DİJİTAL İKİZ TEKNOLOJİSİNİN İMALAT SEKTÖRÜNDE KULLANIMI NOKTASINDA KRİTİK ÖNEME SAHİP BAŞARI FAKTÖRLERİNİN SWARA YÖNTEMİYLE DEĞERLENDİRİLMESİ

Abstract

Dijital dönüşümle birlikte ortaya çıkan ileri teknolojilere dayalı Dijital İkiz (Digital Twin), özellikle üretimde operasyonel gelişimi vaat eden çok önemli bir teknoloji olarak karşımıza çıkmaktadır. Dijital ikiz teknolojisi, ürün yaşam seyri boyunca gerçek zamanlı bilgiler kullanarak yüksek kalite ve hızda kişiselleştirilmiş ürünlerin sürdürülebilir bir şekilde üretilmesini sağlayacak akıllı üretim sistemlerini oluşturma yöntemidir. Özellikle üretimde oluşturacağı olumlu etki nedeniyle sektör tarafından yaygın bir şekilde kullanımı önem taşımaktadır. Bu çalışmanın amacı, işletmeler açısından oldukça önemli olan dijital ikiz teknolojisinin imalat sektöründe başarı bir biçimde kullanımına katkı sağlayacak faktörlerin belirlenmesi ve önem düzeylerine göre sıralanmasıdır. Bu doğrultuda geniş bir literatür araştırması sonucunda belirlenen 8 adet kriter, uzman görüşlerine başvurulduktan sonra SWARA yöntemiyle analiz edilmiştir. Elde edilen bulgular, dijital ikiz teknolojisinin etkin kullanımı noktasında en önemli başarı faktörünün “üst yönetimin desteği” olduğu görülmektedir. Bu kriteri sırasıyla; “örgüt içi politika ve stratejilerin oluşturulması”, “yeterli mali kaynak” ve “güçlü bilgi teknolojileri altyapısı “izlemektedir. Dijital ikizin işletmelerde başarılı bir şekilde kullanılmasında daha düşük öneme sahip kriterlerin sırasıyla, “yüksek bilgi paylaşımı”, “yeterli teknik bilgiye sahip işgücü”, “yüksek veri güvenliği ve gizliliği” ve “teknolojik yeterlilik” olduğu sonucuna ulaşılmıştır.

Keywords

• Dijital İkiz
• Çok Kriterli Karar Verme
• SWARA
• Digital Twin
• Multi-Criteria Decision Making
• SWARA.

EVALUATION OF SUCCESS FACTORS WITH CRITICAL IMPORTANCE FOR THE USE OF DIGITAL TWIN TECHNOLOGY IN THE MANUFACTURING SECTOR WITH THE SWARA METHOD

Abstract

The Digital Twin, based on advanced technologies that emerged with the digital transformation, is a very important technology that promises operational development, especially in production. Digital twin technology is a method of creating smart production systems that will enable the sustainable production of personalized products at high quality and speed by using real-time information throughout the product lifecycle. It is important that it is widely used by the sector, especially because of the positive effect it will have on production. This study aims to determine the factors that will contribute to the widespread use of digital twin technology in the manufacturing sector, which is very important for businesses and to rank them according to their importance levels. In this direction, eight criteria determined as a result of a wide literature search were analyzed by the SWARA method after consulting expert opinions. The findings show that the most important success factor in the effective use of digital twin technology is the "support of the senior management". These criteria are respectively; “creation of internal policies and strategies”, “adequate financial resources” and “strong information technology infrastructure” are followed. It has been concluded that the criteria with lower importance in the successful use of the digital twin in businesses are "high information sharing", "workforce with sufficient technical knowledge", "high data security and privacy" and "technological competence".

Keywords

• Digital Twin
• Multi-Criteria Decision Making
• SWARA.

