caku jiss Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 1309-3738 2791-9374 Cankiri Karatekin University 10.54558/jiss.1848061 Production and Operations Management Üretim ve Operasyon Yönetimi KOBİ'lerde İşbirlikçi Robotların Entegrasyonu: Otomotiv Yan Sanayinde Bir Vaka Çalışması Integration of Collaborative Robots in SMEs: A Case Study in the Automotive Sub-Industry https://orcid.org/0000-0001-8004-852X Taşar Bahar İZMİR DEMOKRASİ ÜNİVERSİTESİ 04 29 2026 17 1 113 124 12 24 2025 02 12 2026 Copyright © 2010, Journal of Institute of Social Sciences 2010 Journal of Institute of Social Sciences

Amaç: Endüstri 4.0’dan Endüstri 5.0’a geçiş süreci, ileri otomasyon teknolojileri ile insan odaklı üretim sistemlerine olan ilgiyi artırmıştır. Bu bağlamda işbirlikçi robotlar (cobotlar), insan-robot iş birliği yoluyla hem operasyonel performansı hem de çalışan ergonomisini geliştirme potansiyeline sahip önemli bir teknoloji olarak öne çıkmaktadır. Ancak bu avantajlara rağmen, küçük ve orta ölçekli işletmelerde (KOBİ’ler) cobot benimsemesi; yüksek yatırım maliyetleri ve yatırımın geri dönüşüne ilişkin belirsizlikler nedeniyle sınırlı kalmaktadır. Bu çalışmanın amacı, KOBİ’lerin kısıtları ve karar verme ihtiyaçları dikkate alınarak geliştirilen, pratik ve insan odaklı bir cobot entegrasyon çerçevesini ortaya koymak ve ampirik olarak doğrulamaktır. Yöntem: Çalışmada; süreç yeniden tasarımı, performans değerlendirmesi ve ekonomik fizibilite analizini bir araya getiren sekiz aşamalı bir cobot entegrasyon çerçevesi önerilmektedir. Önerilen çerçeve, otomotiv yan sanayinde faaliyet gösteren ve jant üretimi yapan bir KOBİ’de gerçekleştirilen simülasyon tabanlı bir vaka çalışması ile test edilmiştir. CNC işleme hücresindeki yükleme ve boşaltma operasyonları Ayrık Olay Simülasyonu kullanılarak modellenmiştir. Mevcut manuel sistem ve cobot entegre edilmiş sistem olmak üzere iki senaryo analiz edilmiştir. Çevrim süresi, günlük üretim miktarı ve genel ekipman etkinliği (OEE) gibi temel performans göstergeleri değerlendirilmiş; ayrıca yatırımın ekonomik uygulanabilirliğini belirlemek amacıyla geri ödeme süresi analizi yapılmıştır.Bulgular: Simülasyon sonuçları, cobot entegrasyonunun mevcut sisteme kıyasla çevrim süresini yaklaşık %6 oranında azalttığını göstermektedir. Günlük üretim miktarı 240 parçadan 267 parçaya yükselirken, OEE değeri %70,8’den yaklaşık %82 seviyesine çıkmıştır. Ekonomik analizler, cobot yatırımının yaklaşık 1,55 yıl içerisinde geri kazanılabileceğini ortaya koymakta ve önerilen yaklaşımın KOBİ’ler açısından finansal olarak uygulanabilir olduğunu göstermektedir.Sonuç: Elde edilen bulgular, simülasyon tabanlı analiz ve süreç yeniden tasarımı ile desteklenen cobot entegrasyonunun KOBİ’ler için ölçülebilir operasyonel ve ekonomik faydalar sağlayabileceğini doğrulamaktadır. Önerilen çerçeve, Endüstri 5.0 ilkeleriyle uyumlu, yapılandırılmış, veri temelli ve sürdürülebilir bir karar verme süreci sunmaktadır.Özgünlük: Bu çalışma, KOBİ’lere özel olarak tasarlanmış bütüncül ve pratik bir cobot entegrasyon çerçevesi sunarak literatüre katkı sağlamaktadır. Önceki çalışmaların çoğunlukla teknik uygunluk veya güvenlik boyutuna odaklanmasının aksine, bu araştırma; insan odaklı tasarım, simülasyon tabanlı performans değerlendirmesi ve ekonomik analizleri tek bir karar destek yapısı içinde bütünleştirmektedir.

