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Journals / Yönetim Bilişim Sistemleri Dergisi / 2026 / Cilt: 12 Sayı: 1

DIGITALIZATION-ORIENTED BOM MANAGEMENT IN THE AUTOMOTIVE INDUSTRY

OTOMOTİV ENDÜSTRİSİNDE DİJİTALLEŞME ODAKLI BOM YÖNETİMİ

Merve Yılmaz(TOFAŞ TÜRK OTOMOBİL FABRİKASI A.Ş.)0009-0001-2816-7967
Burcu Sam(TOFAŞ TÜRK OTOMOBİL FABRİKASI A.Ş.)0009-0009-6312-1452
Pages
24–36
Publication Date
2026-06-25
DOI
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AI Summary & Key Takeaways

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

This narrative review examines the role of BOM management in accelerating product development processes and maintaining competitive advantages in the context of digitalization in the automative industry, and it analyzes the advantages of synchronizing engineering and production BOMs with digital twin through PLM systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Today, the automotive industry is rapidly evolving due to technological advancements, increased competition and changing consumer expectations, necessitating enhanced innovation and optimized manufacturing processes.
📊
Key Findings & Data:Effective Bill of Materials (BOM) management plays a crucial role in improving product development efficiency and expediting market introduction of innovations.
💡
Conclusion & Impact:This narrative review examines the role of BOM management in accelerating product development processes and maintaining competitive advantages in the context of digitalization in the automative industry, and it analyzes the advantages of synchronizing engineering and production BOMs with digital twin through PLM systems.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Malzeme Listesi (BOM) Yönetimi · Ürün Yaşam Döngüsü Yönetimi (PLM) · Dijital İkiz

Abstract

Today, the automotive industry is rapidly evolving due to technological advancements, increased competition and changing consumer expectations, necessitating enhanced innovation and optimized manufacturing processes. Effective Bill of Materials (BOM) management plays a crucial role in improving product development efficiency and expediting market introduction of innovations. As vehicle systems become more complex, especially with the integration of software and electronics in electric and autonomous vehicles, the role of synchronized BOMs with digital twins will become increasingly critical. Effective BOM management will facilitate not only the mechanical and physical aspects but also the integration and testing of software systems that control vehicle operations. This narrative review examines the role of BOM management in accelerating product development processes and maintaining competitive advantages in the context of digitalization in the automative industry, and it analyzes the advantages of synchronizing engineering and production BOMs with digital twin through PLM systems.

Özet

Otomotiv endüstrisi, teknolojik gelişmeler, artan rekabet ve değişen tüketici beklentileri nedeniyle hızla gelişmekte ve bu durum gelişmiş inovasyon ve optimize edilmiş üretim süreçlerini gerektirmektedir. Etkin Ürün Ağacı (BOM) yönetimi, ürün geliştirme verimliliğini artırmada ve yeniliklerin pazara sunulmasını hızlandırmada önemli bir rol oynamaktadır. Araç sistemleri, özellikle elektrikli ve otonom araçlarda yazılım ve elektroniğin entegrasyonu ile daha karmaşık hale geldikçe, dijital ikizlerle senkronize BOM'ların rolü giderek daha kritik hale gelecektir. Ürün ağacının etkili yönetimi sadece mekanik ve fiziksel unsurları değil, aynı zamanda araç operasyonlarını kontrol eden yazılım sistemlerinin entegrasyonu ve test edilmesini de kolaylaştıracaktır. Anlatısal derleme olarak hazırlanan çalışmada, BOM yönetiminin otomotiv endüstrisinde dijitalleşme bağlamında ürün geliştirme süreçlerini hızlandırmada ve rekabet avantajını korumadaki rolü incelenmiş ve PLM sistemleri aracılığıyla mühendislik ve üretim BOM'larının dijital ikizlerle senkronize edilmesinin avantajları analiz edilmiştir.

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Cite This Article

Yılmaz, M., Sam, B. (2026). DIGITALIZATION-ORIENTED BOM MANAGEMENT IN THE AUTOMOTIVE INDUSTRY. Yönetim Bilişim Sistemleri Dergisi, Cilt: 12 Sayı: 1, 24–36. https://ajindex.com/articles/1578460

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References (29)

