Identifying contributors to supply chain outcomes in a multi-echelon
  setting: a decentralised approach
Stefan Schoepf Jack Foster Alexandra Brintrup
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.