With decades of experience spanning the supply chain and delivery sectors, Rohit Laila has witnessed the evolution of logistics firsthand. He joins us to discuss a critical shift in the industry: the move from rigid, static planning to dynamic, adaptive models. We’ll explore how increasing volatility is challenging traditional systems, the profound impact of real-time data on decision-making, and how leadership must evolve to balance technology with human judgment in this new era of logistics.
Given that logistics plans are increasingly exposed to mid-cycle changes in cost and capacity, what are the primary operational challenges teams face in maintaining high service levels and reliable ETAs? Please share an example of how a familiar route can be disrupted.
The core challenge is that the ground is constantly shifting beneath our feet, but customer expectations remain sky-high. They still demand on-time deliveries and ETAs they can count on, regardless of what’s happening behind the scenes. Operationally, this creates immense pressure. A route that was perfectly optimized on Monday can fall apart by Wednesday. For instance, a carrier partner who was a top performer last week might suddenly see their local capacity tighten, causing their service levels to slip. Or a typically smooth-running hub can get bogged down by a brief labor shortage or an uneven flow of inbound goods, creating a ripple effect that throws all subsequent timings off schedule. These aren’t massive, once-a-year disasters; they’re constant, smaller disruptions that accumulate and make it incredibly difficult to keep the original plan intact.
When planners make manual adjustments to legacy TMS setups, the reasoning is often lost in emails or notes. Could you describe the downstream effects of these undocumented fixes, especially when finance and sustainability teams need to account for shifts in costs or emissions?
This is a massive and growing problem. When a plan breaks, planners have to step in and make quick, manual fixes to keep things moving. While these adjustments often solve the immediate issue, they create significant blind spots later on. The “why” behind a decision—the context for rerouting a shipment or switching a carrier—gets buried in an email thread or a planner’s personal notes instead of being logged in the system of record. When the finance team later sees a cost variance, or the sustainability team needs to explain an unexpected spike in emissions for their climate reporting, they have no clear audit trail. They have to go back and try to reconstruct the decision after the fact, which is inefficient and undermines accountability. As transport activity becomes more integrated into financial and climate reporting, this lack of a shared, documented reality is simply not sustainable.
Agentic TMS platforms aim to connect planning with real-time network performance. Can you walk us through how these systems integrate live data signals—like weather, tariffs, and carrier capacity—to proactively suggest alternative routes or partners when the original plan is no longer optimal?
Think of an agentic TMS as a co-pilot that sits alongside your existing systems, constantly watching the horizon. It pulls in a continuous stream of live data signals—carrier performance metrics, fluctuating tariffs, shifting customer demand, incoming weather patterns, and real-time capacity information—and synthesizes them into a single, current view of the network. So, instead of a plan based on assumptions made last week, you have a plan that reflects what is happening right now. When a critical variable changes, like a trade lane suddenly tightening or a tariff unexpectedly moving, the system doesn’t just send an alert. It proactively surfaces intelligent options, highlighting alternative routes or vetted partners that can keep service steady and costs in check. It’s about moving from reactive fire-fighting to proactive, data-driven course correction.
Moving from static to adaptive planning can yield significant benefits. Could you provide specific examples or metrics on how continuously adjusting plans improves vehicle fill rates, reduces empty running, and helps keep ETAs accurate during a disruption?
The benefits are very tangible. By managing loads based on what is truly available in the network at any given moment, we can dramatically improve vehicle fill rates. The system can see an opportunity to consolidate shipments or hold a load for a few hours to wait for a fuller truck, which is something a static plan would completely miss. This directly combats the costly problem of empty running, where trucks are on the road without full cargo. Furthermore, because the system is constantly re-calibrating based on live conditions, the predictive delivery ETAs it generates are far more accurate and reliable. When a disruption occurs, the plan adapts so quickly that the impact on the end customer is minimized, and the new ETA reflects the adjusted reality, not a hopeful guess from an outdated schedule. This continuous cycle of adjustment keeps the entire network more stable and efficient.
As adaptive technologies become more common, leadership will depend on judgment. How should a logistics leader decide when to trust a system’s automated recommendation versus when to apply their own experience to override it, especially when balancing service, cost, and emissions?
This is the next great frontier for logistics leadership. The technology provides the options, but the final decision still requires human wisdom. A leader should trust the system when the data is clear and the recommendation aligns with established strategic priorities. For example, if the system suggests a slightly longer route that avoids a major weather disruption and has a negligible impact on cost and emissions, that’s an easy call to trust. However, a leader’s experience is crucial in ambiguous situations. Perhaps the system recommends a new, low-cost carrier to save money, but the leader knows from experience that this carrier has a poor track record with fragile goods for a particular high-value client. In that case, overriding the system to protect a key customer relationship—balancing service over immediate cost—is the right strategic move. The skill lies in using the technology as a powerful advisor, not a dictator, and knowing how to weigh those competing factors of service, cost, and sustainability based on the broader business context.
What is your forecast for adaptive logistics?
I believe that by 2026, adaptive logistics will no longer be a niche advantage but a fundamental requirement for competitive operations. The volatility we’re seeing isn’t a temporary trend; it’s the new normal. Companies that continue to rely on static, week-old plans will be consistently outmaneuvered by those whose networks can sense and respond to change in near real-time. The technology will become more integrated and intuitive, but the real evolution will be in leadership. The most successful leaders will be those who master the art of the human-machine partnership—leveraging AI for its analytical power while applying their own strategic judgment to navigate the complex trade-offs that no algorithm can fully solve on its own.
