研究生: |
謝孟涵 Hsieh, Meng-Han |
---|---|
論文名稱: |
以多準則決策架構推衍數位孿生技術之影響關係與導入策略 A MCDM Framework Based Derivations of Causal Relations Among Technologies of Digital twin and Introduction Strategies |
指導教授: |
黃啟祐
Huang, Chi-Yo |
口試委員: |
黃啟祐
Huang, Chi-Yo 羅乃維 Lo, Nai-Wei 黃日鉦 Huang, Jih-Jeng |
口試日期: | 2024/07/08 |
學位類別: |
碩士 Master |
系所名稱: |
工業教育學系科技應用管理碩士在職專班 Department of Industrial Education_Continuing Education Master's Program of Technological Management |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 英文 |
論文頁數: | 128 |
中文關鍵詞: | 數位轉型 、五維模型 、數位孿生 、決策試驗實驗室評估法 、基於決策實驗室評估法之網路流程 、多準則折衷評估方法 、技術路徑圖 |
英文關鍵詞: | Digital Transformation Strategy, Five-Dimension Model, Digital Twins, Decision Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based Analytic Network Process (DANP), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Technology Roadmap |
研究方法: | 決策試驗實驗室評估法 、 基於決策實驗室評估法之網路流程 、 多準則折衷評估方法 |
DOI URL: | http://doi.org/10.6345/NTNU202401485 |
論文種類: | 學術論文 |
相關次數: | 點閱:281 下載:0 |
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隨著大數據分析、人工智慧等新興技術的進步,數位孿生技術日益成熟,可降低複雜供應鏈斷鏈之風險。然而目前數位孿生之研究中,大部分僅提到數位孿生在製造工廠的應用,鮮少探討於供應鏈上導入數位孿生時,可優先導入的技術與採取的策略。
為解決此問題,本研究導入數位孿生五維模型,採取混合多準則決策分析法 (Multiple Criteria Decision Making, MCDM),使用決策試驗實驗室評估法 (Decision Making Trial and Evaluation Laboratory, DEMATEL) 進行在上下游工廠實施數位轉型策略的因果關係分析,確定彼此的影響程度,選擇影響力最大的數位轉型策略。並且,利用基於決策實驗室評估法之網路流程 (DEMATEL-based Analytic Network Process, DANP) 對分析結果中的策略進行加權評估,以考慮其重要度指標,也使用多準則折衷評估方法 (VlseKriterijumska Optimizacija I Kompromisno Resenje, VIKOR) 得出導入數位孿生所需的折衷排名技術,最後搭配路徑圖,提供供應鏈公司導入數位孿生相關技術時的參考,能有效利用數位孿生的關鍵技術與其相應策略,減少不必要的投資成本。
本研究為了能更理解公司目前在供應鏈上使用數位孿生時採用的技術與策略,邀請於供應鏈管理中導入數位孿生的產學專家填寫問卷。依據實證研究的結果,評估五維模型所需技術的優先順序,並規劃供應鏈導入數位孿生時所需的策略。本研究結果為希望導入數位孿生的供應鏈公司推衍出關鍵技術與最佳化策略,以提高生產效率和產品效能。
Owing to the prosperous development of advanced technologies such as big data analysis and Artificial Intelligence (AI), digital twins technologies have gradually become mature, which diminishes the risks of disruption in complex supply chain. However, among most of the current thesis about digital twins merely mention the use cases of digital twins in manufacturing factories. Less research discusses the imported technologies and adopted strategies that should be given priority under the structure of digital twins in supply chains.
For the purpose of overcoming this problem, this research integrates five-dimension digital twins and has adopted Multiple Criteria Decision Making (MCDM). First of all, Decision Making Trial and Evaluation Laboratory (DEMATEL) is integrated to analyze cause and effect of the digital transformation strategies that upstream and downstream businesses tend to adopt to make sure which strategies influence others the most. In this way, the digital transformation strategy in an maximal influence degree will be opted for. Furthermore, DEMATEL-based Analytic Network Process (DANP) conducts the weighted assessment for the analysis results to consider the relative significance. Also, VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is applied to obtain the compromise grading of the essential technologies to perform Digital twin in supply chains. Consequently, firms in supply chains can take technology roadmaps for reference when introducing digital twins, which helps managers implement the most crucial technologies and the relevant strategies of digital twins.
To realize more about the technologies and strategies that enterprises employ currently for supply chains, this study invites the digital twin experts from academia and industry to fill out the questionnaire. According to the empirical research results, digital twin models can be used to analyze the precedence of required technologies in five-dimension digital twins and envision the supply chain strategies. In terms of firms that expect to introduce digital twins into supply chains, the study’s findings can help deduce key technologies and optimization strategies to enhance manufacture efficiency and product performances.
