Identification of Key Factors of Digital Transformation of Manufacturing companies Using Hybrid DEMATEL Method
DOI:
https://doi.org/10.31181/dmame712024931Keywords:
Digital transformation, Hybrid DEMATEL, Key factors, Manufacturing companiesAbstract
This study constructs the influencing factors system of digital transformation of manufacturing companies and determines the influencing factors of digital transformation of manufacturing companies from the technical level, the ability level, the environmental level in order to promote the process of digital transformation of manufacturing companies, consider the impact of technology, capacity and environment on digital transformation of manufacturing. To identify the most important factors and crucial factors, developed a model for identifying critical factors based on the hybrid DEMATEL (Decision-Making Trial and Evaluation Laboratory) methodology. Combined with the related data, this study carries out empirical analysis to demonstrate and verify the influencing factors system of digital transformation of manufacturing companies with the help of hybrid DEMATEL method. The empirical analysis results indicate that the investment share of digital technology, the benefit level of company of manufacturing industry and government support are the crucial factors, and the construction level of digital platform, the technical level of employees, level of construction of digitized equipment and industrial supporting capacity are the non crucial factors. The feasibility and validity of hybrid DEMATEL method are verified.
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Zhou, J., Li, P., Zhou, Y., Wang, B., Zang, J., & Meng, L. (2018). Toward new-generation Intelligent manufacturing. Engineering, 4(1), 11-20. https://doi.org/10.1016/j.eng.2018.01.002
Xiao, J., Wang, Q., & Ou, S. (2021). Research on evaluation model of regional manufacturing capability under digital and Intelligent transformation. In Journal of Physics: Conference Series, 2044(1), 012128. https://doi.org/10.1088/1742-6596/2044/1/012128
Yu, Y., Zhang, J. Z., Cao, Y., & Kazancoglu, Y. (2021). Intelligent transformation of the manufacturing companies for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management. Technological Forecasting and Social Change, 172, 120999. https://doi.org/10.1016/j.techfore.2021.120999
Wang, Y., & Han, M. (2020). Research on the impact mechanism of organizational-based psychological ownership on the Intelligent transformation of manufacturing companies: Based on the perspective of technological change. Psychology Research and Behavior Management, 13, 775-786. https://doi.org/10.2147/PRBM.S264662
Liu, X. Y., Cheng, Q., & Guo, K. (2017). Influencing factors of Intelligent manufacturing: Empirical analysis based on SVR model. Procedia Computer Science, 122, 1024-1030. https://doi.org/10.1016/j.procs.2017.11.469
Stadnicka, D., Litwin, P., & Antonelli, D. (2019). Human factor in Intelligent manufacturing systems-knowledge acquisition and motivation. Procedia CIRP, 79, 718-723. https://doi.org/10.1016/j.procir.2019.02.023
Zhou, Y., & Chuah, K. B. (2000). Human intelligence: The key factor for successful Intelligent manufacturing. Integrated Manufacturing Systems. https://doi.org/10.1108/09576060010303659
Sim, H. S. (2021). Hierarchical factor analysis methodology for Intelligent manufacturing. Complexity, 2021, 1-18. https://doi.org/10.1155/2021/5593374
Yang, J., Ying, L., & Gao, M. (2020). The influence of Intelligent manufacturing on financial performance and innovation performance: The case of China. Enterprise Information Systems, 14(6), 812-832. https://doi.org/10.1080/17517575.2020.1746407
Zhong, M. C., & Fu, C. H. (2017). Research on digital transformation and upgrading of manufacturing companies in Jinjiang City Based on SWOT analysis. Journal of Anshan Normal College, 19(3), 24-30.
Su, B., & Yang. S. L. (2018). Research on Influencing Factors of digital transformation and upgrading of manufacturing companies based on grounded theory. Science and Technology Management Research, 38(8), 115-123.
Jiyuan, Z., Baicun, W., Liu, M., & Yuan, Z. (2018). Brief analysis on three basic paradigms of Intelligent manufacturing. Strategic Study of Chinese Academy of Engineering, 20(4), 13-18. https://doi.org/10.15302/J-SSCAE-2018.04.003
Kalyaev, I. A. (2019). The transition to advanced digital, Intelligent manufacturing technologies, robotic systems, new materials and methods of design, the creation of systems for processing large amounts of data, machine learning and artificial intelligence. Вестник Российской академии наук, 89(4), 348-350.
Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in digital manufacturing: A review. Frontiers of Information Technology and Electronic Engineering, 18(1), 86-96. https://doi.org/10.1631/FITEE.1601885
He, B., & Bai, K. J. (2021). Digital twin-based sustainable Intelligent manufacturing: A review. Advances of manufacturing, 9(1), 1-21. https://doi.org/10.1007/s40436-020-00302-5
Tschirky, H., Escher, J. P., Tokdemir, D., & Belz, C. (2000). Technology marketing: A new core competence of technology-intensive companies. International Journal of Technology Management, 20(3-4), 459-474. https://doi.org/10.1504/IJTM.2000.002874
Yan, D., & Xu, K. J. (2020). Theory and method of Intelligent manufacturing capacity evaluation. Quality and Certification, (11), 54-56. https://doi.org/10.16691/j.cnki.10-1214/t.2020.11.005
Lai, D., Liu, B. T., Hu, Y., & Liu, Y. L. (2020). Intelligent manufacturing capability evaluation diagnosis based on maturity model. Small and Medium-Sized Enterprise Management and Technology, (8), 155-156.
Chen, G., Xue, L., & Zhang, B. (2020). Evaluation of Intelligent manufacturing capability based on genetic algorithms projection tracking model. Shenzhen Social Sciences, 3, 41-49.
