Using machine learning to predict catering demand

Each year, airline and rail companies serve millions of cups of tea, biscuits and sandwiches to passengers. Transport executives know that getting customers their choice of food and beverages at the right time along the journey increases customer satisfaction and reduces the risk of staff abuse. These operators must balance the upside of offering a wide selection of products against the risk of stocking items that don’t sell, leading to wastage and incurring an opportunity cost by taking up space that could have been used for other goods. Anticipating what passengers will want to eat and drink is a task that many passenger services companies still conduct manually, as part of a time-consuming ordering and reconciliation operation. We set out to help one transport operator to use automated, data-driven insights to improve the stock ordering process. We worked with the client through the entire project lifecycle, from proving the validity of a data-based approach and securing internal stakeholder buy-in, through to project rollout. The project harnessed the power of machine learning algorithms to create a predictive tool that identifies cost efficiency opportunities, leverages the company’s data assets to improve the accuracy of catering sales forecasts with a user-friendly system, and delivers real benefits on each journey.