Abdullah, F. M., Al-Ahmari, A. M., & Anwar, S. (2023). An integrated fuzzy DEMATEL and fuzzy TOPSIS method for analyzing smart manufacturing technologies. Processes, 11(3), 906.
Agbossou, I. (2024). Multi-Scalar Urban Digital twin Design: Architecture and OpenUSD Standards Based Methodology.
Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., & Edinbarough, I. (2022). State of Industry 5.0—Analysis and identification of current research trends. Applied System Innovation, 5(1), 16-30.
Agrawal, A., Fischer, M., & Singh, V. (2022). Digital twin: From concept to practice. Journal of Management in Engineering, 38(3), 06022001.
Alam, K. M., & El Saddik, A. (2017). C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access, 5, 2050-2062.
Albukhitan, S. J. P. c. s. (2020). Developing digital transformation strategy for manufacturing. 170, 664-671.
Anderl, R., & Trippner, D. (2000). STEP: Standard for the exchange of product model data. Berlin, Germany: Springer Vieweg Verlag.
Asad, U., Khan, M., Khalid, A., & Lughmani, W. A. (2023). Human-centric digital twins in industry: A comprehensive review of enabling technologies and implementation strategies. Sensors, 23(8), 3938.
Baldwin, R., & Di Mauro, B. W. (2020). Economics in the time of COVID-19: A new eBook. VOX CEPR Policy Portal, 2(3).
Balla, M., Haffner, O., Kučera, E., & Cigánek, J. (2023). Educational case studies: Creating a digital twin of the production line in TIA portal, unity, and Game4Automation framework. Sensors, 23(10), 4977.
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 7, 167653-167671.
Bartelt, M., & Kuhlenkötter, B. (2016). conexing Abschlussbericht. Werkzeug zur interdisziplinären Planung und produktbezogenen virtuellen Optimierung von automatisierten Produktionssystemen. Bochumer Universitätsverlag Westdeutscher Universitätsverlag (Maschinenbau, 10).
Barthelmey, A., Lee, E., Hana, R., & Deuse, J. (2019). Dynamic digital twin for predictive maintenance in flexible production systems. Paper presented at the IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal.
Barthelmey, A., Lenkenhof, K., Schallow, J., Lemmerz, K., Deuse, J., & Kuhlenkötter, B. (2016). Technical documentation as a service–An approach for integrating editorial and engineering processes of machinery and plant engineers. Procedia CIRP, 52, 167-172.
Baruffaldi, G., Accorsi, R., & Manzini, R. (2019). Warehouse management system customization and information availability in 3pl firms: A decision-support tool. Industrial management & data systems, 119(2), 251-273.
Barykin, S. Y., Bochkarev, A. A., Kalinina, O. V., & Yadykin, V. K. (2020). Concept for a Supply Chain Digital twin. International Journal of Mathematical, Engineering and Management Sciences, 5(6), 1498-1515.
Bazaz, S. M., Lohtander, M., & Varis, J. (2019). 5-dimensional definition for a manufacturing digital twin. Procedia Manufacturing, 38, 1705-1712.
Bécue, A., Maia, E., Feeken, L., Borchers, P., & Praça, I. (2020). A new concept of digital twin supporting optimization and resilience of factories of the future. Applied Sciences, 10(13), 4482.
Berghaus, S., & Back, A. (2017). Disentangling the fuzzy front end of digital transformation: Activities and approaches.
Berisha-Gawlowski, A., Caruso, C., & Harteis, C. (2021). The concept of a digital twin and its potential for learning organizations. Digital transformation of learning organizations, 95-114.
Bitton, R., Gluck, T., Stan, O., Inokuchi, M., Ohta, Y., Yamada, Y., . . . Shabtai, A. (2018). Deriving a cost-effective digital twin of an ICS to facilitate security evaluation. Paper presented at the Computer Security: 23rd European Symposium on Research in Computer Security, ESORICS 2018, Barcelona, Spain, September 3-7, 2018, Proceedings, Part I 23.
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic Construction Digital twin: Directions for future research. Automation in Construction, 114, 103179.
Brandenbourger, B., & Durand, F. (2018). Design pattern for decomposition or aggregation of automation systems into hierarchy levels. Paper presented at the 2018 IEEE 23rd international conference on emerging technologies and factory automation (ETFA), Torino, Italy.
Cathey, G., Benson, J., Gupta, M., & Sandhu, R. (2021). Edge centric secure data sharing with digital twins in smart ecosystems. Paper presented at the 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), Online, USA.
Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86-97.
Centomo, S., Dall’Ora, N., & Fummi, F. (2020). The design of a digital-twin for predictive maintenance. Paper presented at the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria.
Chakraborty, S., & Adhikari, S. (2021). Machine learning based digital twin for dynamical systems with multiple time-scales. Computers & Structures, 243, 106410.
