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Üretim Sistemlerinde Dijital İkiz Kullanımına Yönelik Bariyer Analizi

Year 2025, Volume: 9 Issue: 1, 247 - 261
https://doi.org/10.29216/ueip.1614573

Abstract

Dijital ikiz, gerçek bir nesne, kaynak ya da sürecin sanal ortamda dijital benzerinin oluşturulduğu ve veri bağlantısı yoluyla anlık ve dinamik olarak fiziksel dünya ile sanal dünya etkileşiminin canlı tutulduğu bir platformdur. Yeni gelişen bir sistem olduğundan dijital ikizin üretim sistemlerince uygulanması sırasında ortaya çıkan çeşitli bariyerler, bu platformun benimsenmesini zorlaştırabilmektedir. Dijital ikizin üretim projelerinde başarılı olması için bariyerleri ortadan kaldıracak stratejilere ihtiyaç duyulmaktadır. Bu çalışmada üretim sistemlerinde dijital ikiz kullanımı sırasında ortaya çıkabilecek bariyerler belirlenmiş ve uzman görüşleri doğrultusunda bariyerler birbirleri ile karşılaştırılmıştır. Çalışmanın amacı bariyerler arasındaki göreceli ağırlıkları tespit ederek önem önceliğini ortaya koymaktır. Analiz tekniği olarak uzman görüşlerinin dilsel değişkenler üzerinden modellenmesine imkân sağlayan bulanık Best Worst metodu (BWM) kullanılmıştır. Sonuç olarak “sistemsel ve teknolojik entegrasyon yetersizlikleri" bariyerinin üretimde dijital ikiz uygulamaları için öncelikli olduğu tespit edilmiştir. Çalışma, dijital ikiz ve üretim birlikteliğini inceleyerek birlikteliğe engel olabilecek unsurları ortaya koymakta ve uygulayıcılar için genel bir çerçeve sunmaktadır.

References

  • Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99-106.
  • Akyüz, G., Tosun, Ö., & Aka, S. (2020). Performance evaluation of non-life insurance companies with Best-Worst method and TOPSIS. Uluslararası Yönetim İktisat ve İşletme Dergisi, 16(1), 108-125.
  • Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & Banaitis, A. (2021). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International Journal of Sustainable Development & World Ecology, 28(2), 125-142.
  • Attaran, M., & Celik, B. G. (2023). Digital Twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 6, 100165.
  • Chen, Z. S., Chen, K. D., Xu, Y. Q., Pedrycz, W., & Skibniewski, M. J. (2024). Multiobjective optimization-based decision support for building digital twin maturity measurement. Advanced Engineering Informatics, 59, 102245.
  • Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266, 121981.
  • Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020). Digital Twin for maintenance: A literature review. Computers in Industry, 123, 103316.
  • Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952-108971.
  • Ghoushchi, S. J., Yousefi, S., & Khazaeili, M. (2019). An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Applied Soft Computing, 81, 105505.
  • Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31.
  • Henrichs, E., Noack, T., Pinzon Piedrahita, A. M., Salem, M. A., Stolz, J., & Krupitzer, C. (2021). Can a byte improve our bite? An analysis of digital twins in the food industry. Sensors, 22(1), 115.
  • informatics10010014 Park, K. T., Lee, J., Kim, H. J., & Noh, S. D. (2020). Digital twin-based cyber physical production system architectural framework for personalized production. The International Journal of Advanced Manufacturing Technology, 106, 1787-1810.
  • 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.
  • Karimi, H., Sadeghi-Dastaki, M., & Javan, M. (2020). A fully fuzzy best–worst multi attribute decision making method with triangular fuzzy number: A case study of maintenance assessment in the hospitals. Applied Soft Computing, 86, 105882.