  1. Aydin, A. O. and Güngör, A. (2005). Effective relational database approach to represent bills-of-materials. International Journal of Production Research, 43: 1143-1170.
  2. Capistrano Burgos, R., Sippl, F., Radisic-Aberger, O. and Weisser, T. (2022). Data-based method for the implementation planning of engineering changes in the automotive industry. Proceedings of the Design Society, 2: 343-352.
  3. Chen, J., Xiao, Y., Wang, G. and Guo, B. (2023). Research on the integrated management and mapping method of BOM multi-view for complex products. Mathematical Biosciences and Engineering, 20 (7): 12682–12699.
  4. Cinelli, M., Ferraro, G., Iovanella, A., Lucci, G. and Schiraldi, M. M. (2017). A network perspective on the visualization and analysis of bill of materials. International Journal of Engineering Business Management, 9.
  5. Dhuieb, M. A., Belkadi, F., Laroche, F. and Bernard, A. (2014). Thinking factory for the future: from PLM to augmented reality. EuroVR’14, Bremen, 8–10 December 2014.
  6. Gartner. (2020). Gartner says digital twins will be used by half of large industrial companies by 2021. https://www.gartner.com/en/newsroom/press-releases, (05.03.2025).
  7. Gehrke, I., Schauss, M., Küsters, D. and Gries, T. (2020). Experiencing the potential of closed-loop PLM systems enabled by industrial internet of things. Procedia Manufacturing, 45: 177-182.
  8. Grieves, M. and Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F.-J. Kahlen, S. Flumerfelt, and A. Alves (Eds.), Transdisciplinary perspectives on complex systems: New findings and approaches (pp. 85-113). Springer.
  9. Gunasekaran, A., Williams, H. J. and McGaughey, R. E. (2005). Performance measurement and costing system in new enterprise. Technovation, 25: 523-533.
  10. Karabulut, N. and Akyüz, Y. (2023). A comparison of RPA tool and Python programming language for a BOM digitalization project in automobile industry. Yönetim Bilişim Sistemleri Dergisi, 9 (2): 15-26.
  11. Kritzinger, W. et al. (2018). Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine, 51 (11): 1016-1022.
  12. Liu, S., Lu, Y., Shen, X. and Bao, J. (2023). A digital thread-driven distributed collaboration mechanism between digital twin manufacturing units. Journal of Manufacturing Systems, 68: 145-159.
  13. Lopez-Vega, H. and Moodysson, J. (2023). Digital transformation of the automotive industry: An integrating framework to analyse technological novelty and breadth. Industry and Innovation, 30 (1): 67-102.
  14. Luh, Y.-P., Chu, C.-H. and Pan, C.-C. (2010). Data management of green product development with generic modularized product architecture. Computers in Industry, 61 (3): 223-234.
  15. Moyne, J., Barton, K. and Tilbury, D. (2020). Digital twin-based cyber-attack detection framework for cyber-physical manufacturing systems. IEEE Transactions on Automation Science and Engineering, 17 (2): 865-879.
  16. OpenBOM. (t.y.). “BOM webinar: Who is who in bill of materials management process”, https://www.openbom.com/blog/bom-webinar-who-is-who-in-bill-of-materials-management-process, (18.09.2025).
  17. Piroumian, V. (2021). Digital twins: universal interoperability for the digital age. Computer, 54 (1): 61-69.
  18. Pronost, G., Mayer, F., Camargo, M. and Dupont, L. (2023). Digital twins along the product lifecycle: A systematic literature review of applications in manufacturing. Digital Twin, 3: Article 3.
  19. Saratech. (2024). PLM as the master data source for digital manufacturing. https://www.saratech.com/plm-master-data-source, (05.03.2025).
  20. Son, Y. H., Kim, G.-Y., Kim, H. C., Jun, C. and Noh, S. D. (2022). Past, present, and future research of digital twin for smart manufacturing. Journal of Computational Design and Engineering, 9 (1): 1-23.
  21. ST Endüstri. (t.y.). “Dünya genelinde 422 bin ünite robot kurulumu gerçekleşti”, https://www.stendustri.com.tr/dunya-genelinde-422-bin-unite-robot-kurulumu-gerceklesti, (12.09.2025).
  22. Syreyshchikova, N. V. et al. (2020). Automation of production activities of an industrial enterprise based on the ERP system. Procedia Manufacturing, 46: 525-532.
  23. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H. and Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94: 3563-3576.
  24. Tchana, Y. et al. (2019). Designing a unique digital twin for linear infrastructures lifecycle management. Procedia CIRP, 84: 545-549.
  25. Vieira, D., Vieira, R. and Chang Chain, M. (2016). Elements of managerial integration for sustainable product lifecycle management. International Journal of Product Lifecycle Management, 9: 87-107.
  26. Voskuil, J. (2023). The intersection of PLM and digital twin: Foundational concepts for modern manufacturing. PLM Industry Perspectives.
  27. Voskuil, J. (2024). The digital thread: Connecting product data across the lifecycle for digital twin enablement. PLM Industry Perspectives.
  28. Wang, Y., Ren, W., Zhang, C., Li, Y. and Yang, Z. (2022). Bill of material consistency reconstruction method for complex products driven by digital twin. The International Journal of Advanced Manufacturing Technology, 120 (1-2): 185-202.
  29. Zhao, Y., Liu, X. and Zhang, H. (2021). Effects of digital twin on product quality and compliance within PLM systems. International Journal of Advanced Manufacturing Technology, 116 (3-4): 1123-1138.