CMMI SERTİFİKALI BT ŞİRKETLERİNİN ENDÜSTRİ 4.0'A HAZIRLIK DURUMU: KOLAY ÖLÇÜM İÇİN BİR YÖNTEM ÖNERİSİ

Yıl 2024, Sayı: 717, 712 - 740

Hakan Yıldırım , Cihan Ünal

Öz

Bu makale, CMMI (Capability Maturity Model Integration-Yetenek Olgunluk Modeli Entegrasyonu) sertifikalı BT (Bilgi Teknolojileri) şirketlerinin Endüstri 4.0’a hazırlık durumlarını değerlendirmek için karşılaştırmalı ve sistematik bir yöntem önermektedir. CMMI, yazılım süreç yönetimi ve kalite güvencesi için geliştirilmiş uluslararası bir standart olup, günümüzde BT sektörü başta olmak üzere birçok sektörde yaygın olarak kullanılmaktadır. Hâlihazırda on binden fazla BT şirketi CMMI sertifikasına sahiptir ve bu şirketlerin süreç yönetimi konusundaki yetkinlikleri, Endüstri 4.0 gibi dijital dönüşüm süreçlerine ne kadar hazır olduklarını anlamada önemli bir rol oynamaktadır.
CMMI’ın Seviye 4 ve Seviye 5 düzeylerine sahip olan şirketlerin, Endüstri 4.0’ın gerektirdiği yüksek otomasyon, veriye dayalı karar alma süreçleri ve ileri teknoloji entegrasyonuna sahip olma zorunluluğu vurgulanmaktadır. Bu makalede, CMMI kriterlerine dayalı bir Endüstri 4.0 hazırlık seviyesini ölçmek için kullanılan bir çerçeve sunulmaktadır. Bu çerçeve, BT sektörünün ötesine geçerek diğer sektörlere de uygulanabilir potansiyele sahiptir. Önerilen yöntem, CMMI’ın çeşitli alanlara uyarlanmış yaklaşımlarını kullanarak, Endüstri 4.0 için gerekli olan dönüşüm süreçlerini yönetmede rehberlik edebilecek bir model ortaya koymaktadır.
Bu çalışmanın temel amacı, CMMI’ın süreç iyileştirme yeteneklerinden yararlanarak, şirketlerin Endüstri 4.0’a hazırlık seviyelerini objektif bir şekilde ölçebilmektir. Önerilen modelin BT sektöründe başarılı bir şekilde uygulanabileceği, daha sonra diğer sanayi dallarında da genişletilebileceği belirtilmektedir.

Anahtar Kelimeler

Endüstri 4.0, CMMI, süreç yönetimi, kalite güvencesi, dijital dönüşüm

READINESS OF CMMI LEVEL 4-5 CERTIFIED IT COMPANIES FOR INDUSTRY 4.0: A SYSTEMATIC EVALUATION AND MEASUREMENT APPROACH

Öz

This article proposes a comparative and systematic method for evaluating the readiness of CMMI (Capability Maturity Model Integration) certified IT companies for Industry 4.0. CMMI is an international standard developed for software process management and quality assurance and is widely used across many sectors, particularly in the IT industry. Currently, more than ten thousand IT companies hold CMMI certification, and the capabilities of these companies in process management play a critical role in determining their readiness for digital transformation processes like Industry 4.0.
It is emphasized that companies with CMMI Level 4 and Level 5 certifications are required to have high levels of automation, data-driven decision-making processes, and advanced technology integration, which are key requirements for Industry 4.0. The article presents a framework based on CMMI criteria to measure the Industry 4.0 readiness level. This framework is not limited to the IT sector but also has the potential to be applied in other industries. The proposed method utilizes adapted approaches from CMMI to provide guidance in managing the transformation processes necessary for Industry 4.0.
The main objective of this study is to leverage the process improvement capabilities of CMMI to objectively measure companies' readiness for Industry 4.0. While the model is particularly applicable to the IT sector, it is also suggested that it can be expanded to other industries.

Anahtar Kelimeler

Industry 4.0, CMMI, process management, quality assurance, digital transformation

