Research Article
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KOBİ’ler için Bulut Tabanlı Üretim Yürütme Sisteminin Tasarımı ve Gerçekleştirilmesi

Year 2026, Volume: 17 Issue: 1, 68 - 87, 01.03.2026
https://izlik.org/JA39SE47LT

Abstract

This study proposes a multi-tenant, modular, microservices-based, cloud-based MES system designed to meet the digital transformation needs of small and medium-sized manufacturing enterprises (SMEs). The platform is designed as a microservices-based Software-as-a-Service (SaaS) solution accessible via a standard web browser. The platform provides RESTful API support to facilitate easy integration with other applications. A key contribution of the proposed model is its ability to provide real-time data processing and advanced analytics capability. The system provides instantly monitoring of basic production data such as production tracking, downtime-quality analysis, and Overall Equipment Effectiveness (OEE) calculations. In addition to OEE components analysis, the platform supports resource planning and automation processes, document viewing on operator terminals, and event-based alarms for critical production events. Implementation results demonstrates that SME’s can obtain actionable production insights(e.g., OEE trends, downtime loss) with a low deployment effort and scable cloud architecture.

Ethical Statement

Bu çalışmanın, özgün bir çalışma olduğunu; çalışmanın hazırlık, veri toplama, analiz ve bilgilerin sunumu olmak üzere tüm aşamalarından bilimsel etik ilke ve kurallarına uygun davrandığımı; bu çalışma kapsamında elde edilmeyen tüm veri ve bilgiler için kaynak gösterdiğimi ve bu kaynaklara kaynakçada yer verdiğimi; kullanılan verilerde herhangi bir değişiklik yapmadığımı, çalışmanın AJIT-e: Academic Journal of Information Technology in tüm şartlarını ve koşullarını kabul ederek etik görev ve sorumluluklara riayet ettiğimi beyan ederim.

