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.

使用可解释的人工智能方法，通过分散计算估算多阶段生产过程中感兴趣度量的贡献来源，从而避免了数据共享的问题，并通过实证验证展示了其在检测质量变化源头方面相对于集中式方法的有效性。

在多级设置中识别对供应链结果的贡献者：一种分散化的方法