Journals / Uluslararası Davranış, Sürdürülebilirlik ve Yönetim Dergisi / 2026 / Cilt: 13 Sayı: 24

Analysis of the Advantages of Digital Twin Technology in Supply Chains Based on KPI Criteria

Tedarik Zincirlerinde Dijital İkiz Teknolojisinin Avantajlarının KPI Kriterleri Üzerinden Analizi

Pages
1–13
Publication Date
2026-06-29
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, this study aims to contribute to the holistic evaluation of the advantages offered by digital twin technology in supply chains and to propose an original method to the literature with an analysis model based on MCDM.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The main purpose of this study is to determine the advantages offered by the use of digital twins in supply chain management and to systematically classify these advantages within the framework of Key Performance Indicators (KPIs).
📊
Key Findings & Data:The obtained KPIs will be analyzed using multi-criteria decision-making (MCDM) methods which is BWM method and the contributions of digital twin technology to supply chain performance will be ranked according to their importance.
💡
Conclusion & Impact:As a result, this study aims to contribute to the holistic evaluation of the advantages offered by digital twin technology in supply chains and to propose an original method to the literature with an analysis model based on MCDM.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Dijital İkiz · Tedarik Zinciri Yönetimi · Temel Performans Göstergeleri (KPI)

Abstract

The main purpose of this study is to determine the advantages offered by the use of digital twins in supply chain management and to systematically classify these advantages within the framework of Key Performance Indicators (KPIs). The obtained KPIs will be analyzed using multi-criteria decision-making (MCDM) methods which is BWM method and the contributions of digital twin technology to supply chain performance will be ranked according to their importance. The gains provided by digital twin applications in multi-dimensional performance areas such as energy efficiency, carbon emission reduction, shortening delivery times, resource utilization optimization and reducing operational costs constitute the basic analysis framework of the study. This analysis will reveal which performance indicators digital twin technology has the greatest impact on in supply chains and will provide decision support to managers so that they can direct their technology investments more consciously. As a result, this study aims to contribute to the holistic evaluation of the advantages offered by digital twin technology in supply chains and to propose an original method to the literature with an analysis model based on MCDM.

Özet

Bu çalışmanın temel amacı, tedarik zinciri yönetiminde dijital ikiz kullanımının sunduğu avantajları belirlemek ve bu avantajları Temel Performans Göstergeleri (KPI) çerçevesinde sistematik olarak sınıflandırmaktır. Elde edilen KPI’lar, Çok Kriterli Karar Verme (ÇKKV) Yöntemlerinden biri olan BWM (Best-Worst Method) yöntemi kullanılarak analiz edilecek ve dijital ikiz teknolojisinin tedarik zinciri performansına katkıları önem derecelerine göre sıralanacaktır. Dijital ikiz uygulamalarının enerji verimliliği, karbon emisyonlarının azaltılması, teslimat sürelerinin kısaltılması, kaynak kullanım optimizasyonu ve operasyonel maliyetlerin düşürülmesi gibi çok boyutlu performans alanlarında sağladığı kazanımlar, çalışmanın temel analiz çerçevesini oluşturmaktadır. Bu analiz sayesinde, tedarik zincirlerinde dijital ikiz teknolojisinin hangi performans göstergeleri üzerinde en büyük etkiyi yarattığı ortaya konulacak ve yöneticilere teknoloji yatırımlarını daha bilinçli yönlendirebilmeleri için karar desteği sunulacaktır. Sonuç olarak, bu çalışma tedarik zincirlerinde dijital ikiz teknolojisinin sunduğu avantajların bütüncül bir şekilde değerlendirilmesine katkı sağlamayı ve ÇKKV temelli bir analiz modeli ile literatüre özgün bir yöntem önerisi getirmeyi hedeflemektedir.

🔖

Cite This Article

Tayhava, B., Yontar, E. (2026). Analysis of the Advantages of Digital Twin Technology in Supply Chains Based on KPI Criteria. Uluslararası Davranış, Sürdürülebilirlik ve Yönetim Dergisi, Cilt: 13 Sayı: 24, 1–13. https://doi.org/10.71444/jobesam.1780333

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (34)