References

  • Chen, D. (2005). Enterprise-control system integration—An international standard. International Journal of Production Research, 43(20), 4335–4357. https://doi.org/10.1080/00207540500142399
  • Czvetkó, T., & Abonyi, J. (2023). Data sharing in Industry 4.0 – AutomationML, B2MML and International Data Spaces-based solutions. Journal of Industrial Information Integration, 33, 100438. https://doi.org/10.1016/j.jii.2023.100438
  • Dutta, G., Kumar, R., Sindhwani, R., & Singh, R. K. (2022). Overcoming the barriers of effective implementation of manufacturing execution system in pursuit of smart manufacturing in SMEs. Procedia Computer Science, 200, 820–832. https://doi.org/10.1016/j.procs.2022.01.279
  • Emon, M. M. H., & Khan, T. (2025). The transformative role of Industry 4.0 in supply chains: Exploring digital integration and innovation in manufacturing enterprises. Journal of Open Innovation: Technology, Market, and Complexity, 11(2), 100516. https://doi.org/10.1016/j.joitmc.2025.100516
  • Eswaramurthi, K. G., & Mohanram, P. V. (2013). Improvement of manufacturing performance measurement system and evaluation of overall resource effectiveness. American Journal of Applied Sciences, 10(2), 131–138. https://doi.org/10.3844/ajassp.2013.131.138
  • Gharibvand, V., Kolamroudi, M. K., Zeeshan, Q., Çınar, Z. M., Sahmani, S., Asmael, M., & Safaei, B. (2024). Cloud based manufacturing: A review of recent developments in architectures, technologies, infrastructures, platforms and associated challenges. The International Journal of Advanced Manufacturing Technology, 131(1), 93–123. https://doi.org/10.1007/s00170-024-12989-y
  • Helo, P., Suorsa, M., Hao, Y., & Anussornnitisarn, P. (2014). Toward a cloud-based manufacturing execution system for distributed manufacturing. Computers in Industry, 65(4), 646–656. https://doi.org/10.1016/j.compind.2014.01.015
  • Honarpour, A., Jusoh, A., & Md Nor, K. (2012). Knowledge management, total quality management and innovation: A new look. Journal of Technology Management & Innovation, 7(3), 22–31. https://doi.org/10.4067/S0718-27242012000300003
  • Ko, M., Lee, C., & Cho, Y. (2022). Design and implementation of cloud-based collaborative manufacturing execution system in the Korean fashion industry. Applied Sciences, 12(18), 9381. https://doi.org/10.3390/app12189381
  • Lenart, A. (2011). ERP in the cloud – Benefits and challenges (pp. 39–50). https://doi.org/10.1007/978-3-642-25676-9_4
  • Mantravadi, S., & Møller, C. (2019). An overview of next-generation manufacturing execution systems: How important is MES for Industry 4.0? Procedia Manufacturing, 30, 588–595. https://doi.org/10.1016/j.promfg.2019.02.083
  • Mell, P. M., & Grance, T. (2011). The NIST definition of cloud computing (NIST Special Publication 800-145). https://doi.org/10.6028/NIST.SP.800-145
  • Ng Corrales, L. del C., Lambán, M. P., Hernandez Korner, M. E., & Royo, J. (2020). Overall equipment effectiveness: Systematic literature review and overview of different approaches. Applied Sciences, 10(18), 6469. https://doi.org/10.3390/app10186469
  • Niekurzak, M., & Lewicki, W. (2025). Optimisation of the production process of ironing refractory products using the OEE indicator as part of innovative solutions for sustainable production. Sustainability, 17(11). https://doi.org/10.3390/su17114779
  • Oliveira, D., Alvelos, H., & Rosa, M. J. (2025). Quality 4.0: Results from a systematic literature review. The TQM Journal, 37(2), 379–456. https://doi.org/10.1108/TQM-01-2023-0018
  • Panchal, G., & Shaikh, W. A. (2024). Digital transformation and Industry 5.0 roadmap for SMEs to drive productivity and sustainability. https://www.researchgate.net/publication/386046267
  • Pessl, E., & Rabel, B. (2022). Digitization in production: A use case on a cloud-based manufacturing execution system. In Proceedings of the 2022 8th International Conference on Computer Technology Applications (pp. 206–210). https://doi.org/10.1145/3543712.3543730
  • Rahayu, P. C., & Wicaksono, K. A. (2024). Real time OEE monitoring for intelligent manufacture technology. In Proceedings of the 2024 15th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT) (pp. 80–83). https://doi.org/10.1109/ICMIMT61937.2024.10585713
  • Saenz de Ugarte, B., Artiba, A., & Pellerin, R. (2009). Manufacturing execution system – A literature review. Production Planning & Control, 20(6), 525–539. https://doi.org/10.1080/09537280902938613
  • Sahoo, S., & Lo, C.-Y. (2022). Smart manufacturing powered by recent technological advancements: A review. Journal of Manufacturing Systems, 64, 236–250.
  • Shojaeinasab, A., Charter, T., Jalayer, M., Khadivi, M., Ogunfowora, O., Raiyani, N., Yaghoubi, M., & Najjaran, H. (2022). Intelligent manufacturing execution systems: A systematic review. Journal of Manufacturing Systems, 62, 503–522. https://doi.org/10.1016/j.jmsy.2022.01.004
  • Talan, K., & Gupta, N. (2026). Comparative analysis of Industry 4.0 and blockchain adoption readiness dimensions in manufacturing sector: A systematic literature review and research agenda. Future Business Journal, 12(1), 19. https://doi.org/10.1186/s43093-026-00731-x
  • Tortorella, G. L., Fogliatto, F. S., Cauchick-Miguel, P. A., Kurnia, S., & Jurburg, D. (2021). Integration of Industry 4.0 technologies into total productive maintenance practices. International Journal of Production Economics, 240, 108224. https://doi.org/10.1016/j.ijpe.2021.108224
  • Zhang, C., Wang, Y., Zhao, Z., Chen, X., Ye, H., Liu, S., Yang, Y., & Peng, K. (2024). Performance-driven closed-loop optimization and control for smart manufacturing processes in the cloud-edge-device collaborative architecture: A review and new perspectives. Computers in Industry, 162, 104131. https://doi.org/10.1016/j.compind.2024.104131

Design and Implementation of a Cloud-Based Manufacturing Execution System for SMEs

