Worldwide Flight Services (WFS) has enhanced its machine learning tool, known as the Performance Management Platform – Machine Learning Forecast (PMP MLF), to improve the accuracy of air cargo volume forecasts and streamline service strategy development. This upgrade, rolled out this summer, aims to provide better insights for operational planning across the company’s global network.
The PMP MLF utilizes machine learning algorithms that have been trained on a decade’s worth of operational data, allowing WFS to generate forecasts for cargo volumes on a per-flight, per-truck, and daily basis. This technology processes information from more than 3 million air waybills and historical records of flight and truck movements, considering factors like seasonality, holidays, and cargo types.
Currently, the system delivers forecasts pertaining to 9,842 flights and 6,216 truck movements each week, across 75 warehouses in 13 countries. Daily outputs include forecasts on tonnage, unit load devices (ULDs), and piece counts, categorized by transport mode—whether freighter, passenger, or Road Feeder Services—as well as by flight or truck number, customer, and warehouse location.
WFS emphasizes that these forecasts directly inform station-level planning tools, equipping each location with vital, forward-looking data. This capability enables the company to identify and prepare for volume surges proactively, allowing for more agile resource adjustments, such as reallocating labor among teams or sites. The result is a significant reduction in breaches of Service Level Agreements (SLAs) that could arise from understaffing or resource overloads, ultimately minimizing unnecessary overtime or idle time.
The second phase of the PMP MLF rollout has introduced additional digital enhancements, including improved dashboards and visual analytics, better integration with workforce management and rostering tools, and customer-level forecasting capabilities to better manage anticipated volume peaks.
WFS states that the tool maintains a remarkable accuracy range of 92-98%, even during periods of irregular demand. This level of precision is particularly crucial in an industry long plagued by challenges in forecasting due to fluctuating cargo volumes, where traditional labor planning methods often rely on manual estimates and historical averages. Such approaches can leave a 10-15% gap between actual staffing levels and workload requirements, leading to operational inefficiencies and inconsistent service quality.
Jimi Daniel Hansen, Senior Vice President of Operational Excellence at WFS, pointed out that for many years, cargo handlers have depended on rudimentary methods like manual scheduling and basic rolling averages for forecasting. He noted, “By leveraging machine learning within a complex operational network, our goal was to replace reactive guesswork with data-driven clarity to optimize workforce allocation, enhance service levels, and reduce operational waste across our global air cargo network—and we are inspired by the results.”
Hansen further emphasized the customer-centric benefits of the tool: “Predictive planning and precision forecasting means we have achieved a fundamental transformation in how cargo handlers plan and operate. All of these benefits are meaningful to our customers. They translate into fewer delays due to staffing issues, improved service consistency, and transparent, data-backed capacity shared in advance. This is the type of digital innovation they want to see.”
The advancements in WFS’s machine learning tool underscore the growing trend within the air cargo industry to adopt digital solutions for more effective operational management. As companies increasingly seek to navigate the complexities of logistics and demand fluctuations, the integration of sophisticated forecasting technologies is expected to play a pivotal role in driving efficiency and maintaining service quality in the future.
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