Purpose: The transition from Industry 4.0 to Industry 5.0 has increased interest in human-centered production systems that focus on advanced automation technologies. In this context, collaborative robots (cobots) stand out as an important technology with the potential to improve both operational performance and worker ergonomics through human-robot collaboration. However, despite these advantages, cobot adoption in small and medium-sized enterprises (SMEs) remains limited due to high investment costs and uncertainties regarding return on investment. The aim of this study is to present and empirically validate a practical and human-centered cobot integration framework developed by considering the constraints and decision-making needs of SMEs.Methodology: The study proposes an eight-step cobot integration framework that combines process reengineering, performance evaluation, and economic feasibility analysis. The framework is tested through a simulation-based case study conducted in an SME operating in the automotive supply industry, specifically a wheel manufacturing facility. A CNC machining cell’s loading and unloading operations were modeled using Discreate Event Simulation. Two scenarios were analyzed: the existing manual system and a cobot-integrated system. Key performance indicators, including cycle time, daily production output, and overall equipment effectiveness (OEE), were evaluated. Additionally, a payback period analysis was conducted to assess economic viability.Findings: Results indicate that cobot integration leads to a cycle time reduction of approximately 6% compared to the existing system. Daily production output increased from 240 to 267 parts, while the OEE value improved significantly from 70.8% to nearly 82%. The economic analysis reveals that the cobot investment can be recovered within approximately 1.55 years, demonstrating the financial feasibility of the proposed approach for SMEs.Results: Cobot integration, when supported by simulation-based analysis and process reengineering, can deliver measurable operational and economic benefits for SMEs. The proposed framework enables firms to manage cobot investments through a structured, data-driven, and sustainable decision-making process aligned with Industry 5.0 principles.Originality/Value: This study contributes to the literature by offering a holistic and practical cobot integration framework specifically designed for SMEs. Unlike prior studies that focus primarily on technical feasibility or safety aspects, this research integrates human-centered design, simulation-based performance evaluation, and economic analysis within a unified decision-support structure.

 İşbirlikçi Robotlar Küçük ve Orta Ölçekli İşletmeler Ayrık Olay Simülasyonu Operasyonel Performans Collaborative Robots (Cobots) Small and Medium-Sized Enterprises (SMEs) Discrete-Event Simulation Operational Performance Aaltonen, I. & Salmi, T. (2019). Experiences and expectations of collaborative robots in industry and academia: Barriers and development needs. Procedia Manufacturing, 38, 1151-1158. https://doi.org/10.1016/j.promfg.2020.01.204 ABI Research. (2025). Collaborative robots pioneer automation revolution, market to reach US$7.2 billion by 2030 [Press release]. Erişim adresi: https://www.abiresearch.com/press/collaborative-robots-pioneer-automation-revolution-market-to-reach-us7.2-billion-by-2030/ Bag, S., Wood, L. C., Xu, L. & Dhamija, P. (2021). Industry 4.0 and human-robot collaboration: A systematic literature review and future research directions. Technological Forecasting and Social Change, 169, 120786. https://doi.org/10.1016/j.techfore.2021.120786 Baratta, A., Cimino, A., Longo, F. & Mirabelli, G. (2024). Task allocation in human–robot collaboration: A simulation-based approach to optimize operator’s productivity and ergonomics. Procedia Computer Science, 232(12), 688-697. https://doi.org/10.1016/j.procs.2024.01.068 Briken, K., Moore, J., Scholarios, D., Rose, E. & Sherlock, A. (2023). Industry 5.0 and the human in human-centric manufacturing. Sensors, 23(14), 6416. https://doi.org/10.3390/s23146416 Bogue, R. (2022). The changing face of the automotive robotics industry. Industrial Robot, 49(3), 386-390. https://doi.org/10.1108/IR-01-2022-0022 Faccio, M., Minto, R., Rosati, G. & Bottin, M. (2020). The influence of the product characteristics on human–robot collaboration: A model for the performance of collaborative robotic assembly. The International Journal of Advanced Manufacturing Technology, 106(5-6), 2317-2331. Gualtieri, L., Palomba, I., Merati, F. A., Rauch, E. & Vidoni, R. (2020). Design of human-centered collaborative assembly workstations for improving ergonomics and production efficiency: A case study. Sustainability, 12(9), 3606. https://doi.org/10.3390/su12093606 Hanna, A., Larsson, S., Götvall, P.