在端到端的半导体供应链中通过可解释的机器学习进行中断评估
Friedrich-Maximilian Jaenichen1, Christina J Liepold2, Abdelgafar Ismail1
1Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.
概括
本研究引入了一种混合模拟和机器学习模型来分析供应链中断. 它为半导体公司提供了减轻需求波动和改善库存管理的策略.
科学领域:
- 运营研究 运营研究
- 供应链管理 供应链管理
- 人工智能的人工智能
背景情况:
- COVID-19大流行严重扰乱了全球供应链,特别是影响了汽车和半导体等行业.
- 了解和减轻最终市场需求中断的影响对于经济稳定至关重要.
研究的目的:
- 开发和应用一种混合方法,将模拟建模和机器学习结合起来,用于分析供应链漏洞.
- 为半导体公司提供可操作的见解,以管理需求侧风险.
主要方法:
- 采用了与重生树集团集成的供应链模拟模型,是一种基于树的监督机器学习分类器.
- 研究了不同行为和结构参数对关键绩效指标 (如库存水平) 的影响.
- 在模拟中使用反事实分析来获得管理洞察力和测试的缓解策略.
主要成果:
- 在分析模拟数据和确定影响供应链性能的关键参数方面证明了再生树组合的有效性.
- 量化参数变化对库存水平和其他关键绩效指标的影响.
- 确定了具体的管理策略,以减轻需求中断的不利影响.
结论:
- 混合模拟-机器学习方法为理解和管理供应链风险提供了一个强大的框架.
- 调查结果为半导体公司提供了基于数据的见解,以提高对需求波动的弹性.
- 将反事实分析结果重新整合到模拟模型中,可以加深对多层次供应链动态的理解.
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