THE ROLE OF DIGITAL TWIN TECHNOLOGY IN ENHANCING INVENTORY VISIBILITY AND REDUCING BOTTLENECKS IN COMPLEX MANUFACTURING SYSTEMS
DOI:
https://doi.org/10.51699/4t1d7131Keywords:
Digital Twin, Inventory Visibility, Bottleneck Reduction, Manufacturing SystemsAbstract
Industry 4.0 technologies as well as, Digital Twin technology has significantly changed the manufacturing industry as it allows real-time monitoring, simulating, and analyzing physical systems. The study examines how Digital Twin can be used to improve inventory visibility and reduce the number of bottlenecks in complex manufacturing environments. Digital Twin can be used to minimize inefficiencies and transform the overall work performance through the increased transparency of supply chains and streamlined production processes. Methods: The research methodology was quantitative with the secondary data sources at Bureau of Economic Analysis (BEA). The correlation analysis, paired t-tests, and multiple regression analysis were employed to evaluate the correlation between Digital Twin technology and the most important manufacturing parameters, including inventory management and production efficiency. Results: The findings show that Digital Twin technology can have a strong positive impact on the production efficiency and inventory visibility, especially with the help of real-time data and foretelling analytic tools. Nonetheless, the decrease in bottlenecks was not as significant as it should have been, and Digital Agility and Digital Flexibility had minimal impacts on reduction of the bottlenecks. The paper also indicates that Digital Twin technology will be beneficial in streamlining manufacturing, but the capability of workforce and system integration have to be tackled. Conclusion: Digital Twin can provide a viable option as a tool to improve inventory control and to minimize bottlenecks in manufacturing, to allow the creation of leaner, responsive, and greener manufacturing systems.
References
[1] X. Li, H. Liu, W. Wang, Y. Zheng, H. Lv, and Z. Lv, “Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning,” Future Generation Computer Systems, vol. 128, pp. 167–177, 2022, doi: 10.1016/j.future.2021.10.006.
[2] J. Li, Y. Zhang, and C. Qian, “The enhanced resource modeling and real-time transmission technologies for Digital Twin based on QoS considerations,” Robot. Comput. Integr. Manuf., vol. 75, p. 102284, 2022, doi: 10.1016/j.rcim.2021.102284.
[3] C. L. Stergiou and K. E. Psannis, “Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments,” Virtual Reality & Intelligent Hardware, vol. 4, no. 4, pp. 279–291, 2022, doi: 10.1016/j.vrih.2022.05.003.
[4] J. Bao, D. Guo, J. Li, and J. Zhang, “The modelling and operations for the digital twin in the context of manufacturing,” Enterp. Inf. Syst., vol. 13, no. 4, pp. 534–556, 2019, doi: 10.1080/17517575.2018.1526324.
[5] Q. Qi et al., “Enabling technologies and tools for digital twin,” J. Manuf. Syst., vol. 58, pp. 3–21, 2021, doi: 10.1016/j.jmsy.2019.10.001.
[6] Y. H. Pan, T. Qu, N. Q. Wu, M. Khalgui, and G. Q. Huang, “Digital Twin Based Real-time Production Logistics Synchronization System in a Multi-level Computing Architecture,” J. Manuf. Syst., vol. 58, pp. 246–260, 2021, doi: 10.1016/j.jmsy.2020.10.015.
[7] M. Hajmirfattahtabrizi and H. Song, “Investigation of Bottlenecks in Supply Chain System for Minimizing Total Cost by Integrating Manufacturing Modelling Based on MINLP Approach,” Applied Sciences, vol. 9, no. 6, p. 1185, 2019, doi: 10.3390/app9061185.
[8] L. Li, Q. Chang, J. Ni, and S. Biller, “Real time production improvement through bottleneck control,” Int. J. Prod. Res., vol. 47, no. 21, pp. 6145–6158, 2009, doi: 10.1080/00207540802244240.
[9] J. Gejo-García, J. Reschke, S. Gallego-García, and M. García-García, “Development of a System Dynamics Simulation for Assessing Manufacturing Systems Based on the Digital Twin Concept,” Applied Sciences, vol. 12, no. 4, p. 2095, 2022, doi: 10.3390/app12042095.
[10] M. Kumbhar, A. H. C. Ng, and S. Bandaru, “A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks,” J. Manuf. Syst., vol. 66, pp. 92–106, 2023, doi: 10.1016/j.jmsy.2022.11.016.
[11] K. Zhang et al., “Digital twin-based opti-state control method for a synchronized production operation system,” Robot. Comput. Integr. Manuf., vol. 63, p. 101892, 2020, doi: 10.1016/j.rcim.2019.101892.
[12] P. Maheshwari and S. Kamble, “The Application of Supply Chain Digital Twin to Measure Optimal Inventory Policy,” IFAC-PapersOnLine, vol. 55, no. 10, pp. 2324–2329, 2022, doi: 10.1016/j.ifacol.2022.10.055.
[13] M. Attaran and B. G. Celik, “Digital Twin: Benefits, use cases, challenges, and opportunities,” Decision Analytics Journal, vol. 6, p. 100165, 2023, doi: 10.1016/j.dajour.2023.100165.
