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http://dx.doi.org/10.7469/JKSQM.2022.50.4.647

Discovering Essential AI-based Manufacturing Policy Issues for Competitive Reinforcement of Small and Medium Manufacturing Enterprises  

Kim, Il Jung (Head of Manufacturing AI Bigdata Centre, Korea Advanced Institute of Science and Technology)
Kim, Woo Soon (Ministry of SMEs and Startups)
Kim, Joon Young (New Technology Investment Team 2, SK Securities)
Chae, Hee Su (Department of Business Administration, Hanyang University)
Woo, Ji Yeong (Department of Manufacturing AI Bigdata Centre, Korea Advanced Institute of Science and Technology)
Do, Kyung Min (Department of Manufacturing AI Bigdata Centre, Korea Advanced Institute of Science and Technology)
Lim, Sung Hoon (Department of Industrial Engineering, Ulsan National Institute of Science and Technology)
Shin, Min Soo (Department of Business Administration, Hanyang University)
Lee, Ji Eun (Department of MIS and AI Business, Hanyang Cyber University)
Kim, Heung Nam (K-Industry4.0 Headquaters, Korea Advanced Institute of Science and Technology)
Publication Information
Journal of Korean Society for Quality Management / v.50, no.4, 2022 , pp. 647-664 More about this Journal
Abstract
Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.
Keywords
Manufacturing AI Policy; Manufacturing Competitiveness; Manufacturing SMEs; Manufacturing Data; Digital Transformation;
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Times Cited By KSCI : 6  (Citation Analysis)
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