Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at the origin-destination (O\&D) level, focusing on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon. The results demonstrate that our approach outperforms industry benchmarks, offering actionable insights for cargo capacity allocation and strategic decision-making in the air cargo industry. While this work is applied in the airline industry, the methodology is broadly applicable to any field where forecast-based decision-making in a volatile environment is crucial.

Digitisation and its futuristic approach is taking the global business landscape by storm and air cargo industry is not an exception. More than being physical, it is a mindset change. The winners will act now by forgoing resistance and embracing technology! Rewind just six or seven years and you will find the air cargo industry happily transporting cargo believing it has mastered the art, until a wave of technology roiled and disrupted the well set standards. In due course, the consumer base also became more aware and their rising expectations gave major goals to the industry to not only track and trace but also make the entire supply chain leaner.