References

1. Bhatti, G., Mohan, H. ve Raja Singh, R. (2021). Towards the future of smart electric vehicles: Digital twin technology. Renewable and Sustainable Energy Reviews, 141, 110801.
2. Biesinger, F., Meike, D, Krab, B. ve Weyrich, M. (2019). A digital twin for production planning based on cyber-physical systems: A case study for a cyber-physical system-based creation of a digital twin. Procedia CIRP, 79, 355-360.
3. Brown, T.E. (2017). Sensor-based entrepreneurship: A framework for developing new products and services. Business Horizons, 60(6), 819-830.
4. Catapult, H.V. (2021). Untangling the requirements of a digital twin. Univ. Sheff. Adv. Manuf. Res. Cent. (AMRC), 7
5. Cheng, J., Zhang, H., Tao, F. ve Juang, C. F. (2020). DT-II: Digital twin enhanced industrial internet reference framework towards smart manufacturing. Robotics and. Computer- Integrated Manufacturing. 62, 101881.
6. Cimino, C., Hegri, E. ve Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113, 103130.
7. Çelik, T. (2022). Dijital ikizlere giriş ve savunma sanayii projelerinde dijital ikizlerin kullanımı. 10. Savunma Teknolojileri Kongresi SAVTEK 2022, ODTÜ, Ankara.
8. Dahooıe, J. H., Dehshiri, S. J. H., Banaıtıs, A. ve Bınkyte-Velıene, A. (2020). Identifying and prioritızing cost reduction solutions in the supply chain by integratıng value engineering and gray multi-criterıadecision-making. Technological and Economic Development of Economy, 26(6), 2029-4921.