Chakraborty, S., Nijssen, E. J., Valkenburg, R. J. T. F., & Change, S. (2022). A systematic review of industry-level applications of technology roadmapping: Evaluation and design propositions for roadmapping practitioners. 179, 121141.
Chanias, S., Myers, M. D., & Hess, T. J. T. J. o. S. I. S. (2019). Digital transformation strategy making in pre-digital organizations: The case of a financial services provider. 28(1), 17-33.
Chen, C. C. (2022). Multiple Criteria Decision Making Methods Based Scenario Analysis for Defining Digital Transformation Strategies of Small and Medium Sized Machinery Manufacturers. National Taiwan Normal University. Taipei.
Chen, Ken. (2022). Digital twin: the key technology to create the metaverse through the integration of virtuality and reality. New Taipei: DrMaster Press.
Chen, Z., & Huang, L. (2021). Digital twins for information-sharing in remanufacturing supply chain: A review. Energy, 220, 119712.
Choi, J., Moon, S., & Min, S. (2023). Digital twin simulation modeling process with system dynamics: An application to naval ship operation. International Journal of Robust and Nonlinear Control, 33(16), 10136-10150.
Chopra, S., & Meindl, P. (2001). Strategy, planning, and operation. Supply Chain Management, 15(5), 71-85.
Cimino, C., Negri, E., & Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113, 103130.
Cobra, R., Sanvezzo, P., Branciforti, M., & Mascarenhas, J. J. S. (2021). Circular technology roadmapping (TRM): Fostering sustainable material development. 13(13), 7036.
Crespi, N., Drobot, A. T., & Minerva, R. (2023). The Digital twin: What and Why? In The Digital twin (pp. 3-20): Springer.
Dassault Systèmes (2024). Digital twins help improve smart manufacturing capabilities. Speech presented at the TrendForce Smart Manufacturing 2024, Taipei, Taiwan.
D'Almeida, A. L., Bergiante, N. C. R., de Souza Ferreira, G., Leta, F. R., de Campos Lima, C. B., & Lima, G. B. A. J. T. I. J. o. A. M. T. (2022). Digital transformation: a review on artificial intelligence techniques in drilling and production applications. 119(9), 5553-5582.
De Souza, R., William, L., & Timperio, G. (2017). Supply chain digital transformation: insights and tools–anylogistix supply chain optimization software.https://www.anylogistix.com/resources/whitepapers/supplychain-digital-transformation-insights-and-tools/.
Deepu, T., & Ravi, V. (2021). Exploring critical success factors influencing adoption of digital twin and physical internet in electronics industry using grey-DEMATEL approach. Digital Business, 1(2), 100009.
del Rio-Chanona, R. M., Mealy, P., Pichler, A., Lafond, F., & Farmer, J. D. (2020). Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective. Oxford Review of Economic Policy, 36(Supplement_1), S94-S137.
Dietz, M., & Pernul, G. (2019). Digital twin: Empowering Enterprises Towards a System-of-Systems Approach. Business & Information Systems Engineering, 62(2), 179-184.
Drumond, P., de Araújo Costa, I. P., Lellis Moreira, M. Â., dos Santos, M., Simões Gomes, C. F., & do Nascimento Maêda, S. M. (2022). Strategy study to prioritize marketing criteria: an approach in the light of the DEMATEL method. Procedia Computer Science, 199, 448-455.
Duckstein, L., & Opricovic, S. J. W. r. r. (1980). Multiobjective optimization in river basin development. 16(1), 14-20.
Ehm, H., Ramzy, N., Moder, P., Summerer, C., Fetz, S., & Neau, C. (2019). Digital reference–a semantic web for semiconductor manufacturing and supply chains containing semiconductors. Paper presented at the 2019 Winter Simulation Conference (WSC), National Harbor, USA.
Erikstad, S. O. (2017). Merging physics, big data analytics and simulation for the next-generation digital twins. High-performance marine vehicles, 141-151.
Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, Geneva, Switzerland, 1(8), 12-14.
Gai, K., Zhang, Y., Qiu, M., & Thuraisingham, B. (2022). Blockchain-enabled service optimizations in supply chain digital twin. IEEE Transactions on Services Computing.
Galvin, R. (1998). Science roadmaps. In (Vol. 280, pp. 803-803): American Association for the Advancement of Science.
Gawer, A. J. L. R. P. (2021). Digital platforms’ boundaries: The interplay of firm scope, platform sides, and digital interfaces. 54(5), 102045.
Geng, R., Li, M., Hu, Z., Han, Z., & Zheng, R. (2022). Digital twin in smart manufacturing: remote control and virtual machining using VR and AR technologies. Structural and Multidisciplinary Optimization, 65(11), 321.
