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An Assessment of Smart Factories in Korea: An Exploratory Empirical Investigation
Applied Sciences ( IF 2.5 ) Pub Date : 2020-10-25 , DOI: 10.3390/app10217486
Minjae Ko , Chul Kim , Seunghoon Lee , Yongju Cho

After the Industry 4.0 discussion in Germany in 2011, much attention has been paid to smart factory in Korea. Since 2014, smart factories have been established and expanded in Korea. However, about 80% of them were established at a low level. In this paper, we analyze smart factory statuses in detail through an empirical research on 113 manufacturing companies that have established smart factories in Korea. We build a framework based on the resource-based view (RBV) and IT value creation process and analyze the results of five constructs—manufacturing strategy, organization, system, process, and performance—using basic statistical methodologies to derive the current statuses of manufacturing companies that have established smart factories. Our results show that implementing advanced technologies such as AI technology that can implement semi-finished and finished product quality inspection, manufacturing process optimization and product demand forecast is a challenge, particularly for SMEs. We also find that securing and managing facility data is a difficult problem. In addition, while output and material management ranked high, the utilization of integration systems, which is important when building a smart factory, was found to be extremely low. Lastly, the performance indicator results showed that yield management and defect rate were most important, while job creation through the introduction of smart factories was low. Based on the results of this study, the government may be able to determine effective smart factory policies and provide manufacturing companies with a guide on establishing a smart factory.

中文翻译:

对韩国智能工厂的评估:探索性的实证研究

在2011年德国工业4.0讨论之后,韩国的智能工厂受到了很多关注。自2014年以来,智能工厂已在韩国建立和扩展。但是,其中约有80%处于较低水平。本文通过对在韩国建立智能工厂的113家制造公司进行的实证研究,详细分析了智能工厂的状况。我们基于资源基础的视图(RBV)和IT价值创造过程构建一个框架,并使用基本的统计方法来分析五种结构的结果-制造策略,组织,系统,过程和绩效-使用基本统计方法来得出制造的当前状态建立了智能工厂的公司。我们的结果表明,实施AI技术等先进技术可以实现半成品和成品质量检验,制造工艺优化和产品需求预测,这对中小企业尤其是一个挑战。我们还发现保护和管理设施数据是一个难题。此外,虽然产量和物料管理排名很高,但是发现集成系统的利用率非常低,而集成系统在构建智能工厂时很重要。最后,绩效指标结果表明,产量管理和缺陷率是最重要的,而引入智能工厂所创造的就业机会却很少。根据这项研究的结果,
更新日期:2020-10-28
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