A Data-Driven Approach to Predict Supply Chain Risk Due to Suppliers’ Partial Shipments

Supply chain resilience has been identified as a pillar of the new Industry 5.0 paradigm, and artificial intelligence and, in particular, machine learning have been indicated as effective tools to obtain it. However, although a vast amount of qualitative literature highlighted the capability of these technologies, further knowledge about how to properly design and use these tools to manage supply chain risk proactively and thus gain resilience needs to be produced. Indeed, some gaps have been noticed by analyzing the literature proposing approaches for proactively dealing with supply risks. In particular, no predictive approaches have been designed to deal with the operational risk related to the increased workload produced in the material acceptance department generated by suppliers’ partial shipment practices. This paper thus proposes a predictive approach based on ARIMAX model to cover this gap. The proposed approach has been tested in an Italian automotive company, and its performance has been compared with other widely adopted forecasting approaches based on both traditional and deep learning models. Results have highlighted the advantages of the proposed approach in terms of accuracy and time required to build the predictive model. Furthermore, the proposed approach has revealed stable accuracy performance in both short-term and long-term forecasts, resulting in proper support for both short- and long-term planning activities.