Demand forecasting techniques for build-to-order lean manufacturing supply chains
This addresses a gap in forecasting for industries like electronics and automotive, though it is incremental as it builds on existing methods.
The paper tackled the lack of demand forecasting methods for build-to-order supply chains by proposing a novel data transformation technique and testing it on a new dataset, showing it compares well to state-of-the-art methods across thirteen forecasting approaches.
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.