Gigabyte. (2023, March). Enjoy extreme speed driving. Server technology enables fast-moving car design. Gigabyte Newsroom. Retrieved from https://www.gigabyte.com/tw/Article/car-design-fluid-mechanics
Giri, B. C., Molla, M. U., & Biswas, P. (2022). Pythagorean fuzzy DEMATEL method for supplier selection in sustainable supply chain management. Expert Systems with Applications, 193, 116396.
Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and US Air Force vehicles. Paper presented at the 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, Hawaii, USA.
Glatt, M., Sinnwell, C., Yi, L., Donohoe, S., Ravani, B., & Aurich, J. C. (2021a). Modeling and implementation of a digital twin of material flows based on physics simulation. Journal of Manufacturing Systems, 58, 231-245.
Gong, C., & Ribiere, V. J. T. (2021). Developing a unified definition of digital transformation. 102, 102217.
Gorodetsky, V., Larukchin, V., & Skobelev, P. (2020). Conceptual model of digital platform for enterprises of industry 5.0. Paper presented at the Intelligent Distributed Computing XIII.
Grieves, M. (2014). Digital twin: manufacturing excellence through virtual factory replication, 1-7.
Groenveld, P. (1997). Roadmapping integrates business and technology. Research-Technology Management, 40(5), 48-55.
Hai, T. N., Van, Q. N., & Thi Tuyet, M. N. J. E. S. J. (2021). Digital transformation: Opportunities and challenges for leaders in the emerging countries in response to COVID-19 pandemic. 5(1), 21-36.
Hampson, K. D., & Tatum, C. (1993). Technology strategy for construction automation. Automation and robotics in construction X, 125-133.
Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159-1197.
Harring, R. (1984). Motorola's use of the product technology roadmap. Paper presented at The National Communication Forum, Michigan, USA.
Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2).
Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems, 42, 93-103.
Holopainen, M., Saunila, M., Rantala, T., & Ukko, J. (2022). Digital twins’ implications for innovation. Technology Analysis & Strategic Management, 36(8), 1779–1791.
Howard, D. (2019). The digital twin: Virtual validation in electronics development and design. Paper presented at the 2019 Pan Pacific Microelectronics Symposium (Pan Pacific), Hawaii, USA.
Hu, Z., & Mahadevan, S. J. J. o. M. D. (2018). Adaptive surrogate modeling for time-dependent multidisciplinary reliability analysis. 140(2), 021401.
Huang, C.-Y., Chen, H., Tzeng, G.-H., & Hu, K.-H. (2010). Enhancing the performance of a SOC design service firm by using a novel DANP based MCDM framework on the balanced scorecard. Paper presented at the 40th International Conference on Computers & Indutrial Engineering, Awaji Island, Japan.
Huang, C. Y., Hsieh, H. L., & Chen, H. (2020). Evaluating the Investment Projects of Spinal Medical Device Firms Using the Real Option and DANP-mV Based MCDM Methods. International Journal of Environmental Research and Public Health, 17(9), 3335.
Huang, C.-Y., Huang, J.-J., Chang, Y.-N., & Lin, Y.-C. (2021). A fuzzy-mop-based competence set expansion method for technology roadmap definitions. Mathematics, 9(2), 135.
Ivanov, D. (2024). Conceptualisation of a 7-element digital twin framework in supply chain and operations management. International Journal of Production Research, 62(6), 2220-2232.
Jacobs, F. R., & Chase, R. B. (2023). Operations and supply chain management(17th ed.), Supply Chain Processes (pp. 349-424). New York, NY: McGraw-Hill.
Jiang, H., Qin, S., Fu, J., Zhang, J., & Ding, G. (2021). How to model and implement connections between physical and virtual models for digital twin application. Journal of Manufacturing Systems, 58, 36-51.
Jiang, Y., Yin, S., Li, K., Luo, H., & Kaynak, O. J. P. T. o. t. R. S. A. (2021). Industrial applications of digital twins. 379(2207), 20200360.
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36-52.
Jovanovic, M., Sjödin, D., & Parida, V. J. T. (2022). Co-evolution of platform architecture, platform services, and platform governance: Expanding the platform value of industrial digital platforms. 118, 102218.
Kaarlela, T., Pieskä, S., & Pitkäaho, T. (2020). Digital twin and virtual reality for safety training. Paper presented at the 2020 11th IEEE international conference on cognitive infocommunications (CogInfoCom), Mariehamn, Finland.
Kamble, S. S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., & Sharma, R. (2022). Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework. Technological Forecasting and Social Change, 176, 121448.
Karabag, S. F. J. J. o. A. E., & Research, B. (2020). An unprecedented global crisis! The global, regional, national, political, economic and commercial impact of the coronavirus pandemic. 10(1), 1-6.
Kaur, M. J., Mishra, V. P., & Maheshwari, P. (2020). The convergence of digital twin, IoT, and machine learning: transforming data into action. Digital twin technologies and smart cities. Berlin, Germany: Springer Cham.
