Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach

TL;DR

This paper introduces a decentralised explainable AI method to identify contributors to supply chain outcomes in multi-echelon settings, addressing data privacy issues and improving detection of quality variations without requiring data sharing.

Contribution

The paper presents a novel decentralised explainable AI approach for supply chain analysis, enabling contributor identification without data sharing among actors.

Findings

Effective detection of quality variation sources

Comparable accuracy to centralised methods

Validated on real manufacturing data

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.