A predictive data-driven approach for supply chain quality risks in the automotive sector

Nowadays, modern supply chains are exposed to an increasing number of risks. Among different risks, supplier quality risks consist of non-compliant delivery of supplier products, which on the one hand, can affect the inventory and, on the other hand, can lead to an increased workload due to the time spent to manage quality issues. In supply chain risk management, artificial intelligence, machine learning and deep learning have been identified as valuable tools for predicting incumbent risk. However, a lack of data-driven approaches for predicting the extra amount of time required to manage supply chain quality risk has been identified in the literature. The aim of this paper is thus to present a deep learning model for predicting supplier quality risk and to investigate its predictive capabilities. The potential of the proposed approach has been tested on a real case study of an Italian automotive company and its performance has been compared with other predictive models when considering forecasts made at different levels of aggregation and with different forecasting lengths.