Blank sailings across the main East–West container trades surged sharply this month, signalling a new phase of schedule volatility that is rippling through ports, terminals, and inland networks.

Data from Drewry shows a 122 percent month on month increase in withdrawn sailings, effectively doubling removed capacity compared with the prior period. While seasonal fluctuations around the Lunar New Year are typical, analysts and operators say the scale of disruption now reflects deeper structural pressures shaping global liner networks.

Geopolitical tensions, renewed United States–China tariff disputes, inflation dampening consumer demand, and uncertainty tied to energy transition investments are collectively distorting trade visibility. The result is a planning environment where demand signals shift faster than traditional schedule planning cycles can absorb.

Variability Overtakes Capacity as Core Operational Risk

For ports and terminals, the operational strain is less about total capacity and more about unpredictability.

A cancelled sailing can erase a berth window within hours. Late arrivals disrupt yard allocation, labour shifts, and equipment deployment. Inland barge and rail rotations fall out of sync, triggering idle assets in one corridor and congestion spikes in another.

“Schedule information is the backbone of efficient port calls,” said Sjoerd de Jager, Managing Director and Co Founder of PortXchange. “But in volatile conditions, static ETAs become outdated almost immediately. If the industry wants to reduce waste, it needs to move from static planning to predictive, continuously updated coordination.”

Even where weekly capacity levels remain broadly stable, frequent schedule adjustments create cascading inefficiencies. Waiting times lengthen, towage and pilotage slots shift, and fuel consumption rises as vessels speed up or slow down to recover lost time.

Static ETA Models Struggle in Dynamic Networks

Estimated times of arrival have historically functioned as fixed planning anchors across maritime logistics chains. In practice, many are calculated once and manually revised only when disruptions become visible.

That approach is proving inadequate in an environment shaped by port congestion, lock delays, weather systems, draft restrictions, and network knock on effects from earlier port calls.

As schedule variance compounds, buffer costs rise. Terminals hold additional yard space. Operators deploy extra labour coverage. Inland fleets absorb idle time to protect service reliability.

Industry data indicates that schedule reliability on some East–West services has fallen below long term averages, amplifying pressure on downstream logistics planning.

Predictive ETA Systems Gain Commercial Traction

Parts of the inland shipping sector are accelerating adoption of predictive scheduling tools designed to recalculate arrival forecasts continuously.

In collaboration with a major inland operator, PortXchange developed ETAPredictor, an AI based ETA engine that integrates live vessel movements with historical and network data inputs.

According to the company, deployment reduced ETA prediction error by more than 50 per cent within six months. Improved forecast accuracy enabled tighter berth planning, reduced towboat idle time, and stabilised workforce allocation.

Predictive ETA updates are delivered through API integrations into existing terminal and operator systems, allowing planning teams to adjust berth windows, crane assignments, and hinterland connections in near real time.

The technology is gaining attention as blank sailings and rolling schedule revisions become embedded features of liner network management rather than short term anomalies.

Periods of disruption tend to expose process inefficiencies that remain hidden in stable markets. Operators reliant on static scheduling often compensate through physical buffers such as warehouse capacity and yard storage. Data driven coordination offers an alternative buffer built on information rather than infrastructure.

Shipping cycles have always fluctuated. What appears to be shifting is the baseline level of uncertainty surrounding them.

As de Jager noted, the industry cannot control blank sailings or geopolitical shocks, but it can control planning precision. In a market defined by volatility, the ability to anticipate schedule change is emerging as a competitive differentiator rather than a technological upgrade.