Year 2026, Volume: 17 Issue: 1, 68 - 87, 01.03.2026
https://izlik.org/JA39SE47LT

Abstract

Bu çalışma; küçük ve orta ölçekli işletmelerin (KOBİ) dijital dönüşüm ihtiyaçlarını karşılamak üzere tasarlanmış çok kiracılı, modüler ve mikro hizmet tabanlı bir bulut MES modeli önermektedir. Platform, standart bir web tarayıcısı üzerinden erişilebilen mikroservis tabanlı Hizmet Olarak Yazılım (SaaS) çözümü olarak tasarlanmıştır. Ayrıca, diğer uygulamalarla kolay entegrasyon sağlamak amacıyla RESTful API desteği sunmaktadır. Önerilen sistemin temel katkısı, gerçek zamanlı veri işleme ve ileri analitik yetenekleri sağlama kapasitesidir. Sistem; üretim takibi, duruş ve kalite analizi ile Genel Ekipman Etkinliği (OEE) hesaplamaları gibi temel üretim verilerinin anlık izlenmesini sağlamaktadır. OEE bileşen analizine ek olarak platform; kaynak planlama ve otomasyon süreçlerini desteklemekte, operatör terminallerinde doküman görüntüleme imkanı sunmakta ve kritik üretim olayları için olay tabanlı alarm mekanizmaları sağlamaktadır. Uygulama sonuçları, KOBİ'lerin düşük kurulum maliyeti ve ölçeklenebilir bulut mimarisi sayesinde üretim içgörülerini (örneğin; OEE trendleri, duruş kayıpları) etkin bir şekilde elde edebileceğini ortaya koymaktadır.

Ethical Statement

542 / 5.000 I declare that this study is an original study; that I have acted in accordance with the principles and rules of scientific ethics in all stages of the study, including preparation, data collection, analysis, and presentation of information; that I have cited all data and information not obtained within the scope of this study and included these sources in the bibliography; that I have not made any changes to the data used; that I have accepted all terms and conditions of AJIT-e: Academic Journal of Information Technology and have complied with ethical duties and responsibilities.