-L. & Bengtsson, K. (2022). Deliberative safety for industrial intelligent human–robot collaboration: Regulatory challenges and solutions for taking the next step towards Industry 4.0. Robotics and Computer-Integrated Manufacturing, 78, 102386. https://doi.org/10.1016/j.rcim.2022.102386 Horst, J., Marvel, J. & Messina, E. (2021). Best practices for the integration of collaborative robots into workcells within small and medium-sized manufacturing operations (NIST AMS 100-41). https://doi.org/10.6028/NIST.AMS.100-41 Jennes, P. & Di Minin, A. (2023). Cobots in SMEs: Implementation processes, challenges, and success factors. In 2023 IEEE International Conference on Technology and Entrepreneurship (ICTE). https://doi.org/10.1109/ICTE58739.2023.10488658 Kimaporn, P. & Nunkaew, W. (2024). Combining metaheuristics and process mining: Improving cobot placement in a combined cobot assignment and job-shop scheduling problem. Operations Research Perspectives, 1836-1845. https://doi.org/10.1016/j.procs.2022.01.384 Kinast, A., Dörner, K. & Rinderle-Ma, S. (2020, November). Biased random-key genetic algorithm for cobot assignment in an assembly/disassembly job shop scheduling problem. In International Conference on Industry 4.0 and Smart Manufacturing. Universität Wien. http://eprints.cs.univie.ac.at/6568/ Liao, Y., Deschamps, F., Loures, E. D. F. R. & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0-A systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629. https://doi.org/10.1080/00207543.2017.1308576 Messina, E. R. (2024). Research opportunities for advancing measurement science for manufacturing robotics (NIST Grant/Contractor Report GCR 24-054). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.GCR.24-054 Mokhtarzadeh, M., Tavakkoli-Moghaddam, R., Vahedi-Nouri, B. & Farsi, A. (2020). Scheduling of human–robot collaboration in assembly of printed circuit boards: A constraint programming approach. International Journal of Computer Integrated Manufacturing, 33(5), 460-473. https://doi.org/10.1080/0951192X.2020.1736713 Manufacturing Technology Centre. (2024). A guide to human-robot collaboration in manufacturing. https://mtcprod.s3.eu-west-1.amazonaws.com/s3fs-public/2024-07/A%20Guide%20to%20Human-Robot%20Collaboration%20in%20Manufacturing.pdf Polonara, M., Romagnoli, A., Biancini, G. & Carbonari, L. (2024). Introduction of collaborative robotics in the production of automotive parts: A case study. Machines, 12(3), 196. https:// doi.org/10.3390/machines12030196 Porubčinová, M. & Fidlerová, H. (2020). Determinants of Industry 4.0 technology adaption and human–robot collaboration. Research Papers Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 28(46), 10-21. https://doi.org/10.2478/rput-2020-0002 Rajkumar, N., Yachipappan, B., Mathews, A., Radha, V. & Judeson Antony Kojippillai, C. (2025). Industry 5.0: The human-centric future of manufacturing. In V. Sharma et al. (Eds.), Challenges in information, communication and computing technology (pp. 562–567). Taylor & Francis. https://doi.org/10.1201/9781003590859-57 Shaw, J. (2024). Robots, cobots and their impact on automotive assembly. USC Consulting Group. Erişim adresi: https://usccg.com/blog/robots-cobots-and-their-impact-on-automotive-assembly/ Schnell, M. & Holm, M. (2022). Challenges for manufacturing SMEs in the introduction of collaborative robots. In Proceedings of the 10th Swedish Production Symposium (SPS2022) (pp. 3549-3557). IOS Press. Stecke, K. E. & Mokhtarzadeh, M. (2022). Balancing collaborative human–robot assembly lines to optimise cycle time and ergonomic risk. International Journal of Production Research, 60(1), 25-47. https://doi.org/10.1080/00207543.2021.1989077 Thongdonnoi, C., Chutima, P. & Jiamsanguanwong, A. (2023). Application of collaborative robots for increasing productivity in an eyeglasses lenses manufacturer. Engineering Journal, 27(10), 93-112. https://doi.org/10.4186/ej.2023.27.10.93 Țițu, A.-M., Gușan, V. & Dragomir, M. (2023). Integration of collaborative robots in the automotive industry during post-pandemic recovery. Acta Technica Napocensis – Series: Applied Mathematics, Mechanics, and Engineering, 66(1S). https://atna-mam.utcluj.ro/index.php/Acta/article/view/2224 Tran, B. & Attorney, P. (2025). SMEs & robotics: Are small manufacturers adopting? PatentPC. Erişim adresi: https://patentpc.com/blog/smes-robotics-are-small-manufacturers-adopting Xu, L. D., Xu, E. L. & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806 Vido, M., Scur, G., Massote, A. A. & Lima, F. (2020). The impact of the collaborative robot on competitive priorities: Case study of an automotive supplier. Gestão & Produção, 27(4). https://doi.org/10.1590/0104-530x5358-20 Villani, V., Pini, F., Leali, F. & Secchi, C. (2018). Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics, 55(2), 248-266. https://doi.org/10.1016/j.mechatronics.2018.02.009 Weckenborg, C., Kieckhäfer, K., Müller, C., Grunewald, M. & Spengler, T. S. (2020). Balancing of assembly lines with collaborative robots. Business Research, 13(1), 93-132. Zhang, Y.-J., Liu, L., Huang, N., Radwin, R. & Li, J. (2021). From manual operation to collaborative robot assembly: An integrated model of productivity and ergonomic performance. IEEE Robotics and Automation Letters, 6(2), 895-902.