基于可靠性方法的新兴经济体制造业选址决策支持

M. Gadalla, Ahmed E. Azab
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引用次数: 0

摘要

在当今的分布式制造现实中,世界各地的投资者都面临着为其制造工厂选择最佳地理位置的困境。一方面,新兴经济体可能具有吸引力,因为它们的廉价劳动力,以及在某些情况下可能缺乏或减少监管、诉讼和文书工作。另一方面,同样是这些新兴经济体,由于其政治体系缺乏稳定性,因此也会出现相关的经济波动,因此风险可能相当大。由于政治不稳定、腐败、缺乏民主和法治、社会和种族不公正以及宗教极端主义等因素,这些经济体可能在相对较短的时间内崩溃。在本文中,我们提出了一种建模方法,其中将经济表示为工程系统,其寿命受潜在条件、事件和失效模式的影响。这些脆弱经济体面临的条件和因素被建模为造成其不稳定的推动者和平减者。因此,在当今的全球气候下,可靠性工程的所有定律都可以用来决定这样一个系统的成功概率和它的生命周期,面对所有的不确定性和给定的风险。经济环境的健康是解决设施选址和配置问题的关键因素;这需要决定大型制造业投资的形式,包括正在建设的新制造工厂和伴随的供应链。在这种不确定的经济动荡之后,还讨论了允许可封装制造系统更容易搬迁的推动因素。系统动力学将作为未来的工作,在量化所提议的指标时,解释力(平减和推动)。人工智能和数据分析技术也被推荐用于量化可靠性参数。
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Decision Support for Locating Manufacturing Plants in Emerging Economies Using a Reliability Approach
In today’s distributed manufacturing reality, investors worldwide are faced with the dilemma of deciding on the optimal geographic spot for their manufacturing plants. On the one hand, emerging economies could be appealing because of their cheap labor as well as possibly their lack of or reduced regulations, litigation, and paperwork in some cases. On the other hand, these very same emerging economies can be quite risky because of the lack of stability of their political systems and hence, the associated economic volatility. Such economies can collapse in a relatively short period of time due to factors such as political instability, corruption, lack of democracy and the rule of law, social and racial injustices, and religious extremism, to name a few. In this paper, we propose a modeling approach where an economy is represented as an engineering system, the lifespan of which is subject to potential conditions, events, and failure modes. Such conditions and factors in the face of these fragile economies are modeled as pushers and deflators contributing to their instability. Hence, all laws of Reliability Engineering can be used to decide on the probability of success of such a system and its lifetime in the face of all uncertainty and given risks in today’s global climate. It is imperative that the health of the economic climate is a critical element solving the facility location and allocation problem; this entails deciding on large manufacturing investments in the form of new manufacturing plants being constructed and the accompanied supply chains. Enablers to allow for packageable manufacturing systems easier to relocate in the wake of this uncertain economic turmoil are also discussed. System Dynamics will be used as future work to account for the forces (deflators and pushers) when quantifying the proposed metrics. AI and Data Analytics techniques are also recommended to quantify the reliability parameters.
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