  Build-to-order (BTO) supply chains have become common-place in industries
such as electronics, automotive and fashion. They enable building products
based on individual requirements with a short lead time and minimum inventory
and production costs. Due to their nature, they differ significantly from
traditional supply chains. However, there have not been studies dedicated to
demand forecasting methods for this type of setting. This work makes two
contributions. First, it presents a new and unique data set from a manufacturer
in the BTO sector. Second, it proposes a novel data transformation technique
for demand forecasting of BTO products. Results from thirteen forecasting
methods show that the approach compares well to the state-of-the-art while
being easy to implement and to explain to decision-makers.