9. Deepu, T. S. ve 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.
10. Dygalo, V., Keller, A. ve Shcherbin, A. (2020). Principles of application of virtual and physical simulation technology in production of digital twin of active vehicle safety systems. Transportation Research Procedia, 50, 121-129.
11. Grieves, M. ve Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Springer International Publishing: 85–113.
12. Gurria, A. (2017). The next production revolution: Implications for government and business, OECD Report OECD Publishing Press, 10.1787/f69a68e9-en.
13. Jones, D., Snider, C., Nassehi, A. Yon, J. ve Hicks, B. (2020). Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology. 29, 36-53.
14. Joshi, S. ve Sharma, M. (2022). Digital technologies (DT) adoption in agri-food supply chains amidst COVID-19: An approach towards food security concerns in developing countries. Journal of Global Operations and Strategic Sourcing. 15(2), 262-282. https://doi.org/10.1108/JGOSS-02-2021-0014.
15. Katchasuwanmanee, K., Bateman, R. ve Cheng, K. (2016). Development of the energy-smart production management system (e-proman): A big data driven approach, analysis and optimisation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 230(5), 972-978.
16. Kies, A., Kraub, J., Schmetz, A., Schmitt, R. ve Brecher, C. (2022). Interaction of digital twins in a sustainable battery cell production. Procedia CIRP, 107, 1216-1220.
17. Kritzinger, W., Karner, M., Traar, G., Henjes, J. ve Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC. 51(11), 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474.
18. Lin, D., Lee, C.K. ve Lin, K. (2016). Research on effect factors evaluation of internet of things (IOT) adoption in Chinese agricultural supply chain, IEEE International Conferancee Industry. Eng. Eng. Management: 612-615.
19. Lu, Y., Liu, C., 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.
20. Lyall, A., Mercier, P. ve Gstettner, S. (2018). The death of supply chain management. Harvard Business Review, 15, 2-4.
21. Majeed, R. A. ve Breesam, H. K. (2021). Application of SWARA technique to find criteria weights for selecting landfillsSite in Baghdad Governorate. Materials Science and Engineering, IOP Publishing: Bristol, UK.
22. Malik, A.A. ve Bilberg, A. (2018). Digital twins of human robot collaboration in a production setting. Procedia Manufacturing, 17, 278-285.
23. Negri, E., Fumagalli, L., ve Macchi, M. (2017). A review of the roles of digital twin in cpsbased production systems. Procedia Manufacturing, 11, 939–948.
24. Opoku, D. G. J., Perera, S., Osei-Kyei, R. ve Rashidi, M. (2021). Digital twin application in the construction industry: A literature review. Journal of Building Engineering, 40, 102726.
25. Pan, Y. ve Zhang, L. (2021). A BIM-Data mining integrated digital twin framework for advanced project management. Automation in Construction, 124, 103564.
26. Qi, Q., Tao, F., Zuo, Y. ve Zhao, D. (2018). Digital twin service towards smart manufacturing. Procedia 51st CIRP Conference on Manufacturing Systems, 72, 237–242. https://doi.org/10.1016/j.procir.2018.03.103.
27. Radovic, D. ve Stevic, Z. (2018). Evaluatıon and selection of KPI in transport using SWARA method. Trasport & Logistics: The International Journal, 18(44), 60-68.
28. Ricondo, I., Porto, A. ve Ugarte, M. (2021). A digital twin framework for the simulation and optimization of production systems. Procedia CIRP, 104, 762-767.
29. Rodic, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), 193–207.
30. Saraeian, S. ve Shirazi, B. (2022). Digital twin-based fault tolerance approach for Cyber–Physical Production System. ISA Transactions, https://doi.org/10.1016/j.isatra.2022.03.007.
31. Schleich, B., Anwer, N., Mathieu, L. ve Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals, 66(1), 141-144.
32. Sharma, R., Shishodia, A., Kamble, S., Gunasekaran, A. ve Belhadi, A. (2020). Agriculture supply chain risks and COVID-19: Mitigation strategies and implications for the practitioners, International Journal of Logistics Research and Applications, 1-27.
33. Tharma, R., Winter, R. ve Eigner, M. (2018). An approach for the implementation of the digital twin in the automotive wiring harness field. Proceedings of International Design Conference: 3023–3032.
34. Uhlemann, T.H.J., Lehmann, C. ve Steinhilper, R. (2017). The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia Cirp, 61, 335-340.
35. Uludağ, A. S. ve Doğan, H. (2021). Üretim yönetiminde çok kriterli karar verme yöntemleri: Literatür, teori ve uygulama, Ankara: Nobel Yayıncılık.
36. Verdouw, C. N. ve Kruize J. W. (2017). Digital twins in farm management: illustrations from the fiware accelerators smartagrifood and fractals. 7 th Asian-Australasian Conference on Precision Agriculture, 1–5.
37. Wang J., Ma Y., Gao R. X. ve Wu D. (2018). Deep learning for smart manufacturing: methods and applications. Journal of Manufacturing Systems, 48, 144–56.
38. Wang, Y.M., Wang, Y.S. ve Yang, Y.F. (2010). Understanding the determinants of RFID adoption in the manufacturing industry, Technol. Forecast. Soc. Change, 77(5), 803-815.
39. Wnag, Y., Kang, X. ve Chen, Z. (2022). A survey of digital twin techniques in smart manufacturing and management of energy applications. Green Energy and Intelligent Transportation. https://doi.org/10.1016/j.geits.2022.100014.
40. Wu, Z., Sun, J., Liang, L. ve Zha, Y. (2011). Determination of weights for ultimate cross efficiency using shannon entropy. Expert Systems With Applications, 38, 5162-5165.
41. Yang, Z. ve Lin, Y. (2020). The effects of supply chain collaboration on green innovation performance: An interpretive structural modeling analysis, Sustainable Production and Consumption, 23, 1-10.
42. Zolfani, S. H. ve Saparauskas, J. (2013). New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Inzinerine Ekonomika- Engineering Economics, 24(5), 408-414.
43. Zolfani, S. H., Salimi, J., Maknoon, R. ve Simona, K. (2015). Tecknology foresight about R&D projects selection; Application of SWARA method at the policy making level. Inzinerine Ekonomika- Engineering Economics, 26(5), 571-580.
44. Zolfani, S., Yazdani, M. ve Zavadskas, E. K. (2018). An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Computing, https://doi.org/10.1007/s00500-018-3092-2.
45. Zutshi, A. ve Grilo, A. (2019). The emergence of digital platforms: A conceptual platform architecture and impact on industrial engineering. Computers & Industrial Engineering, 136, 546-555 https://doi:10.1016/j.cie.2019.07.027.