  • Kheybari, S., Kazemi, M., & Rezaei, J. (2019). Bioethanol facility location selection using best-worst method. Applied Energy, 242, 612-623.
  • 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.
  • Loaiza, J. H., & Cloutier, R. J. (2022). Analyzing the implementation of a digital twin manufacturing system: Using a systems thinking approach. Systems, 10(2), 22.
  • Moreno, A., Velez, G., Ardanza, A., Barandiaran, I., de Infante, Á. R., & Chopitea, R. (2017). Virtualisation process of a sheet metal punching machine within the Industry 4.0 vision. International Journal on Interactive Design and Manufacturing (IJIDeM), 11(2), 365-373.
  • Neto, A. A., Deschamps, F., da Silva, E. R., & de Lima, E. P. (2020). Digital twins in manufacturing: an assessment of drivers, enablers and barriers to implementation. Procedia Cirp, 93, 210-215.
  • Opoku, D.-G.J., Perera, S., Osei-Kyei, R., Rashidi, M., Bamdad, K., Famakinwa, T. (2023). Barriers to the Adoption of Digital Twin in the Construction Industry: A Literature Review. Informatics, 10, 14. https://doi.org/10.3390/ informatics10010014.
  • 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.
  • Pires, F., Melo, V., Almeida, J., & Leitão, P. (2020, June). Digital twin experiments focusing virtualisation, connectivity and real-time monitoring. In 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), 1, 309-314, IEEE.
  • Rafsanjani, H. N., & Nabizadeh, A. H. (2023). Towards digital architecture, engineering, and construction (AEC) industry through virtual design and construction (VDC) and digital twin. Energy and Built Environment, 4(2), 169-178.
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  • Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021). Digital twin paradigm: A systematic literature review. Computers in Industry, 130, 103469.
  • Soori, M., Arezoo, B., & Dastres, R. (2023). Digital twin for smart manufacturing, A review. Sustainable Manufacturing and Service Economics, 100017.
  • Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022). Digital twin modeling. Journal of Manufacturing Systems, 64, 372-389.
  • Tekinerdogan, B., & Verdouw, C. (2020). Systems architecture design pattern catalog for developing digital twins. Sensors, 20(18), 5103.
  • Vachálek, J., Bartalský, L., Rovný, O., Šišmišová, D., Morháč, M., & Lokšík, M. (2017, June). The digital twin of an industrial production line within the industry 4.0 concept. In 2017 21st International Conference on Process Control (PC), 258-262, IEEE.
  • van Dinter, R., Tekinerdogan, B., & Catal, C. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, 107008.
  • Wu, J., Yang, Y., Cheng, X. U. N., Zuo, H., & Cheng, Z. (2020, November). The development of digital twin technology review. In 2020 Chinese Automation Congress (CAC), 4901-4906, IEEE.
  • Yadav, V. S., & Majumdar, A. (2024). What impedes digital twin from revolutionizing agro-food supply chain? Analysis of barriers and strategy development for mitigation. Operations Management Research, 1-17.
  • Yin, Y., Zheng, P., Li, C., & Wang, L. (2023). A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation. Robotics and Computer-Integrated Manufacturing, 81, 102515.
  • Zhang, R., Wang, F., Cai, J., Wang, Y., Guo, H., & Zheng, J. (2022). Digital twin and its applications: A survey. The International Journal of Advanced Manufacturing Technology, 123(11), 4123-4136.839