Kaynakça

• Alharthi, A. M., Alghamdi, A. A., Al-Ghaith, W., & McAllen, D. (2022). Metaverse as a digital business ecosystem: The economics and business models of hyper-connectivity. International Journal of Entrepreneurial Behavior & Research, 28(9), 52-73. Doi: https://doi.org/10.1108/IJEBR-12-2021-0984
• Ariffin, K. A. Z.,&Ahmad, F. H. (2021). Indicators for maturity and readiness for digital forensics investigation in era of industrial revolution 4.0. Computers & Security, 105, 102237. Doi: https://doi.org/10.1016/j.cose.2021.102237
• Aslam, F.,Aimin, W., Li, M., & Ur Rehman, K. (2020). Innovation in the Era of IoT and Industry 5.0: Absolute Innovation Management (AIM) Framework. Information, 11(2), 124. Doi: https://doi.org/10.3390/info11020124
• Basl J. (2018) Analysis of Industry 4.0 Readiness Indexes and Maturity Models and Proposal of the Dimension for Enterprise Information Systems. In: Tjoa A.,Raffai M., Doucek P., Novak N. (eds) Research and Practical Issues of Enterprise Information Systems. CONFENIS 2018. Lecture Notes in Business Information Processing, vol 327. Springer, Cham. Doi: https://doi.org/10.1007/978-3-319-99040-8_5
• Brettel. M, Friederichsen. N, Keller. M, Rosenberg. M, (2014). World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering Vol:8, No:1, 2014
• Bottani, E.,&Vignali, G. (2019). Augmented reality technology in the manufacturing industry: A review of the last decade. IISE Transactions, 51(3), 284-310. Doi: https://doi.org/10.1080/24725854.2018.1493244
• CMMI Principles and Values, May 2018, [online] Available: https://msdn.microsoft.com/en-us/library/hh765978(v=vs.120).aspx .
• Endüstri 4.0 Platformu (Bağlantı Tarihi: 05.02.2021) www.endüstri40.com/teknik
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
• Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. Doi: https://doi.org/10.1016/j.future.2013.01.010
• Jiang, P.,Liu, Z., Niu, X., &Zhang, L. (2021). A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting. Energy, 217, 119361. Doi: https://doi.org/10.1016/j.energy.2020.119361
• Leyh C.,Schäffer T., Bley K., Forstenhäusler S. (2017) Assessing the IT and Software Landscapes of Industry 4.0-Enterprises: The Maturity Model SIMMI 4.0. In: Ziemba E. (eds) Information Technology for Management: New Ideas and Real Solutions. ISM 2016, AITM 2016. Lecture Notes in Business Information Processing, vol 277. Springer, Cham. Doi: https://doi.org/10.1007/978-3-319-53076-5_6
• Machado, C. G.,Winroth, M., Carlsson, D., Almström, P., Centerholt, V., & Hallin, M. (2019). Industry 4.0 readiness in manufacturing companies: challenges and enabler stowards increased digitalization. Procedia CIRP, 81, 1113–1118. Doi: https://doi.org/10.1016/j.procir.2019.03.262
• Muelaner, J. (2020). Unsettled Technology Domains for Rapidand Automated Verification of Industry 4.0 Machine Tools. SAE International. Doi: https://doi.org/10.4271/epr2020019
• Raza, U., Kulkarni, P., & Sooriyabandara, M. (2017). Low Power Wide Area Networks: An overview. IEEE Communications Surveys & Tutorials, 19(2), 855-873. https://doi.org/10.1109/COMST.2017.2652320
• Rauschnabel, P. A., Rossmann, A., & tom Dieck, M. C. (2017). An adoption framework for mobile augmented reality games: The case of Pokémon Go. Computers in Human Behavior, 76, 276-286. Doi: https://doi.org/10.1016/j.chb.2017.07.030
• Schumacher, A., Erol, S., &Sihn, W. (2016). A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP, 52, 161–166. Doi: https://doi.org/10.1016/j.procir.2016.07.040
• Schwab, K. (2016). The fourth industrial revolution. World Economic Forum.
• SEI, (Software Engineering Industry) Bağlantı Tarihi, 02.03.2021) www.sei .cmu.edu Capability Maturity Model Integration - CMMI® version 1.3. Software Engineering Institute, Pittsburgh, Pennsylvania, USA
• Stentoft, J.,Jensen, K. W., Philipsen, K., &Haug, A. (2019). Drivers and Barriers for Industry 4.0 Readiness and Practice: A SME Perspective with Empirical Evidence. Proceedings of the 52nd Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences. Doi: https://doi.org/10.24251/hicss.2019.619
• Sony, M.,Amp; Naik, S. (2019, January 29). Key ingredients for evaluating Industry 4.0 readiness for organizations: A literature review. Retrieved February 11, 2021, from https://www.emerald.com/insight/content/doi/10.1108/BIJ-09-2018-0284/full/html
• Tang, J., Sun, D., Liu, S., &Gaudiot, J.-L. (2017). Enabling Deep Learning on IoT Devices. Computer, 50(10), 92-96. Doi: https://doi.org/10.1109/mc.2017.3641648
• Ünal, C., Sungur, C., &Yildirim, H. (2022). Application of the maturity model in industrial corporations. Sustainability, 14(15), 9478.
• Ünal, C. , (2022) Sanayi 4.0 için hazırlık ve olgunluk endeksi modeli geliştirilmesi. Doktora Tezi, Selçuk Üniversitesi
• Yıldırım. H, Doktora tezi, THE APPLICATION OF SHIPBUILDING MANAGERIAL AND OPERATIONAL CAPABILITY ASSESSMENT MODEL (S-MCM) TO TURKISH SHIPYARDS, Piri Reis Üniversitesi, İstanbul, 2018.
• Wood, P. B., & Vickers, D. (2018, March). Anticipated impact of the capability maturity model integration (CMMI®) V2. 0 on aerospace systems safety and security. In 2018 IEEE Aerospace Conference (pp. 1-11). IEEE.
• Zubrow,D., High Maturity Software Engineering Measurementand Analysis, May 2018, [online] Available: https://insights.sei.cmu.edu/sei_blog/2012/02/high-maturity-software-engineering-measurement-and-analysis.html.