Richard Borggreve: Signals, Noise, and Forecasting Performance under Varying Freight Market Predictability

Signals, Noise, and Confidence: Human and Algorithmic Forecasting Performance under Varying Freight Market Predictability
 

Freight-rate forecasting is critical to multinational firms navigating volatile transportation costs, geopolitical disruptions, and increasingly fragile supply chains. Yet trade lanes differ in structure and their predictability, raising questions about whether standardized corporate forecasting processes are appropriate across heterogeneous market environments. Drawing on a forecasting tournament, the study compares human, and algorithmic approaches across trade lanes with different signal-to-noise conditions, assessing both forecasting accuracy and confidence. The findings aim to clarify how firms can match forecasting methods to the underlying predictability of the task.
 

Richard Borggreve started his PhD program at the Kühne Logistics University in September 2022 under the supervision of Prof. Dr. Gordon Wilmsmeier and Prof. Dr. Kai Hoberg. Richard’s academic background was formed at Lund University where he graduated with a B.Sc.BA. and later concluded his studies with a M.Sc.BA. specializing in the fields of organization and informatics. Before starting the master thesis he studied urban transport economics at the Universidad de Sevilla in Spain. Richard has worked in various countries and has broad professional experience in the fields of operations and trade management in both forwarding and container shipping.