Computer Science > Machine Learning
[Submitted on 22 Jul 2023 (v1), last revised 15 Aug 2024 (this version, v2)]
Title:Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
View PDF HTML (experimental)Abstract: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.
Submission history
From: Stefan Schoepf [view email][v1] Sat, 22 Jul 2023 20:03:16 UTC (2,115 KB)
[v2] Thu, 15 Aug 2024 20:51:48 UTC (5,451 KB)
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