Worldwide Flight Services (WFS) has developed a new machine learning-powered digital tool to deliver highly accurate forecasts of cargo volumes by flight, truck and day, enabling warehouses to align labour and resources further in advance.

The Performance Management Platform – Machine Learning Forecast (PMP MLF) has been trained on more than 10 years of operational data, processing over three million air waybills alongside historical flight and truck movement records.

The system incorporates seasonality, holidays and cargo types to generate daily forecasts of tonnage, unit load devices (ULDs) and piece counts.

The platform is currently deployed across 75 warehouses in 13 countries, producing forecasts for 9,842 flights and 6,216 truck movements each week.

Data is broken down by transport mode, flight or truck number, customer and warehouse location, feeding directly into station-level planning tools.

WFS says the system achieves forecast accuracy levels of between 92% and 98%, even during periods of irregular demand. This allows the ground handler to detect volume surges earlier and adjust resources proactively, shifting labour between teams or sites to reduce service level agreement breaches, avoid unnecessary overtime and minimise idle time.

The air cargo sector has traditionally struggled with accurate forecasting due to volatile volumes, with labour planning often based on manual estimates or historical averages, typically resulting in a 10–15% mismatch between staffing levels and actual workload.

Phase two of the PMP MLF tool was rolled out in summer 2025, introducing enhanced dashboards and visual analytics, tighter integration with workforce management and rostering systems, and customer-level forecasting to allow joint planning of expected volume peaks.

Jimi Daniel Hansen, SVP operational excellence, said: “For many years, cargo handlers have relied on manual scheduling, Excel spreadsheets, or basic rolling averages for forecasting – and we know some still do.

“By leveraging machine learning within a complex operational network, our goal was to replace reactive guesswork with data-driven clarity to optimise workforce allocation, enhance service levels, and reduce operational waste across our global air cargo network – and we are inspired by the results.

“Predictive planning and precision forecasting mean we have achieved a fundamental transformation in how cargo handlers plan and operate. These benefits translate into fewer delays due to staffing issues, improved service consistency, and transparent, data-backed capacity shared in advance.”

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