Pan, J. (2018). From government central regulation to social co-governance: A new vision of Internet financial governance. Legal Science, (1), 67-77. https://doi.org/10.16290/j.cnki.1674-5205.2018.01.007
Grodach, C., & Gibson, C. (2019). Advancing manufacturing? Blinkered visions in US and Australian urban policy. Urban Policy and Research, 37(3), 279-293. https://doi.org/10.1080/08111146.2018.1556633
Zhao, X. E. (2020). Enhance supply chain and industrial chain collaboration supporting capacity. Xinjiang Nonferrous Metals, 43(4), 104-105. https://doi.org/10.16206/j.cnki.65-1136/tg.2020.04.044
Si, S. L., You, X. Y., Liu, H. C., & Zhang, P. (2018). DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications. Mathematical Problems in Engineering, 2018, 3696457. https://doi.org/10.1155/2018/3696457
Sun, Y. H., Huang. Z. H., & Li. Y. (2022). DEMATEL complex factor analysis algorithms recent progress review. Computer science and Exploration, 16(3), 11. https://doi.org/10.3778/j.issn.1673-9418.2106089
Zhou, Q., Huang, W., & Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety science, 49(2), 243-252. https://doi.org/10.1016/j.ssci.2010.08.005
Li, Y., Hu, Y., Zhang, X., Deng, Y., & Mahadevan, S. (2014). An evidential DEMATEL method to identify critical success factors in emergency management. Applied Soft Computing, 22, 504-510. https://doi.org/10.1016/j.asoc.2014.03.042
Jin, W. J., Huang, C. F., Qiu, D. F., & Xu, H. (2020). Identification of key factors of emergency material support capability based on mixed decision making. Operation Research and Management, 29(11), 78-83.
Liu, P. D., & Guan, Z. L. (2009). A hybrid multi-attribute decision making method based on binary semantics. Control and Decision, 24(7), 1074-1077+1082. https://doi.org/10.13195/j.cd.2009.07.116.liupd.032
Dytczak, M., & Ginda, G. (2013). Is explicit processing of fuzzy direct influence evaluations in DEMATEL indispensable? Expert Systems with Applications, 40(12), 5027-5032. https://doi.org/10.1016/j.eswa.2013.02.034
Wang, Z., Shao, L., & Yang, C. (2023). Fuzzy prediction of the mine’s ventilation structure’s tunnel air volume. Expert Systems with Applications, 231, 120843. https://doi.org/10.1016/j.eswa.2023.120843
Zhou, Q., Huang, W. L., & Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Science, 49, 243-252. https://doi.org/10.1016/j.ssci.2010.08.005
Bhatia, M. S., & Srivastava, R. K. (2018). Analysis of external barriers to remanufacturing using grey-DEMATEL approach: An Indian perspective. Resources, Conservation & Recycling, 136, 79-87. https://doi.org/10.1016/j.resconrec.2018.03.021
Guo, Y., Zhang, S. H., Yang, J. Q., Yu, G. H., & Wang, Y. (2022). Dual memory scale network for multi-step time series forecasting in thermal environment of aquaculture facility: A case study of recirculating aquaculture water temperature. Expert Systems with Applications, 208, 118218. https://doi.org/10.1016/j.eswa.2022.118218
Torağay, O., & Murat, A. (2015). Performance evaluation of faculty departments by a Delphi method based on 2-Tuple fuzzy Linguistic representation model and TOPSIS. International Journal of Basic & Applied Science, 15(5), 11.
Mohanta, K. K., & Toragay, O. (2023). Enhanced performance evaluation through neutrosophic data envelopment analysis leveraging pentagonal neutrosophic numbers. Journal of Operational and Strategic Analytics, 1(2), 70-80. https://doi.org/10.56578/josa010204
Ana, L. M. D. M., & Rodrigo, F. F. (2023). Optimizing business value via IT governance mechanisms: An examination of SMEs in Southern Minas Gerais, Brazil. Journal of Operational and Strategic Analytics, 1(3), 106-114. https://doi.org/10.56578/josa010301
Jasvinder, S., Chandan, D. S., & Dharmpal, D. (2023). Digital Manufacturing. Wiley Online Library. https://doi.org/10.1002/9781119865216.ch7
Ghasempoor, A. M., Vladislav, D. S., Karbasian, M., Osintsev, N., & Nozick, V. (2021). Evaluation and selection of supplier in supply chain with fuzzy analytical network process approach. Journal of Fuzzy Extension and Application, 2 (1), 69-88. https://doi.org/10.22105/JFEA.2021.274734.1078
Balaei, S., Mohammadi, N., & Doroudi, H. (2022). Designing a hybrid model for the green supply chain in Guilan steel industry. International Journal of Research in Industrial Engineering, 12(1), 73-87. https://doi.org/10.22105/riej.2022.298634.1327
Tuncah, Y. T., & Akkartal, G. R. (2022). How warehouse location decisions changed in medical sector after pandemic? A fuzzy comparative study. Journal of Fuzzy Extension and Applications, 3(1), 81-95. https://doi.org/10.22105/jfea.2022.315262.1172
Nagarajan, D., Kanchana, A., Kavikumar, J., Nasreen, K., Seyyed, A. E., & Mohd, A. S. (2023). A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making. Scientifc Reports, 13, 10455. https://doi.org/10.1038/s41598-023-37497-z
Amir, B., Seyyed, E. N., Farhad, H. L., Mohammad, F., & Seyyed, A. E. (2023). Presenting a productivity analysis model for Iran oil industries using Malmquist network analysis. Decision Making: Applications in Management and Engineering, 6(2), 251-292. https://doi.org/10.31181/dmame622023705
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