Journal of Information Science Theory and Practice

Korea Institute of Science and Technology Information (한국과학기술정보연구원 과학기술정보센터)
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An Economic Ripple Effect Analysis of Domestic Supercomputing Simulation in the Industrial Sector

  • Ko, Mihyun (Intelligent Simulation Center, Korea Institute of Science and Technology Information (KISTI)) ;
  • Kim, Myungil (Intelligent Simulation Center, Korea Institute of Science and Technology Information (KISTI)) ;
  • Park, Sung-Uk (Department of Industry-University Convergence, Hanbat National University)
  • Received : 2022.04.25
  • Accepted : 2022.06.20
  • Published : 2022.06.20
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Abstract

The manufacturing industry is the foundation that drives economic growth, and manufacturing innovation is essential for sustainable growth advantage and the transition into a digital economy. Therefore, major countries actively support the field of simulations, which incorporate information and communication technologies into manufacturing, and announce various policies at the national level along with increasing investment. Simulation technology virtualizes product development processes to replace physical production and experimentation of products, dramatically reducing time and costs. In South Korea, the Korea Institute of Science and Technology Information (KISTI) has supported manufacturing companies for about 14 years by providing relevant technologies. This study uses the input-output table for the Bank of Korea to analyze the economic ripple effect. First, we identified the domestic industrial sector dealing with the supercomputing-based simulation industry. Then we analyzed its ripple effects by dividing them into the production inducement effect, value-added inducement effect, employment inducement effect, and forward/backward linkage effect. Consequently, when the supercomputing simulation budget of KISTI (28.3 billion won, 2007-2020) was set as an input coefficient, the analysis showed 45.1 billion won as the production inducement effect, 24.7 billion won as the value-added inducement effect, and 282 individuals per 1 billion won as the employment inducement effect. This study is significant in that it derived the effects of the inputs by analyzing the economic ripple effects of the projects of KISTI, which have been supporting South Korean manufacturing companies for the past 14 years with supercomputing-based simulations.

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References

  1. Bank of Korea. (2021). 2019 Industry linkage table extension table. Economics Statistics System.
  2. Bizwit Research & Consulting. (2021). Global simulation software market size study, by component, by deployment, by end-use industry and regional forecasts 2020-2027. Bizwit Research & Consulting.
  3. Correa, N., & Todorov, V. (2021). Competitive industrial performance report 2020. United Nations Industrial Development Organization.
  4. Diallo, S. Y., Gore, R. J., Padilla, J. J., & Lynch, C. J. (2015). An overview of modeling and simulation using content analysis. Scientometrics, 103(3), 977-1002. https://doi.org/10.1007/s11192-015-1578-6.
  5. DoD Instruction, DODD 5000.61. (2009). Modeling and simulation (M&S) verification, validation, and accreditation (VV&A). Department of Defense.
  6. Hong, K. W., Kim, H. C., & Lee, J. T. (2010). The analysis of economic effects of school foodservice using the input-output analysis -a case of elementary school foodservice at Naju city, Cheonnam province-. Journal of the Korea AcademiaIndustrial Cooperation Society, 11(10), 3747-3755. https://doi.org/10.5762/KAIS.2010.11.10.3747.
  7. Hwang, K. I. (2021). Korea's manufacturing competitiveness, the pillar of the COVID-19 economic crisis. i-KIET Industrial and Economic Issues, 108, 1-12. https://www.kiet.re.kr/research/issueView?issue_no=746.
  8. Jee, B. G., Kim, T., & Lee, G. (2011). Economic impact of tourism industry in Korea -an input-output analysis-. Journal of the Korea Academia-Industrial Cooperation Society, 12(7), 3039-3045. https://doi.org/10.5762/KAIS.2011.12.7.3039.
  9. Kagermann, H., Wahlster, W., & Helbig, J. (2013). Securing the future of German manufacturing industry. Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Forschungsunion, Plattform Industrie 4.0.
  10. Kim, M., Jung, J., Han, Y., Park, S. U., & Kim, J. (2017). An analysis and industrial classification of modeling and simulation service industry. Journal of the Korea AcademiaIndustrial Cooperation Society, 18(3), 185-198. https://doi.org/10.5762/KAIS.2017.18.3.185.
  11. Kim, M., Park, S. U., & Kim, J. (2016). An economic ripple effect analysis of domestic supercomputing modeling and simulation. Journal of the Korea Academia-Industrial Cooperation Society, 17(11), 340-347. https://doi.org/10.5762/KAIS.2016.17.11.340.
  12. KISTI. (2021). Development of supercomputing applied technologies to solve engineering issues in the industrial and public sectors. https://www.ntis.go.kr/project/pjtInfo.do?pjtId=1711149493&pageCode=TH_PJT_PJT_DTL.
  13. Lavery, G., Pennell, N., Brown, S., & Evans, S. (2013). The next manufacturing revolution: Non-labour resource productivity and its potential for UK manufacturing. Institute for Manufacturing, Cambridge University.
  14. Lee, S., & Ko, M. (2018). Exploring the key technologies on next production innovation. Journal of the Korea Convergence Society, 9(9), 199-207. https://doi.org/10.15207/JKCS.2018.9.9.199.
  15. Ministry of National Defense. (2014). Defense interoperability management directive. https://www.korea.kr/archive/expDocView.do?docId=36947.
  16. National Science Foundation. (2006). Simulation-based engineering science: Revolutionizing engineering science through simulation. National Science Foundation.
  17. OECD. (2016). Enabling the next production revolution: The future of manufacturing and services - interim report. OECD.
  18. Park, S. U. (2018). An economic ripple effect analysis of National Science & Technology Information Service: Focusing an input-output analysis. Journal of Korea Technology Innovation Society, 21(4), 1296-1312. https://www.koreascience.or.kr/article/JAKO201819867049447.page.
  19. Park, S. U., & Hahn, S. H. (2011). An economic ripple effect analysis of national scientific data center construction. Journal of Information Management, 42(3), 55-69. https://doi.org/10.1633/JIM.2011.42.3.055.
  20. Related Ministries Joint. (2020). AI/data-based SME manufacturing innovation advancement strategy. Related Ministries Joint.
  21. Scientific Computing World. (2019). Advancing the industrial internet with simulation-based digital twins. https://www.scientific-computing.com/analysis-opinion/advancingindustrial-internet-simulation-based-digital-twins.
  22. Sokolowski, J. A., & Banks, C. M. (2008). Principles of modeling and simulation: A multidisciplinary approach. Wiley.
  23. World Economic Forum. (2021). Global lighthouse network: Unlocking sustainability through fourth industrial revolution technologies. World Economic Forum.
  24. Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000). Theory of modeling and simulation: Integrating discrete event and continuos complex dynamic systems. 2nd ed. Academic Press.