Revealing the compound interrelationships toward sustainable transition in semiconductor supply chain: A sensitivity analysis

Hailing Qiu, Shuan Wei Tseng, Xuan Zhang, Caiyan Huang and Kuo-Jui Wu

International Journal of Production Economics, 2024, vol. 271, issue C

Abstract: This study provides specific guidelines for allocating resources by determining the positive and negative interrelationships as well as the sensitive criteria and factors of the semiconductor supply chain. However, revealing these compound interrelationships enables the semiconductor supply chain to accelerate the sustainable transition effectively and efficiently. Previous studies have introduced the sustainable transition without considering the compound interrelationships for guiding the semiconductor supply chain. The proposed hybrid digital twin sensitivity analysis method used in this study integrates clustering, machine learning, a cloud model, and the Monte Carlo simulation method. The contributions of the study are as follows: (1) the discovered factors strengthen the theoretical basis of the sustainable transition; (2) the proposed hybrid method reveals compound interrelationships by utilizing insufficient data; and (3) these compound interrelationships are presented in a visual diagram to provide guidelines for allocating resources to promote the sustainable transition. In practice, the total chromium and power consumption are the sensitive criteria influencing sustainable finance; technological innovations are influenced by the revenue and production area.

Keywords: Semiconductor supply chain; Sustainable transition; Clustering; Machine learning; Cloud model; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:271:y:2024:i:c:s0925527324000756

DOI: 10.1016/j.ijpe.2024.109218

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