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<p>Wafer foundries face cultural differences when building localized wafer fabs and have proposed multiple measures to mitigate the impact of cultural differences. However, given the limited budget, time, and other resources, it is critical to prioritize these measures. Due to the lack of experience and references, how to prioritize criteria and evaluate the performances of measures remains indeterminate. Decision makers also need a highly flexible decision-making process to respond to changing local conditions. This issue is urgent and important for foundries, but there is a clear gap between theory and practice. To fill this gap, this study proposes a neutrosophic collaborative intelligence (NCI) approach, in which experts’ judgments are more reasonably aggregated and the indeterminacy of experts’ judgments is retained until the last step, thereby making a flexible decision, which is distinct from previous studies. In the NCI approach, each expert uses the calibrated neutrosophic geometric mean to derive the neutrosophic weights of criteria for prioritizing mitigating measures. The neutrosophic technique for order of preference by similarity to the ideal solution is then used to evaluate the suitability of each measure. Finally, the neutrosophic weighted intersection operator is devised to aggregate the evaluation results by all experts. A systematic procedure is also established to prioritize these measures. The NCI approach has been applied to a real case. According to the experimental results, two out of the three experts emphasized legal resolvability as the most decisive factor behind why some mitigating measures were more favorable than others. The most suitable measure for mitigating the impact of cultural differences was accelerating yield learning through early mass production, while the least suitable measure was designing a staggered shift system. Other conclusions were indeterminate because experts’ more uncertain judgments had a greater impact on the two measures to be compared than their more certain judgments, which can be resolved by experts modifying their judgments.</p>

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Prioritizing Measures to Mitigate the Impact of Cultural Differences on Semiconductor Supply Chain Localization: A Neutrosophic Collaborative Intelligence Approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月25日

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  • Min-Chi Chiu,
  • Tin-Chih Toly Chen

摘要

Wafer foundries face cultural differences when building localized wafer fabs and have proposed multiple measures to mitigate the impact of cultural differences. However, given the limited budget, time, and other resources, it is critical to prioritize these measures. Due to the lack of experience and references, how to prioritize criteria and evaluate the performances of measures remains indeterminate. Decision makers also need a highly flexible decision-making process to respond to changing local conditions. This issue is urgent and important for foundries, but there is a clear gap between theory and practice. To fill this gap, this study proposes a neutrosophic collaborative intelligence (NCI) approach, in which experts’ judgments are more reasonably aggregated and the indeterminacy of experts’ judgments is retained until the last step, thereby making a flexible decision, which is distinct from previous studies. In the NCI approach, each expert uses the calibrated neutrosophic geometric mean to derive the neutrosophic weights of criteria for prioritizing mitigating measures. The neutrosophic technique for order of preference by similarity to the ideal solution is then used to evaluate the suitability of each measure. Finally, the neutrosophic weighted intersection operator is devised to aggregate the evaluation results by all experts. A systematic procedure is also established to prioritize these measures. The NCI approach has been applied to a real case. According to the experimental results, two out of the three experts emphasized legal resolvability as the most decisive factor behind why some mitigating measures were more favorable than others. The most suitable measure for mitigating the impact of cultural differences was accelerating yield learning through early mass production, while the least suitable measure was designing a staggered shift system. Other conclusions were indeterminate because experts’ more uncertain judgments had a greater impact on the two measures to be compared than their more certain judgments, which can be resolved by experts modifying their judgments.