Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
This addresses the challenge for organisations in supply chain management to pinpoint external causes of metric changes without compromising data privacy, representing an incremental improvement over existing methods.
The paper tackles the problem of identifying causes of changes in supply chain metrics like product quality in multi-echelon settings where data sharing is limited due to privacy concerns, by proposing a decentralised explainable AI approach that computes estimated contributions without sharing data, and it demonstrates effectiveness in detecting quality variation sources compared to a centralised method using Shapley additive explanations.
Organisations 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 multi-echelon 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 decentralised computing of estimated contributions to a metric of interest in a multi-stage production process. This approach mitigates the need to convince supply chain actors to share data, as all computations occur in a decentralised manner. Our method is empirically validated using data collected from a real multi-stage manufacturing process. The results demonstrate the effectiveness of our approach in detecting the source of quality variations compared to a centralised approach using Shapley additive explanations.