Identifying Contributors to Supply Chain Outcomes in a Multiechelon Setting: A Decentralised Approach

Organizations often struggle to identify the causes of change in metrics, such as product quality and delivery duration. This task becomes increasingly challenging when the cause lies outside of company borders in multiechelon supply chains that are only partially observable. Although traditional supply chain management has advocated for data sharing to gain better insights, this does not take place in practice due to data privacy concerns. We propose the use of explainable artificial intelligence for decentralized computing of estimated contributions to a metric of interest in a multistage production process. This approach mitigates the need to convince supply chain actors to share data, as all computations occur in a decentralized manner. Our method is empirically validated using data collected from a real multistage manufacturing process. The results demonstrate the effectiveness of our approach in detecting the source of quality variations compared to a centralized approach using Shapley additive explanations.