  1. Aşan, H., ve Ayçin, E. (2020). Kurumsal kaynak planlama sistemlerinin seçimindeki kriterlerin best-worst metodu ile değerlendirilmesi. Akademik İzdüşüm Dergisi, 5(2), 114-124.
  2. Barricelli, B. R., Casiraghi, E., ve Fogli, D. (2019). A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 7, 167653–167670.
  3. Baysal, H. Ö. Z. Endüstri 5.0’da Dijital İkiz ve Denetim. Sanayi Yönetiminde Gelecek Yaklaşımları Dijitalleşmeve Yetenekler, 223.
  4. Boschert, S., ve Rosen, R. (2016). Digital twin—the simulation aspect. In Mechatronic futures: Challenges and solutions for mechatronic systems and their designers (pp. 59-74). Cham: Springer International Publishing.
  5. Brockett, A., ve Rezaee, Z. (2012). Corporate sustainability: Integrating performance and reporting. John Wiley ve Sons.
  6. Daneshvar Kakhki, M., ve Gargeya, V. B. (2019). Information systems for supply chain management: a systematic literature analysis. International Journal of Production Research, 57(15-16), 5318-5339.
  7. Deng, J., Zhou, F., Hou, W., Silver, Z., Wong, C. Y., Chang, O.,. ve Zuo, Q. K. (2021). The prevalence of depression, anxiety, and sleep disturbances in COVID-19 patients: a meta-analysis. Annals of the new York Academy of Sciences, 1486(1), 90-111.
  8. Dipura, S., ve Soediantono, D. (2022). Benefits of key performance indicators (KPI) and proposed applications in the defense industry: a literature review. International Journal of Social and Management Studies, 3(4), 23-33.
  9. Freese, F., ve Ludwig, A. (2025). A conceptual framework for supply chain digital twins–development and evaluation. International Journal of Logistics Research and Applications, 28(6), 676-698.
  10. Grieves, M. W. (2019). Virtually intelligent product systems: Digital and physical twins.
  11. Grieves, M., ve Vickers, J. (2016). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems: New findings and approaches (pp. 85-113). Cham: Springer International Publishing.
  12. Grieves, M., ve Vickers, J. (2017). Digital Twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F.-J. Kahlen, S. Flumerfelt, ve A. Alves (Eds.), Transdisciplinary Perspectives on Complex Systems (pp. 85–113). Springer. https://bestworstmethod.com/
  13. Ivanov, D., ve Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International journal of production research, 58(10), 2904-2915.
  14. Ivanov, D., Blackhurst, J., ve Das, A. (2021). Supply chain resilience and its interplay with digital technologies: Making innovations work in emergency situations. International journal of physical distribution ve logistics management, 51(2), 97-103.
  15. Karşıyaka, O., Sütçü, A. (2019). Mobilya Üretim Süreçlerinde Verimliliği Artırmaya Yönelik 5S Uygulamaları. Bilge International Journal of Science and Technology Research, 3(2), 87-101.
  16. Kayan, R. R., Jauhar, S. K., Kamble, S. S., ve Belhadi, A. (2025). Optimizing bio-hydrogen production from agri-waste: A digital twin approach for sustainable supply chain management and carbon neutrality. Computers ve Industrial Engineering, 204, 111021.
  17. Klappich, D. (2019). Hype cycle for supply chain execution technologies. Available at: gartner. COM/en/documents/3947306/hype-cycle-for-supply-chain-execution-technologies-2019.
  18. Koca, G., ve Akçakaya, E. D. U. (2021). Giyilebilir teknolojik ürünlerin tasarımında etkili olan faktörlerin best- worst metodu (BWM) ile değerlendirilmesi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 8(1), 136-150.
  19. Kritzinger, W., Karner, M., Traar, G., Henjes, J., ve Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022.
  20. Leng, J., Liu, Q., Ye, S., ve Zhang, H. (2021). Digital twins-based smart manufacturing system design in industry 4.0: A review. Journal of Manufacturing Systems, 60, 278–292.
  21. Liu, C., Gu, R., Yang, J., Luo, L., Chen, M., Xiong, Y.,. ve Sun, Q. (2024). A Self-Powered Dual Ratchet Angl Sensing System for Digital Twins and Smart Healthcare. Advanced Functional Materials, 34(42), 2405104.
  22. Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., ve Wang, F. Y. (2021). A digital twin-based approach for smart manufacturing: A case study of intelligent CNC machine tools. Journal of Manufacturing Systems, 58, 84–94.
  23. Lu, Y., Liu, C., Kevin, I., Wang, K., Huang, H., ve Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and computer-integrated manufacturing, 61, 101837.
  24. Marmolejo-Saucedo, J. A. (2020). Design and development of digital twins: A case study in supply chains. Mobile Networks and Applications, 25(6), 2141-2160.
  25. Öktem, B. (2019). Sürdürülebilirlik Raporlamasında Ölçüm Parametreleri KPI’lar (Temel Performans Göstergelerı): Bankacılık Sektöründe Analiz. Finansal Araştırmalar ve Çalışmalar Dergisi, 11(21), 247-263.
  26. Pujawan, I. N., ve Bah, A. U. (2021). Supply chains under COVID-19 disruptions: Literature review and research agenda. Supply Chain. In Forum Int. J.
  27. Qi, Q., ve Tao, F. (2018). Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access, 6, 3585–3593.
  28. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  29. Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
  30. Shengli, W. (2021). Is human digital twin possible?. Computer Methods and Programs in Biomedicine Update, 1, 100014.
  31. Tao, F., Zhang, H., Liu, A., ve Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
  32. Verdouw, C., Tekinerdogan, B., Beulens, A., ve Wolfert, S. (2021). Digital twins in smart farming. Agricultural Systems, 189, 103046.
  33. Deng, J., Zhou, F., Hou, W., Silver, Z., Wong, C. Y., Chang, O.,. ve Zuo, Q. K. (2021). The prevalence of depression, anxiety, and sleep disturbances in COVID-19 patients: a meta-analysis. Annals of the new York Academy of Sciences, 1486(1), 90-111.
  34. Liu, C., Gu, R., Yang, J., Luo, L., Chen, M., Xiong, Y.,. ve Sun, Q. (2024). A Self-Powered Dual Ratchet Angl Sensing System for Digital Twins and Smart Healthcare. Advanced Functional Materials, 34(42), 2405104.