AI-Driven Supply Chain Management in Electronics Manufacturing: A Case Study of the BOMate Platform by Foxconn

Abstract

Artificial intelligence has become an important driver of transformation in supply chain management (SCM). Existing research has focused primarily on consumer-facing contexts and localized applications such as forecasting, inventory, and logistics. Comparatively less attention has been given to how leading manufacturing firms implement AI to support SCM processes in real-world production contexts. To address this gap, this study adopts a case study approach to examine Foxconn’s BOMate platform as an example of AI-enabled supply chain management in advanced electronics manufacturing. The current case in study focuses on the AI-driven platform design principles which integrate experts’ rules, big data and AI modeling to augment procurement decision-making. The findings illustrate that BOMate contributes to SCM in several important ways. First, it augments human decision-making by combining expert rules, accumulated sourcing knowledge, and AI-based processing within a human-AI collaborative framework. Second, it enhances sourcing efficiency by accelerating component retrieval, substitute identification, and decision support. More broadly, the case suggests that AI in Foxconn’s supply chain is not limited to localized automation, but supports system-level coordination across procurement, production, and supply chain operations. This study contributes to the literature by providing empirical insight into AI-enabled SCM in a leading manufacturing firm and highlights the strategic importance of AI in improving supply chain efficiency, resilience, and technological transformation.