[14] K. Chen et al., “Digital Twins and Data-Driven in Plant Factory: An Online Monitoring Method for Vibration Evaluation and Transplanting Quality Analysis,” Agriculture, vol. 13, no. 6, p. 1165, 2023, doi: 10.3390/agriculture13061165.
[15] K. Chen et al., “Digital Twins in Plant Factory: A Five-Dimensional Modeling Method for Plant Factory Transplanter Digital Twins,” Agriculture, vol. 13, no. 7, p. 1336, 2023, doi: 10.3390/agriculture13071336.
[16] T. Y. Melesse, V. Di Pasquale, and S. Riemma, “Digital Twin Models in Industrial Operations: A Systematic Literature Review,” Procedia Manuf., vol. 42, pp. 267–272, 2020, doi: 10.1016/j.promfg.2020.02.084.
[17] A. Z. Abideen, V. P. K. Sundram, J. Pyeman, A. K. Othman, and S. Sorooshian, “Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics,” Logistics, vol. 5, no. 4, p. 84, 2021, doi: 10.3390/logistics5040084.
[18] K. Zhang et al., “Digital twin-based opti-state control method for a synchronized production operation system,” Robot. Comput. Integr. Manuf., vol. 63, p. 101892, Jun. 2020, doi: 10.1016/j.rcim.2019.101892.
[19] S. Taj, A. S. Imran, Z. Kastrati, S. M. Daudpota, R. A. Memon, and J. Ahmed, “IoT-based supply chain management: A systematic literature review,” Internet of Things, vol. 24, p. 100982, Dec. 2023, doi: 10.1016/j.iot.2023.100982.
[20] T. Moshood, G. Nawanir, S. Sorooshian, and O. Okfalisa, “Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics,” Applied System Innovation, vol. 4, no. 2, p. 29, Apr. 2021, doi: 10.3390/asi4020029.
[21] J. Wan et al., “Wearable IoT enabled real-time health monitoring system,” EURASIP J. Wirel. Commun. Netw., vol. 2018, no. 1, p. 298, Dec. 2018, doi: 10.1186/s13638-018-1308-x.
[22] E. Borandag, “A Blockchain-Based Recycling Platform Using Image Processing, QR Codes, and IoT System,” Sustainability, vol. 15, no. 7, p. 6116, Apr. 2023, doi: 10.3390/su15076116.
[23] P Choudhury and M Sharfuddin, “Management Information Systems in Contemporary Business: Prospects, Strategic Advantages, and Future Challenges,” Business & Social Sciences, vol. 1, no. 1, pp. 1–7, Jan. 2023, doi: 10.25163/business.1110509.
[24] B. Arff, J. Haasis, J. Thomas, C. Bonenberger, W. Höpken, and R. Stetter, “Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT,” Applied Sciences, vol. 13, no. 6, p. 3525, Mar. 2023, doi: 10.3390/app13063525.
[25] F. Tahmasebinia, L. Lin, S. Wu, Y. Kang, and S. Sepasgozar, “Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy,” Applied Sciences, vol. 13, no. 15, p. 8814, Jul. 2023, doi: 10.3390/app13158814.
[26] M. Attaran and B. G. Celik, “Digital Twin: Benefits, use cases, challenges, and opportunities,” Decision Analytics Journal, vol. 6, p. 100165, Mar. 2023, doi: 10.1016/j.dajour.2023.100165.
[27] Q. Qi et al., “Enabling technologies and tools for digital twin,” J. Manuf. Syst., vol. 58, pp. 3–21, Jan. 2021, doi: 10.1016/j.jmsy.2019.10.001.
[28] J. Li, Y. Zhang, and C. Qian, “The enhanced resource modeling and real-time transmission technologies for Digital Twin based on QoS considerations,” Robot. Comput. Integr. Manuf., vol. 75, p. 102284, Jun. 2022, doi: 10.1016/j.rcim.2021.102284.
[29] MR Haque and MNM Khan, “Strategic Integration of Big Data Analytics to Enhance Risk Management, Sustainable Supply Chains, and Business Competitiveness in the United States,” Business & Social Sciences, vol. 1, no. 1, pp. 1–7, Jan. 2023, doi: 10.25163/business.1110498.
[30] D. Ivanov and A. Dolgui, “A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0,” Production Planning & Control, vol. 32, no. 9, pp. 775–788, Jul. 2021, doi: 10.1080/09537287.2020.1768450.
[31] A. Z. Abideen, V. P. K. Sundram, J. Pyeman, A. K. Othman, and S. Sorooshian, “Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics,” Logistics, vol. 5, no. 4, p. 84, Nov. 2021, doi: 10.3390/logistics5040084.
[32] P Choudhury and N Imtiaz, “Overcoming Data Excess to Improve Decision-Making and Information Systems Plans for Organizational Performance,” Journal of Primeasia, vol. 1, no. 3, pp. 1–7, Jan. 2020, doi: 10.25163/primeasia.1110403.
[33] D. Zhong, Z. Xia, Y. Zhu, and J. Duan, “Overview of predictive maintenance based on digital twin technology,” Heliyon, vol. 9, no. 4, p. e14534, Apr. 2023, doi: 10.1016/j.heliyon.2023.e14534.
[34] G. Falekas and A. Karlis, “Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects,” Energies (Basel)., vol. 14, no. 18, p. 5933, Sep. 2021, doi: 10.3390/en14185933.
