building efficient loads and consolidating moves.
To achieve that, transportation optimization and load planning are crucial, optimizing routes, hubs, carriers, modes and consolidation strategies. However, there’s no one-size-fits-all approach to optimization—it needs to account for your specific business and the scenarios your transportation network actually deals with. Straightforward rules-based optimization can’t help you to improve utilization if it generates recommendations and routes that simply aren’t compatible with operational reality.
Instead, teams need to be able to carefully configure their optimization engine to reflect that reality, so that the recommendations and decisions aren’t just theoretically sound but also practically achievable.
In addition, transportation optimization needs to adapt as those plans and scenarios play out in real time. What was optimal at the start of the day isn’t necessarily still optimal by noon! Being able to adjust on the fly means teams aren’t beholden to out-of-date decisions.
But optimizing all this (and juggling service levels and emissions too) across a whole complex network in real time requires a volume of data, and the ability to process and understand it, that no human team can manage.
AI is driving higher utilization to counterbalance lower capacity
AI is exceptional at the kinds of utility-maximizing calculations that transport managers need to run at scale. A combination of optimization and AI-driven intelligence helps teams with:
- Finding backhauls for private and dedicated fleet
- Optimizing load building
- Optimizing routes and plans
- Improving consolidation
- Finding more accurate routing and continuous moves
- Automating transportation planning, scheduling, exceptions and decision support.
It’s essential to note that transportation systems that analyze data and make recommendations or decisions must do so with full awareness of context. That demands interoperability between transportation management and your WMS, OMS, RMS (returns management), and business planning systems (demand & supply planning, forecasting, replenishment et cetera).
Extending efficiency through the network
Once goods are in motion, transportation teams need to stay on top of shipments across a global multi-mode network, ensuring they avoid additional costs and inefficiencies. When capacity is scarce, making sure that all available capacity is being used effectively and smoothly is critical.
Using digital supply chain networks, transportation teams can get advance notice of at-risk shipments to avoid last-minute expedites and costly adjustments. They also receive the benefits of real co-operation with trading partners – not just sharing workarounds or constant quick check-ins, but synchronization through the network, so everyone is working on the same page.
Automation across multi-party processes means you don’t just automate within your business, but across the touchpoints in your network – no more mornings spent updating and confirming today’s appointments, or checking that yesterday’s loads were picked up or delivered. That means more time optimizing things that materially reduce costs and improve service, and less time checking where shipments are.
It also means that transportation managers have more time for one critically under-appreciated element to capacity crunches: relationship management. Keeping drivers happy is vital when the market is tight, and automating mundane logistical tasks can give transport teams time to build relationships and foster greater retention and resilience.
