增材制造供应链弹性分析:一项探索性研究

Pinkesh K. Patel, F. Defersha, Sheng Yang
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引用次数: 2

摘要

前所未有的全球化水平和激烈的竞争使供应链变得前所未有的复杂和脆弱。自然灾害发生率的增加和前所未有的COVID-19凸显了通过暴露其对外部事件的敏感性来提高供应链弹性(SCR)的重要性。增材制造(AM)被认为是一种颠覆性技术,可以实现分层制造,并被认为是改进SCR的重要贡献者,因为它可以通过扩大设计自由度、提高材料效率、缩短供应链和分散制造来带来新的机会。然而,很少有研究定量地测量AM对SCR的影响。为了填补这一研究空白,本文首先提出了AM-SCs的SCR评估指标,然后采用与理想解决方案相似的偏好排序技术(TOPSIS)推导出可量化的SCR分数,用于衡量不同sc的绩效。以油门踏板总成为例,介绍了三种不同的SC配置:采用传统制造的原始总成、采用增材制造的原始总成和采用增材制造的重新设计总成。探索性研究表明,考虑增材制造的重新设计组件可将SCR提高200%。敏感性分析还表明,供应商的零件数量和反应时间是提高SCR的影响因素。最后,本文还讨论了所提出框架的挑战和局限性以及未来的研究范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resilience Analysis of Additive Manufacturing-enabled Supply Chains: An Exploratory Study
Unparalleled level of globalization and fierce competition have made supply chains (SCs) exceedingly complex and fragile as ever before. Increased incidences of natural disasters and unprecedented COVID-19 have highlighted the significance of improving supply chain resilience (SCR) by divulging its susceptibility to the external events. Additive manufacturing (AM) is envisioned as the disruptive technology that allows layer-wised fabrication and has been claimed to be an important contributor to the improved SCR as it could bring new opportunities through expanded design freedom, improved material efficiency, shortened supply chains, and decentralized manufacturing. Nonetheless, rare research has quantitatively measured the impacts of AM on SCR. To fill this research gap, the indices for assessing SCR of AM-enabled supply chains (AM-SCs) are first proposed, and then, the technique for order of preference by similarity to ideal solution (TOPSIS) is employed to derive a quantifiable SCR score that can be used to measure the performance of different SCs. A case study of a gas pedal assembly is presented with three different SC configurations: the original assembly with conventional manufacturing, original assembly with AM, and redesigned assembly with AM. The exploratory study shows that the redesigned assembly with AM considerations could improve the SCR by 200%. Sensitivity analysis also revealed that part count and reaction time of suppliers are influential factors of improving SCR. Last, challenges and limitations of the proposed framework are also deliberated upon alongside future research scope.
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