Kerr, C., Phaal, R. J. T. F., & Change, S. (2020). Technology roadmapping: Industrial roots, forgotten history and unknown origins. 155, 119967.
Knebel, F. P., Trevisan, R., do Nascimento, G. S., Abel, M., & Wickboldt, J. A. (2023). A study on cloud and edge computing for the implementation of digital twins in the Oil & Gas industries. Computers & Industrial Engineering, 182, 109363.
Khuntia, J., Saldanha, T., Kathuria, A., & Tanniru, M. R. J. E. J. o. I. S. (2024). Digital service flexibility: a conceptual framework and roadmap for digital business transformation. 33(1), 61-79.
Kong, T., Hu, T., Zhou, T., & Ye, Y. (2021). Data construction method for the applications of workshop digital twin system. Journal of Manufacturing Systems, 58, 323-328.
Konopik, J., Jahn, C., Schuster, T., Hoßbach, N., & Pflaum, A. J. D. B. (2022). Mastering the digital transformation through organizational capabilities: A conceptual framework. 2(2), 100019.
Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., & Weinmann, A. J. I. j. o. i. m. (2022). Digital transformation in business and management research: An overview of the current status quo. 63, 102466.
Kraus, S., Schiavone, F., Pluzhnikova, A., & Invernizzi, A. C. J. J. o. B. R. (2021). Digital transformation in healthcare: Analyzing the current state-of-research. 123, 557-567.
Kritzler, M., Funk, M., Michahelles, F., & Rohde, W. (2017). The virtual twin: Controlling smart factories using a spatially-correct augmented reality representation. Paper presented at the Proceedings of the seventh international conference on the internet of things, Linz, Austria.
Lai, C., Xu, L., & Shang, J. (2020). Optimal planning of technology roadmap under uncertainty. Journal of the Operational Research Society, 71(4), 673-686.
Lam, W. S., Lam, W. H., & Lee, P. F. (2023). A Bibliometric analysis of digital twin in the supply chain. Mathematics, 11(15), 3350.
Lambert, D., Stock, J. R., & Ellram, L. M. (1998). Fundamentals of logistics management. New York, NY: McGraw-Hill.
Latif, H., & Starly, B. (2020). A simulation algorithm of a digital twin for manual assembly process. Procedia Manufacturing, 48, 932-939.
Lee, J., Azamfar, M., & Bagheri, B. (2021). A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems. Manufacturing Letters, 27, 87-91.
Lee, J. H., Phaal, R., & Lee, S.-H. (2013). An integrated service-device-technology roadmap for smart city development. Technological Forecasting and Social Change, 80(2), 286-306.
Lee, Y. H., & Lee, S. (2022). Deep reinforcement learning based scheduling within production plan in semiconductor fabrication. Expert Systems with Applications, 191, 116222.
Lesser, R., & Reeves, M. J. B. (2020). Leading out of adversity. The Boston Consulting Group. Retrieved from https://www.bcg.com/publications/2020/business-resilience-lessons-covid-19
Li, W., Rentemeister, M., Badeda, J., Jöst, D., Schulte, D., & Sauer, D. U. (2020). Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. Journal of energy storage, 30, 101557.
Liao, S., Wu, M.-J., Huang, C.-Y., Kao, Y.-S., & Lee, T.-H. (2014). Evaluating and Enhancing Three-Dimensional Printing Service Providers for Rapid Prototyping Using the DEMATEL Based Network Process and VIKOR. Mathematical Problems in Engineering, 2014, 1-16.
Lim, K. Y. H., Zheng, P., & Chen, C.-H. (2020). A state-of-the-art survey of Digital twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31, 1313-1337.
Liu, M., Fang, S., Dong, H., & Xu, C. (2021). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346-361.
Liu, S., Lu, S., Li, J., Sun, X., Lu, Y., & Bao, J. (2021). Machining process-oriented monitoring method based on digital twin via augmented reality. The International Journal of Advanced Manufacturing Technology, 113, 3491-3508.
Liu, Y., Zhang, Q., Ouyang, Z., & Huang, H.-Z. (2021). Integrated production planning and preventive maintenance scheduling for synchronized parallel machines. Reliability Engineering & System Safety, 215, 107869.
Liu Y., Zhao X., Zhu M., Liu M., Tian H. C., Liu D. F., Sun S. M. (2023). Digital twin: The way to transform in the era of digital and real integration. Beijing, China: Posts and Telecome Press.
Love, P. E., & Matthews, J. (2019). The ‘how’of benefits management for digital technology: From engineering to asset management. Automation in Construction, 107, 102930.
Love, P. E., Matthews, J., & Zhou, J. (2020). Is it just too good to be true? Unearthing the benefits of disruptive technology. International Journal of Information Management, 52, 102096.
Lutters, E. (2018). Pilot production environments driven by digital twins. South African journal of industrial engineering, 29(3), 40-53.
