The STIC Analysis: A Decision Support Method for Investments in Automation

M. Bonini, M. Mete, T. Nguyen, Augusto Urru, W. Echelmeyer
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引用次数: 1

Abstract

Compared to the automotive sector, where automation is the rule, in many other less standardized sectors automation is still the exception. This could soon hurt the productivity of industrialized countries, where the unemployment is low and the population is aging. Phenomena like the recent downfall in productivity, due to lockdowns and social distancing for prevention of health hazards during the COVID19 pandemic, only add to the problem. For these reasons, the relevance, motivation and intention for more automation in less standardized sectors has probably never been higher. However, available statistics say that providers and users of technologies struggle to bring more automation into action in automation-unfriendly sectors. In this paper, we present a decision support method for investment in automation that tackles the problem: the STIC analysis. The method takes a holistic and quantitative approach tying together technological, context-related and economic input parameters and synthetizing them in a final economic indicator. Thanks to the modelling of such parameters, it is possible to gain sensibility on the technological and/or process adjustments that would have the highest impact on the efficiency of the automation, thereby delivering value for both technology users and technology providers.
STIC分析:自动化投资的决策支持方法
与汽车行业相比,自动化是规则,在许多其他不太标准化的行业,自动化仍然是例外。这可能很快就会损害工业化国家的生产率,这些国家的失业率很低,人口正在老龄化。在2019冠状病毒病大流行期间,为预防健康危害而实施的封锁和保持社交距离导致生产力下降,这类现象只会加剧这一问题。由于这些原因,在标准化程度较低的行业实现更多自动化的相关性、动机和意图可能从未像现在这样高。然而,现有的统计数据表明,技术提供商和用户很难在自动化不友好的行业中实现更多的自动化。在本文中,我们提出一种自动化投资决策支持方法来解决这个问题:STIC分析。该方法采用整体和定量的方法,将技术、环境相关和经济投入参数联系在一起,并将它们综合为最终的经济指标。由于这些参数的建模,有可能获得对自动化效率影响最大的技术和/或过程调整的敏感性,从而为技术用户和技术提供者提供价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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