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Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study

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  • Saporiti, Nicolò
  • Cannas, Violetta Giada
  • Pozzi, Rossella
  • Rossi, Tommaso

Abstract

Digital Twin (DT) implementation in manufacturing plants has attracted increasing attention. Owing to advancements in the use of technologies related to Industry 4.0 pillars, such as the Internet of Things, Big Data analytics, and simulation, the potential of DTs to profoundly impact manufacturing has been recognised. However, DT implementation is challenging. In practice, manufacturing companies that consider DT implementation may encounter several challenges, which can prevent the achievement of its potential benefits and impede its successful realization. Research on this topic lacks empirical evidence and models to guide practitioners to overcome this problem. Therefore, the aim of this study was to map the key challenges related to DT implementation in manufacturing contexts and propose a set of possible countermeasures. To achieve this objective, we conducted a Delphi study involving 15 experts, both practitioners and academics. The process required three rounds. In the first round, the experts were requested to provide a personalized list of potential challenges to DT implementation. In the second round, the experts evaluated the challenges from the literature and their suggested potential challenges, providing a measure of relevance. Furthermore, experts were asked to propose possible countermeasures to these challenges. Finally, a third round achieved consensus. The study identified 18 key challenges divided into four categories and proposed a set of possible countermeasures to overcome these problems. Moreover, a relevance/agreement matrix of the key challenges was proposed to establish a relative impact.

Suggested Citation

  • Saporiti, Nicolò & Cannas, Violetta Giada & Pozzi, Rossella & Rossi, Tommaso, 2023. "Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study," International Journal of Production Economics, Elsevier, vol. 261(C).
  • Handle: RePEc:eee:proeco:v:261:y:2023:i:c:s0925527323001202
    DOI: 10.1016/j.ijpe.2023.108888
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    References listed on IDEAS

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    1. Kembro, Joakim & Näslund, Dag & Olhager, Jan, 2017. "Information sharing across multiple supply chain tiers: A Delphi study on antecedents," International Journal of Production Economics, Elsevier, vol. 193(C), pages 77-86.
    2. Raj, Alok & Dwivedi, Gourav & Sharma, Ankit & Lopes de Sousa Jabbour, Ana Beatriz & Rajak, Sonu, 2020. "Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective," International Journal of Production Economics, Elsevier, vol. 224(C).
    3. Dohale, Vishwas & Gunasekaran, Angappa & Akarte, Milind & Verma, Priyanka, 2021. "An integrated Delphi-MCDM-Bayesian Network framework for production system selection," International Journal of Production Economics, Elsevier, vol. 242(C).
    4. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    5. Veepan Kumar & Prem Vrat & Ravi Shankar, 2021. "Prioritization of strategies to overcome the barriers in Industry 4.0: a hybrid MCDM approach," OPSEARCH, Springer;Operational Research Society of India, vol. 58(3), pages 711-750, September.
    6. Guanghui Zhou & Chao Zhang & Zhi Li & Kai Ding & Chuang Wang, 2020. "Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1034-1051, February.
    7. Min, Qingfei & Lu, Yangguang & Liu, Zhiyong & Su, Chao & Wang, Bo, 2019. "Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry," International Journal of Information Management, Elsevier, vol. 49(C), pages 502-519.
    8. Xi Vincent Wang & Lihui Wang, 2019. "Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3892-3902, June.
    9. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Stahre, Johan, 2017. "Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030," International Journal of Production Economics, Elsevier, vol. 191(C), pages 154-169.
    10. Guilherme Luz Tortorella & Diego Fettermann, 2018. "Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(8), pages 2975-2987, April.
    11. Ekström, Thomas & Hilletofth, Per & Skoglund, Per, 2021. "Towards a purchasing portfolio model for defence procurement – A Delphi study of Swedish defence authorities," International Journal of Production Economics, Elsevier, vol. 233(C).
    12. Alexandre Moeuf & Samir Lamouri & Robert Pellerin & Simon Tamayo-Giraldo & Estefania Tobon-Valencia & Romain Eburdy, 2020. "Identification of critical success factors, risks and opportunities of Industry 4.0 in SMEs," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1384-1400, March.
    13. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
    14. Culot, Giovanna & Orzes, Guido & Sartor, Marco & Nassimbeni, Guido, 2020. "The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    15. Li, Ming & Li, Zhi & Huang, Xidian & Qu, Ting, 2021. "Blockchain-based digital twin sharing platform for reconfigurable socialized manufacturing resource integration," International Journal of Production Economics, Elsevier, vol. 240(C).
    16. Fundin, Anders & Bergquist, Bjarne & Eriksson, Henrik & Gremyr, Ida, 2018. "Challenges and propositions for research in quality management," International Journal of Production Economics, Elsevier, vol. 199(C), pages 125-137.
    17. Leung, Eric K.H. & Lee, Carmen Kar Hang & Ouyang, Zhiyuan, 2022. "From traditional warehouses to Physical Internet hubs: A digital twin-based inbound synchronization framework for PI-order management," International Journal of Production Economics, Elsevier, vol. 244(C).
    18. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
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