Manita, R., Elommal, N., Baudier, P., Hikkerova, L. J. T. F., & Change, S. (2020). The digital transformation of external audit and its impact on corporate governance. 150, 119751.
Martinez Hernandez, V., Neely, A., Ouyang, A., Burstall, C., & Bisessar, D. (2019). Service business model innovation: the digital twin technology. Paper presented at the 26th EurOMA Conference, Helsinki, Finland.
McIlwraith, A. (2021). Information security and employee behaviour: how to reduce risk through employee education, training and awareness: Routledge.
Meier, N., Müller-Polyzou, R., Brach, L., & Georgiadis, A. (2021). Digital twin support for laser-based assembly assistance. Procedia CIRP, 99, 460-465.
Melesse, T. Y., Bollo, M., Di Pasquale, V., & Riemma, S. (2022). Digital twin for Inventory Planning of Fresh Produce. IFAC-PapersOnLine, 55(10), 2743-2748.
Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., . . . Tutorials. (2022). Digital twins: A survey on enabling technologies, challenges, trends and future prospects. 24(4), 2255-2291.
Mishra, D. B., Haider, I., Gunasekaran, A., Sakib, M. N., Malik, N., & Rana, N. P. J. I. J. o. P. E. (2023). “Better together”: Right blend of business strategy and digital transformation strategies. 266, 109040.
Moshood, T., Nawanir, G., Sorooshian, S., & Okfalisa, O. (2021). Digital twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics. Applied System Innovation, 4(2), 29.
Munodawafa, R. T., & Johl, S. K. J. I. A. (2022). Design and development of an eco-innovation management information system to accelerate firms’ digital transformation strategy. 10, 37796-37810.
Nadkarni, S., & Prügl, R. (2021). Digital transformation: a review, synthesis and opportunities for future research. Management Review Quarterly, 71, 233-341.
Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939-948.
Newrzella, S. R., Franklin, D. W., & Haider, S. (2021). 5-dimension cross-industry digital twin applications model and analysis of digital twin classification terms and models. IEEE Access, 9, 131306-131321.
Niaki, S. V. D., & Shafaghat, A. (2021). A review of the concept of supply chain digital twin in the era of industry 4.0. ournal of Applied Intelligent Systems and Information Sciences, 2(2), 47-57.
NVIDIA. (2022). Siemens and NVIDIA to Enable Industrial Metaverse. NVIDIA Newsroom. Retrieved from https://resources.nvidia.com/en-us-omniverse-industrial-digital-twins/siemens-and-nvidia-technology?lx=deNrXD&ncid=no-ncid
NVIDIA (2022). The Next Evolution of Industrial Automation with Siemens Xcelerator and NVIDIA Omniverse. NVIDIA. Retrieved from https://resources.nvidia.com/en-us-omniverse-industrial-digital-twins/next-evolution-of-Industrial-automation?lx=deNrXD
Pan, S. L., & Nishant, R. (2023). Artificial intelligence for digital sustainability: An insight into domain-specific research and future directions. International Journal of Information Management, 72, 102668.
Perno, M., Hvam, L., & Haug, A. (2022). Implementation of digital twins in the process industry: A systematic literature review of enablers and barriers. Computers in Industry, 134, 103558.
Pittaway, J. J., & Montazemi, A. R. J. G. i. q. (2020). Know-how to lead digital transformation: The case of local governments. 37(4), 101474.
Porsche. (2024, June). Glimpse into the future: the digital twin of a high-voltage battery. Porsche Newsroom. Retrieved from https://newsroom.porsche.com/fr_CH/2024/innovation/porsche-engineering-digital-twin-high-voltage-battery-36556.html
Qader, G., Junaid, M., Abbas, Q., & Mubarik, M. S. (2022). Industry 4.0 enables supply chain resilience and supply chain performance. Technological Forecasting and Social Change, 185, 122026.
Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585-3593.
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., . . . Nee, A. (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58, 3-21.
Rachmawati, S. M., Putra, M. A. P., Lee, J. M., & Kim, D. S. J. E. A. o. A. I. (2023). Digital twin-enabled 3D printer fault detection for smart additive manufacturing. Engineering Applications of Artificial Intelligence (Vol. 124). Paris, France: The International Federation of Automatic Control.
Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021). The role of ai, machine learning, and big data in digital twinning: A systematic literature review, challenges, and opportunities. IEEE Access, 9, 32030-32052.
Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 4(4), 529-763.
Rodríguez-Abitia, G., & Bribiesca-Correa, G. J. F. I. (2021). Assessing digital transformation in universities. 13(2), 52.
Rosen, K. M., & Pattipati, K. R. (2023). Operating Digital twins Within an Enterprise Process. The Digital twin. Berlin, Germany: Springer Cham.
