Navigating demand forecasting in make-to-order manufacturing : the role of global models and intermittent time-series

Demand forecasting can optimise production and supply chain practices in manufacturing organisations. However, demand forecasting is not widely adopted among make-to-order (MTO) manufacturers with mass customisation offers. Building effective demand forecasting systems is challenging in such organisations due to the numerous unique manufactured articles and sparse demand patterns. This position paper argues that make-to-order manufacturers should employ demand forecasting to a larger extent, and that the forecasting community should address challenges related to the domain. Key challenges include creating models capable of predicting both demand size and timing of intermittent forecasts, as well as a deeper insight into the effects of global deep learning time-series models. We perform a pilot experiment using demand forecasting in a purchasing decision support system to validate the usefulness of demand forecasting for MTO manufacturing organisations with mass customisation offers. A research roadmap is proposed to address the identified challenges.