A machine learning model predicting supplier delivery delays under partial shipments conditions: a case study in the automotive sector

In today's increasingly vulnerable supply chain landscape, the ability to anticipate risks is paramount for business survival. Of particular importance is the estimation of supplier delivery delays, especially for companies heavily reliant on outsourcing and just-in-time practices, where late deliveries can disrupt production flow and result in significant revenue loss. Recognizing this critical need, researchers have developed machine learning models to forecast supplier delivery delays. However, existing models often overlook the possibility of a single order being delivered in multiple shipments by the supplier. To address this limitation, this study thus proposes a novel multioutput regression model to deal with delivery delay predictions in presence of partial shipments conditions. The proposed model is thus built to be able to estimate four key variables for each order: the days between the planned delivery date and the date of the first partial shipment, the days between the planned delivery date and the date of the second partial shipment and the amount of quantity delivered respectively in the first e second partial shipments. An empirical investigation of the predictive accuracy reachable by the proposed approach, based on real-world data from an automotive case study, is conducted to evaluate the proposed approach's effectiveness. Moreover, the capability of the proposed approach to properly estimate the real cost impact generated by the non punctual delivery of purchased components is compared with the capability to estimate the same effect using a model not specifically designed to consider situations involving partial shipments. © 2024, AIDI - Italian Association of Industrial Operations Professors.