Saarikko, T., Westergren, U. H., & Blomquist, T. J. B. h. (2020). Digital transformation: Five recommendations for the digitally conscious firm. 63(6), 825-839.
Sandberg, J., Holmström, J., & Lyytinen, K. J. M. I. S. Q. (2020). Digitization and phase transitions in platform organizing logics: Evidence from the process automation industry. 44(1), 129-153.
Schilirò, D. (2021). Digital transformation, COVID-19, and the future of work. International Journal of Business Management and Economic Research, 12(3), 1945-1952.
Scott, S. J. T. E. B. U. (2020). The coronavirus and public service media: why digital transformation matters now more than ever. The European Broadcasting Union. Retrieved from ttps://www.ebu.ch/news/2020/03/the-coronavirus-and-public-service-media-why-digital-transformation-matters-nowmore-than-ever
Simon, C., & Haag, S. (2019). Simulation of Horizontal and Vertical Integration in Digital twins. Anwendungen und Konzepte der Wirtschaftsinformatik(10), 6-6.
Singh, A., Klarner, P., & Hess, T. J. L. R. P. (2020). How do chief digital officers pursue digital transformation activities? The role of organization design parameters. 53(3), 101890.
Singh, M., Rajan, M., Shivraj, V., & Balamuralidhar, P. (2015). Secure mqtt for internet of things (iot). Paper presented at the 2015 fifth international conference on communication systems and network technologies, Gwalior, India.
Smith, K. A., & Gupta, J. N. (2000). Neural networks in business: techniques and applications for the operations researcher. Computers & Operations Research, 27(11-12), 1023-1044.
Son, W., Lee, S. J. T. A., & Management, S. (2019). Integrating fuzzy-set theory into technology roadmap development to support decision-making. Technology Analysis & Strategic Management, 31(4), 447-461.
Srai, J., Settanni, E., Tsolakis, N., & Parminder, K. Supply Chain Digital twins: Opportunities and Challenges Beyond the Hype. 2019. In: 23rd Cambridge International Manufacturing Symposium. Centre for International Manufacturing, Institute for Manufacturing, Department of Engineering. University of Cambridge, Cambridge.
Standardization, I. O. f. (2012). ISO/PAS 17506: 2012, Industrial Automation Systems and Integration—COLLADA Digital Asset Schema Specification for 3D Visualization of Industrial Data. International Organization for Standardization Geneva, Switzerland.
Stergiou, C. L., & Psannis, K. E. (2022). Digital twin intelligent system for industrial IoT-based big data management and analysis in cloud. Virtual Reality & Intelligent Hardware, 4(4), 279-291.
Tadapaneni, N. R. (2020). Cloud computing security challenges. International journal of Innovations in Engineering research and Technology, 6(7).
Tao, F., Liu, W., Zhang, M., Hu, T.-l., Qi, Q., Zhang, H., . . . Huang, Z. (2019). Five-dimension digital twin model and its ten applications. Computer Integrated Manufacturing Systems, 25(1), 1-18.
Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022). Digital twin modeling. Journal of Manufacturing Systems, 64, 372-389.
Tavoletti, E., Kazemargi, N., Cerruti, C., Grieco, C., & Appolloni, A. J. E. J. o. I. M. (2022). Business model innovation and digital transformation in global management consulting firms. 25(6), 612-636.
Taylan, O., Alamoudi, R., Kabli, M., AlJifri, A., Ramzi, F., & Herrera-Viedma, E. (2020). Assessment of Energy Systems Using Extended Fuzzy AHP, Fuzzy VIKOR, and TOPSIS Approaches to Manage Non-Cooperative Opinions. Sustainability, 12(7), 2745.
Tomczyk, M., & van der Valk, H. (2022). Digital twin Paradigm Shift: The Journey of the Digital twin Definition. Paper presented at the 2022 24th International Conference on Enterprise Information Systems (ICEIS), Online, Czech Republic.
Trauer, J., Mac, D. P., Mörtl, M., & Zimmermann, M. (2023). A Digital twin Business Modelling Approach. Proceedings of the Design Society, 3, 121-130.
Tu, Y.-H., & Peng, C.-C. (2020). An ARMA-based digital twin for MEMS gyroscope drift dynamics modeling and real-time compensation. IEEE Sensors Journal, 21(3), 2712-2724.
Tzeng, G.-H., & Huang, C.-Y. (2011). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190.
Utitech. (2022, December). If an enterprise wants to introduce digital twins, how can it resolve its two major concerns about substantial benefits and costs? Tech Orange. Retrieved from https://buzzorange.com/techorange/2022/12/12/utitech-digital-twin/
VanDerHorn, E., & Mahadevan, S. (2021). Digital twin: Generalization, characterization and implementation. Decision Support Systems, 145, 113524.
Vial, G. J. M. d. t. (2021). Understanding digital transformation: A review and a research agenda. 13-66.