References

  • Chen, D. (2005). Enterprise-control system integration—An international standard. International Journal of Production Research, 43(20), 4335–4357. https://doi.org/10.1080/00207540500142399
  • Czvetkó, T., & Abonyi, J. (2023). Data sharing in Industry 4.0 – AutomationML, B2MML and International Data Spaces-based solutions. Journal of Industrial Information Integration, 33, 100438. https://doi.org/10.1016/j.jii.2023.100438
  • Dutta, G., Kumar, R., Sindhwani, R., & Singh, R. K. (2022). Overcoming the barriers of effective implementation of manufacturing execution system in pursuit of smart manufacturing in SMEs. Procedia Computer Science, 200, 820–832. https://doi.org/10.1016/j.procs.2022.01.279
  • Emon, M. M. H., & Khan, T. (2025). The transformative role of Industry 4.0 in supply chains: Exploring digital integration and innovation in manufacturing enterprises. Journal of Open Innovation: Technology, Market, and Complexity, 11(2), 100516. https://doi.org/10.1016/j.joitmc.2025.100516
  • Eswaramurthi, K. G., & Mohanram, P. V. (2013). Improvement of manufacturing performance measurement system and evaluation of overall resource effectiveness. American Journal of Applied Sciences, 10(2), 131–138. https://doi.org/10.3844/ajassp.2013.131.138
  • Gharibvand, V., Kolamroudi, M. K., Zeeshan, Q., Çınar, Z. M., Sahmani, S., Asmael, M., & Safaei, B. (2024). Cloud based manufacturing: A review of recent developments in architectures, technologies, infrastructures, platforms and associated challenges. The International Journal of Advanced Manufacturing Technology, 131(1), 93–123. https://doi.org/10.1007/s00170-024-12989-y
  • Helo, P., Suorsa, M., Hao, Y., & Anussornnitisarn, P. (2014). Toward a cloud-based manufacturing execution system for distributed manufacturing. Computers in Industry, 65(4), 646–656. https://doi.org/10.1016/j.compind.2014.01.015
  • Honarpour, A., Jusoh, A., & Md Nor, K. (2012). Knowledge management, total quality management and innovation: A new look. Journal of Technology Management & Innovation, 7(3), 22–31. https://doi.org/10.4067/S0718-27242012000300003
  • Ko, M., Lee, C., & Cho, Y. (2022). Design and implementation of cloud-based collaborative manufacturing execution system in the Korean fashion industry. Applied Sciences, 12(18), 9381. https://doi.org/10.3390/app12189381
  • Lenart, A. (2011). ERP in the cloud – Benefits and challenges (pp. 39–50). https://doi.org/10.1007/978-3-642-25676-9_4
  • Mantravadi, S., & Møller, C. (2019). An overview of next-generation manufacturing execution systems: How important is MES for Industry 4.0? Procedia Manufacturing, 30, 588–595. https://doi.org/10.1016/j.promfg.2019.02.083
  • Mell, P. M., & Grance, T. (2011). The NIST definition of cloud computing (NIST Special Publication 800-145). https://doi.org/10.6028/NIST.SP.800-145
  • Ng Corrales, L. del C., Lambán, M. P., Hernandez Korner, M. E., & Royo, J. (2020). Overall equipment effectiveness: Systematic literature review and overview of different approaches. Applied Sciences, 10(18), 6469. https://doi.org/10.3390/app10186469
  • Niekurzak, M., & Lewicki, W. (2025). Optimisation of the production process of ironing refractory products using the OEE indicator as part of innovative solutions for sustainable production. Sustainability, 17(11). https://doi.org/10.3390/su17114779
  • Oliveira, D., Alvelos, H., & Rosa, M. J. (2025). Quality 4.0: Results from a systematic literature review. The TQM Journal, 37(2), 379–456. https://doi.org/10.1108/TQM-01-2023-0018
  • Panchal, G., & Shaikh, W. A. (2024). Digital transformation and Industry 5.0 roadmap for SMEs to drive productivity and sustainability. https://www.researchgate.net/publication/386046267
  • Pessl, E., & Rabel, B. (2022). Digitization in production: A use case on a cloud-based manufacturing execution system. In Proceedings of the 2022 8th International Conference on Computer Technology Applications (pp. 206–210). https://doi.org/10.1145/3543712.3543730
  • Rahayu, P. C., & Wicaksono, K. A. (2024). Real time OEE monitoring for intelligent manufacture technology. In Proceedings of the 2024 15th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT) (pp. 80–83). https://doi.org/10.1109/ICMIMT61937.2024.10585713
  • Saenz de Ugarte, B., Artiba, A., & Pellerin, R. (2009). Manufacturing execution system – A literature review. Production Planning & Control, 20(6), 525–539. https://doi.org/10.1080/09537280902938613
  • Sahoo, S., & Lo, C.-Y. (2022). Smart manufacturing powered by recent technological advancements: A review. Journal of Manufacturing Systems, 64, 236–250.
  • Shojaeinasab, A., Charter, T., Jalayer, M., Khadivi, M., Ogunfowora, O., Raiyani, N., Yaghoubi, M., & Najjaran, H. (2022). Intelligent manufacturing execution systems: A systematic review. Journal of Manufacturing Systems, 62, 503–522. https://doi.org/10.1016/j.jmsy.2022.01.004
  • Talan, K., & Gupta, N. (2026). Comparative analysis of Industry 4.0 and blockchain adoption readiness dimensions in manufacturing sector: A systematic literature review and research agenda. Future Business Journal, 12(1), 19. https://doi.org/10.1186/s43093-026-00731-x
  • Tortorella, G. L., Fogliatto, F. S., Cauchick-Miguel, P. A., Kurnia, S., & Jurburg, D. (2021). Integration of Industry 4.0 technologies into total productive maintenance practices. International Journal of Production Economics, 240, 108224. https://doi.org/10.1016/j.ijpe.2021.108224
  • Zhang, C., Wang, Y., Zhao, Z., Chen, X., Ye, H., Liu, S., Yang, Y., & Peng, K. (2024). Performance-driven closed-loop optimization and control for smart manufacturing processes in the cloud-edge-device collaborative architecture: A review and new perspectives. Computers in Industry, 162, 104131. https://doi.org/10.1016/j.compind.2024.104131
There are 24 citations in total.

Details

Primary Language English
Subjects Big Data, Data Management and Data Science (Other)
Journal Section Research Article
Authors

Devrim Naz Akdaş 0000-0002-8599-7991

Semih Utku 0000-0002-8786-560X

Submission Date October 22, 2025
Acceptance Date February 25, 2026
Publication Date March 1, 2026
IZ https://izlik.org/JA39SE47LT
Published in Issue Year 2026 Volume: 17 Issue: 1

Cite

APA Akdaş, D. N., & Utku, S. (2026). Design and Implementation of a Cloud-Based Manufacturing Execution System for SMEs. AJIT-E: Academic Journal of Information Technology, 17(1), 68-87. https://izlik.org/JA39SE47LT