Industrial Management Technology in the Digital Age 4.0
Keywords:
Industrial Management Technology, Management, Quality ControlAbstract
This academic article explores and analyzes the role of industrial management technology in driving transformation and enhancing the competitiveness of the industrial sector in the digital age. It covers the application of key technologies, including the Internet of Things (IoT), Artificial Intelligence (AI), Automation and Robotics, Big Data Analytics, and Cloud Technology, across various dimensions of management, production, and quality control. The article presents the concepts and evolution of industrial management technology from its early stages to Industry 4.0, exploring the practical applications of these technologies in various industrial domains. It delves into the impact of these technologies on supply chain and logistics management, as well as the enhancement of quality control processes for greater precision and efficiency. Furthermore, the article introduces new knowledge gained from understanding the integration of technology to create smart factories and sustainable production. The conclusion emphasizes the importance of organizational and human adaptation to technological changes, providing recommendations for practical implementation to enable the Thai industrial sector to keep pace with global changes and gain a competitive advantage.
References
Accenture. (2023). The supply chain of the future: Digital logistics and automation. Accenture Insights. Retrieved from https://www.accenture.com
ASQ. (n.d.). What is quality control? American Society for Quality. Retrieved from https://asq.org
Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
Deloitte Insights. (2020). Digital transformation 2020: Strategy and leadership in the age of AI. Deloitte University Press.
Deloitte. (2020). Industry 4.0: The future of manufacturing. https://www2.deloitte.com/us/en/insights/focus/industry-4-0.html
Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412–421. https://doi.org/10.1016/j.dss.2012.05.048
Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412–421. https://doi.org/10.1016/j.dss.2012.05.048
European Commission. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union. https://doi.org/10.2777/308407
Ford, H. (1922). My life and work. Garden City, NY: Doubleday.
George, M. L., Rowlands, D., Price, M., & Maxey, J. (2022). The lean six sigma pocket toolbook (3rd ed.). McGraw-Hill.
Groover, M. P. (2015). Automation, production systems, and computer-integrated manufacturing (4th ed.). Upper Saddle River, NJ: Pearson.
Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for Industrie 4.0 scenarios: A literature review. In Proceedings of the 49th Hawaii International Conference on System Sciences (HICSS). https://doi.org/10.1109/HICSS.2016.488
IBM. (2024). How is AI being used in manufacturing. https://www.ibm.com/think/topics/ai-in-manufacturing
IBM. (n.d.). Blockchain for supply chain transparency. IBM Blockchain Solutions. Retrieved from https://www.ibm.com/blockchain
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry (Final report of the Industrie 4.0 Working Group). Forschungsunion.
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry (Final report of the Industrie 4.0 Working Group). Forschungsunion.
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. acatech—National Academy of Science and Engineering.
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing INDUSTRIE 4.0: Final report of the Industry 4.0 Working Group. acatech – National Academy of Science and Engineering.
Kim, T.-H., Yu, J., Park, S., & Kim, J. (2021). Product inspection methodology via deep learning. Sensors, 21(15), 5039. https://doi.org/10.3390/s21155039
Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety. META Group (Research Note).
Lee, J., Bagheri, B., & Kao, H. A. (2014). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.01.001
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001 ScienceDirect
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176–189. https://doi.org/10.1016/j.dss.2010.12.006
McKinsey & Company. (2021). The case for digital transformation: How technology is reshaping management and operations. McKinsey Digital.
McKinsey. (2021). Industry 4.0: Reimagining manufacturing operations. https://www.mckinsey.com/capabilities/operations/our-insights/industry-4-0-reimagining-manufacturing-operations
Mediavilla, M. A., et al. (2022). Review and analysis of artificial intelligence methods for supply chain management. Journal of Intelligent Manufacturing, 33(8), 2033–2062. https://doi.org/10.1007/s10845-022-01963-8 ScienceDirect
Mokyr, J. (1998). The second industrial revolution, 1870–1914. In The Oxford encyclopedia of economic history. Oxford University Press.
NetSuite. (2024). AI-driven quality inspection and manufacturing analytics. Oracle NetSuite White Paper. Retrieved from https://www.netsuite.com
Pournader, M., Shi, Y., Seuring, S., & Koh, S. C. L. (2021). Artificial intelligence applications in supply chain management: A systematic literature review. International Journal of Production Economics, 241, 108250. https://doi.org/10.1016/j.ijpe.2021.108250
Pournader, M., Shi, Y., Seuring, S., & Koh, S. C. L. (2021). Artificial intelligence applications in supply chain management: A systematic literature review. International Journal of Production Economics, 241, 108250. https://doi.org/10.1016/j.ijpe.2021.108250
Qi, M., Zhou, X., Zou, B., & Zhang, Y. (2018). On the evaluation of AGVS-based warehouse operation systems. Engineering Applications of Artificial Intelligence, 72, 135–146. https://doi.org/10.1016/j.engappai.2018.03.013
Ren, Z., et al. (2022). State of the art in defect detection based on machine vision. International Journal of Precision Engineering and Manufacturing-Green Technology, 9, 611–640. https://doi.org/10.1007/s40684-021-00343-6 SpringerLink
RF Page. (2025). Real-time quality monitoring in smart factories. RF Page Technology Review. Retrieved from https://rfpage.com
Robotized and automated warehouse systems: Review and recent developments. (2019). ResearchGate preprint. (Comprehensive review of shuttle-based AS/RS, robotic handling, etc.).
Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Pearson. (Global edition preview)
SAP. (n.d.). Manufacturing execution systems (MES) and ERP integration. SAP Knowledge Center. Retrieved from https://www.sap.com
Schwab, K. (2016). The Fourth Industrial Revolution. Geneva: World Economic Forum.
SHRM (Society for Human Resource Management). (2021). The future of work: A journey to 2022. https://www.shrm.org/hr-today/trends-and-forecasting/research-and-surveys/pages/future-of-work-report.aspx
Splunk. (n.d.). Predictive maintenance with IoT and machine learning. Splunk Industrial Data Analytics. Retrieved from https://www.splunk.com
Stevenson, W. J. (2021). Operations management (14th ed.). New York, NY: McGraw-Hill.
Taylor, F. W. (1911). The principles of scientific management. New York, NY: Harper & Brothers.
Tsai, C.-W., Lai, C.-F., & Chao, H.-C. (2015). Data mining for the Internet of Things: A survey. Information Sciences, 318, 64–86. https://doi.org/10.1016/j.ins.2015.02.027
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Wang, J., Ma, Y., & Wang, L. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. https://doi.org/10.1016/j.jmsy.2018.01.003
Womack, J. P., Jones, D. T., & Roos, D. (1990). The machine that changed the world. New York, NY: Rawson Associates.
Xu, L. D., He, W., & Li, S. (2014). Internet of Things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243. https://doi.org/10.1109/TII.2014.2300753
Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3), 2213–2227. https://doi.org/10.1109/JSYST.2019.2905565
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616–630. https://doi.org/10.1016/J.ENG.2017.05.015
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