Wache, H., & Dinter, B. (2020). The digital twin–birth of an integrated system in the digital age. Paper presented at the 53rd Hawaii International Conference on System Sciences, Hawaii, USA.
Wagner, R., Schleich, B., Haefner, B., Kuhnle, A., Wartzack, S., & Lanza, G. (2019). Challenges and potentials of digital twins and industry 4.0 in product design and production for high performance products. Procedia CIRP, 84, 88-93.
Wang, G., Zhang, G., Guo, X., & Zhang, Y. (2021). Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing. Journal of Manufacturing Systems, 59, 165-179.
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144-156.
Wang, L., Deng, T., Shen, Z.-J. M., Hu, H., & Qi, Y. (2022). Digital twin-driven smart supply chain. Frontiers of Engineering Management, 9(1), 56-70.
Wang, X., Wang, Y., Tao, F., & Liu, A. (2021). New paradigm of data-driven smart customisation through digital twin. Journal of Manufacturing Systems, 58, 270-280.
Wang, Y., Wang, S., Yang, B., Zhu, L., & Liu, F. (2020). Big data driven Hierarchical Digital twin Predictive Remanufacturing paradigm: Architecture, control mechanism, application scenario and benefits. Journal of Cleaner Production, 248, 119299.
Wen, T., Dobson, E., & Hvaara, R. (2020). Mesh Learning: A Cloud and Edge–based Computing Network Providing Data–Driven Solutions to the Oil and Gas Industry. Paper presented at the Offshore Technology Conference Asia, Kuala Lumpur, Malaysia.
Wenner, M., Meyer-Westphal, M., Herbrand, M., & Ullerich, C. (2021). The concept of digital twin to revolutionise infrastructure maintenance: The pilot project smartBRIDGE Hamburg. Paper presented at the Proceedings of the 27th ITS world congress, Hamburg, Germany.
West, S., Stoll, O., Meierhofer, J., & Züst, S. (2021). Digital twin providing new opportunities for value co-creation through supporting decision-making. Applied Sciences, 11(9), 3750.
Wieland, A., & Durach, C. F. (2021). Two perspectives on supply chain resilience. Journal of Business Logistics, 42(3), 315-322.
Wright, L., & Davidson, S. (2020). How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7(1), 1-13.
Wu, C., Zhou, Y., Pereia Pessôa, M. V., Peng, Q., & Tan, R. (2021). Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model. Journal of Manufacturing Systems, 58, 79-93.
Xu, Y., Sun, Y., Liu, X., & Zheng, Y. (2019). A digital-twin-assisted fault diagnosis using deep transfer learning. IEEE Access, 7, 19990-19999.
Yang, Y.-P. O., Shieh, H.-M., Leu, J.-D., & Tzeng, G.-H. (2008). A novel hybrid MCDM model combined with DEMATEL and ANP with applications. International Journal of Operations Research, 5(3), 160-168.
Yildiz, E., Møller, C., & Bilberg, A. (2020). Virtual factory: digital twin based integrated factory simulations. Procedia CIRP, 93, 216-221.
Zarnitz, S., & Straube, F. (2023). Digital twins in Logistics: Requirements, Application and Potentials. In Digitalisierung im Einkauf (pp. 189-201): Springer.
Zhang, J., Brintrup, A., Calinescu, A., Kosasih, E., & Sharma, A. (2021). Supply chain digital twin framework design: an approach of supply chain operations reference model and system of systems. arXiv preprint arXiv:2107.09485.
Zhang, J., & Chen, Z. J. J. o. t. K. E. (2024). Exploring human resource management digital transformation in the digital age. 15(1), 1482-1498.
Zhang, J., Deng, T., Jiang, H., Chen, H., Qin, S., & Ding, G. (2021). Bi-level dynamic scheduling architecture based on service unit digital twin agents. Journal of Manufacturing Systems, 60, 59-79.
Zhang, Y., Ma, S., Yang, H., Lv, J., & Liu, Y. (2018). A big data driven analytical framework for energy-intensive manufacturing industries. Journal of Cleaner Production, 197, 57-72.
Zhang, Y., Zhang, Y., Gao, M., Dai, B., Kou, S., Wang, X., . . . Shen, W. (2023). Digital twin perception and modeling method for feeding behavior of dairy cows. Computers and Electronics in Agriculture, 214, 108181.
Zhaoyun, Z., & Linjun, L. (2022). Application status and prospects of digital twin technology in distribution grid. Energy Reports, 8, 14170-14182.
Zheng, Y., Yang, S., & Cheng, H. (2019). An application framework of digital twin and its case study. Journal of Ambient Intelligence and Humanized Computing, 10, 1141-1153.
Zhou, G., Zhang, C., Li, Z., Ding, K., & Wang, C. (2020). Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. International Journal of Production Research, 58(4), 1034-1051