Barrier Analysis for Using Digital Twins in Production Systems

Year 2025, Volume: 9 Issue: 1, 247 - 261
https://doi.org/10.29216/ueip.1614573

Abstract

Digital twin is a platform where a digital equivalent of a real object, resource or process is created in a virtual environment and the physical world and virtual world interaction is kept alive instantly and dynamically through data connection. Since it is a newly developed system, various barriers that arise during the implementation of digital twin in production systems can make it difficult to adopt this platform. For digital twin to be successful in production projects, strategies are needed to eliminate barriers. In this study, barriers that may arise during the use of digital twins in production systems were determined and the barriers were compared with each other in line with expert opinions. The aim of the study is to determine the relative weights between the barriers and to reveal their priority. The fuzzy Best Worst method (BWM), which allows expert opinions to be modeled over linguistic variables, was used as the analysis technique. As a result, it was determined that the barrier of "systemic and technological integration deficiencies" is a priority for digital twin applications in production. The study examines the digital twin and production integration, reveals the elements that may prevent integration and provides a general framework for practitioners.

References

  • Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99-106.
  • Akyüz, G., Tosun, Ö., & Aka, S. (2020). Performance evaluation of non-life insurance companies with Best-Worst method and TOPSIS. Uluslararası Yönetim İktisat ve İşletme Dergisi, 16(1), 108-125.
  • Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & Banaitis, A. (2021). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International Journal of Sustainable Development & World Ecology, 28(2), 125-142.
  • Attaran, M., & Celik, B. G. (2023). Digital Twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 6, 100165.
  • Chen, Z. S., Chen, K. D., Xu, Y. Q., Pedrycz, W., & Skibniewski, M. J. (2024). Multiobjective optimization-based decision support for building digital twin maturity measurement. Advanced Engineering Informatics, 59, 102245.
  • Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266, 121981.
  • Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020). Digital Twin for maintenance: A literature review. Computers in Industry, 123, 103316.
  • Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952-108971.
  • Ghoushchi, S. J., Yousefi, S., & Khazaeili, M. (2019). An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Applied Soft Computing, 81, 105505.
  • Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31.
  • Henrichs, E., Noack, T., Pinzon Piedrahita, A. M., Salem, M. A., Stolz, J., & Krupitzer, C. (2021). Can a byte improve our bite? An analysis of digital twins in the food industry. Sensors, 22(1), 115.
  • informatics10010014 Park, K. T., Lee, J., Kim, H. J., & Noh, S. D. (2020). Digital twin-based cyber physical production system architectural framework for personalized production. The International Journal of Advanced Manufacturing Technology, 106, 1787-1810.
  • 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.
  • Karimi, H., Sadeghi-Dastaki, M., & Javan, M. (2020). A fully fuzzy best–worst multi attribute decision making method with triangular fuzzy number: A case study of maintenance assessment in the hospitals. Applied Soft Computing, 86, 105882.
  • Kheybari, S., Kazemi, M., & Rezaei, J. (2019). Bioethanol facility location selection using best-worst method. Applied Energy, 242, 612-623.
  • 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.
  • Loaiza, J. H., & Cloutier, R. J. (2022). Analyzing the implementation of a digital twin manufacturing system: Using a systems thinking approach. Systems, 10(2), 22.
  • Moreno, A., Velez, G., Ardanza, A., Barandiaran, I., de Infante, Á. R., & Chopitea, R. (2017). Virtualisation process of a sheet metal punching machine within the Industry 4.0 vision. International Journal on Interactive Design and Manufacturing (IJIDeM), 11(2), 365-373.
  • Neto, A. A., Deschamps, F., da Silva, E. R., & de Lima, E. P. (2020). Digital twins in manufacturing: an assessment of drivers, enablers and barriers to implementation. Procedia Cirp, 93, 210-215.
  • Opoku, D.-G.J., Perera, S., Osei-Kyei, R., Rashidi, M., Bamdad, K., Famakinwa, T. (2023). Barriers to the Adoption of Digital Twin in the Construction Industry: A Literature Review. Informatics, 10, 14. https://doi.org/10.3390/ informatics10010014.
  • 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.
  • Pires, F., Melo, V., Almeida, J., & Leitão, P. (2020, June). Digital twin experiments focusing virtualisation, connectivity and real-time monitoring. In 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), 1, 309-314, IEEE.
  • Rafsanjani, H. N., & Nabizadeh, A. H. (2023). Towards digital architecture, engineering, and construction (AEC) industry through virtual design and construction (VDC) and digital twin. Energy and Built Environment, 4(2), 169-178.
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  • Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021). Digital twin paradigm: A systematic literature review. Computers in Industry, 130, 103469.
  • Soori, M., Arezoo, B., & Dastres, R. (2023). Digital twin for smart manufacturing, A review. Sustainable Manufacturing and Service Economics, 100017.
  • Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022). Digital twin modeling. Journal of Manufacturing Systems, 64, 372-389.
  • Tekinerdogan, B., & Verdouw, C. (2020). Systems architecture design pattern catalog for developing digital twins. Sensors, 20(18), 5103.
  • Vachálek, J., Bartalský, L., Rovný, O., Šišmišová, D., Morháč, M., & Lokšík, M. (2017, June). The digital twin of an industrial production line within the industry 4.0 concept. In 2017 21st International Conference on Process Control (PC), 258-262, IEEE.
  • van Dinter, R., Tekinerdogan, B., & Catal, C. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, 107008.
  • Wu, J., Yang, Y., Cheng, X. U. N., Zuo, H., & Cheng, Z. (2020, November). The development of digital twin technology review. In 2020 Chinese Automation Congress (CAC), 4901-4906, IEEE.
  • Yadav, V. S., & Majumdar, A. (2024). What impedes digital twin from revolutionizing agro-food supply chain? Analysis of barriers and strategy development for mitigation. Operations Management Research, 1-17.
  • Yin, Y., Zheng, P., Li, C., & Wang, L. (2023). A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation. Robotics and Computer-Integrated Manufacturing, 81, 102515.
  • Zhang, R., Wang, F., Cai, J., Wang, Y., Guo, H., & Zheng, J. (2022). Digital twin and its applications: A survey. The International Journal of Advanced Manufacturing Technology, 123(11), 4123-4136.839
There are 34 citations in total.

Details

Primary Language Turkish
Subjects Business Administration
Journal Section RESEARCH ARTICLES
Authors

Salih Aka 0000-0002-6386-8582

Publication Date
Submission Date January 6, 2025
Acceptance Date March 26, 2025
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Aka, S. (n.d.). Üretim Sistemlerinde Dijital İkiz Kullanımına Yönelik Bariyer Analizi. Uluslararası Ekonomi İşletme Ve Politika Dergisi, 9(1), 247-261. https://doi.org/10.29216/ueip.1614573

Recep Tayyip Erdogan University
Faculty of Economics and Administrative Sciences
Department of Economics
RIZE / TÜRKİYE