Planning is becoming more difficult to hold steady across many logistics operations. Routes that once followed a familiar pattern are now exposed to mid-cycle changes in cost, capacity and network performance that are hard to anticipate, writes Nishith Rastogi (pictured, below), CEO of Locus.
Volatility is pushing logistics teams away from static plans and towards more adaptive models that can adjust as conditions change. At the same time, the UK’s move toward international sustainability standards is pulling transport activity into financial and climate reporting, which means more scrutiny on how planning decisions are made and how networks respond when plans shift.
None of this reduces what customers expect. They still want deliveries to arrive when promised and ETAs they can rely on. The result is a planning environment where operating conditions move often, while the bar for service stays high.
Limits of legacy planning
Planning infrastructure in many logistics operations is still shaped around older expectations. Tools like Transport Management Systems, ERP modules and carrier portals are designed to maintain order and efficiency. They perform reliably when inputs remain steady and can record movements and costs across a network. Once those inputs start to shift, the assumptions behind the plan begin to move as well.
This is when the rest of the network starts to loosen. A carrier that performed well last week may slip if local capacity tightens. A hub that usually runs smoothly can slow after even a brief disruption. Weather events, labour gaps and uneven inbound flows can quickly change how much of the original plan still holds, and the timings that depend on it become harder to maintain.
Most traditional TMS setups will not adjust on their own when inputs shift. They continue allocating loads to the same partners and follow the same routing logic. Planners are then left to step in and keep service stable, often by making quick manual adjustments. These fixes often work in the moment but can create blind spots later as the reasoning behind them lives in emails or notes rather than in the system of record.
This becomes a problem when finance, sustainability or C-suite teams need evidence of a cost movement or emissions change. Without a shared record of why plans shifted, decisions have to be reconstructed after the fact, making accountability harder to maintain as transport activity moves into financial and climate reporting.
Closer to reality
When decisions lack a clear record of their reasoning, logistics teams look for ways to keep planning connected with how the network is performing. That need for closer alignment is leading many organisations to add agentic TMS, systems that continuously monitor live conditions and adjust plans as constraints shift rather than waiting for manual intervention.
These systems sit alongside existing platforms and track critical signals live. They bring carrier performance, tariffs, demand, weather and capacity into one view, so the plan reflects what is happening now rather than what was assumed at the start of the week.
This means that when a lane tightens or a tariff moves mid-cycle, the system surfaces options that highlight alternative routes or partners and keeps service steady. Predictive delivery helps ETAs stay accurate while loads can be managed around what is truly available, which improves vehicle fill rates and reduces empty running.
Each change keeps its context attached. The trigger and the intent sit inside the same view as the plan. This allows finance, sustainability and operations teams to see why something may have moved and what it achieved.
The broader effect is a network that stays more stable even when conditions are not. Adjustments can happen earlier, disruptions are contained sooner, and planning becomes a continuous cycle rather than a sequence of repairs.
The path for 2026
The next stage for logistics leadership through 2026 is about judgement just as much as embracing agentic technology. As systems grow more adaptive, the real skills lie in knowing how to utilise that flexibility effectively: when to trust the model, when to override it, and how to balance service, cost and emissions as conditions keep shifting.
