Predicting Industry Maturity Index Using Machine Learning Methods

Year 2025, Volume: 9 Issue: 1, 293 - 313, 30.06.2025

Ayhan Doğan , Cihan Ünal

https://doi.org/10.26650/acin.1626593

Abstract

Industry 4.0 has become a widely adopted concept in recent years. Maturity and readiness models are commonly used to assess the current state of industrial organizations in relation to Industry 4.0. Companies’ maturity levels and index scores are typically determined through structured surveys. However, due to their complexity, time consumption, and high cost, many enterprises lack formally assessed maturity index (MI) scores. To address this limitation, this study initially employed survey data to evaluate the accuracy of the proposed machine learning (ML) framework. A 58-question survey was conducted to calculate the MI scores of the companies. These scores were then used as reference values to be predicted based on five easily accessible enterprise-level variables: company age, industry type, ownership structure, number of employees, and annual turnover. This approach tested whether MI could be accurately predicted without relying on lengthy survey processes, using only a minimal set of key enterprise attributes. The results of this study demonstrate that MI can be estimated successfully using ML techniques without the need for answering long and complex surveys. To reduce the burdens associated with conventional survey-based methods, this study employed multiple ML algorithms, including Support Vector Machines (SVM), Gaussian Process Regression (GPR), Linear Regression (LR), Regression Trees (RT), and Ensemble Tree-based models, and advanced boosting-based methods, such as extreme gradient boosting (XGB) and Light Gradient Boosting Machine (LGBM). The findings demonstrate that the proposed model predicts MI with high accuracy and offers a practical and scalable alternative for enterprises seeking to assess their Industry 4.0 readiness.

Keywords

Machine learning , Industry 4.0 , Maturity model , Maturity index

References

• Akdil, K. Y., Ustundag, A., & Cevikcan, E. (2018). Maturity and readiness model for industry 4.0 strategy. Industry 40: Managing the digital transformation. Springer. pp 61-94 google scholar
• Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019). Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors, 20, 109. google scholar
• Aslan, Y., Yavasca, S., & Yasar, C. (2011). Long-term electric peak load forecasting of Kutahya using different approaches. International Journal on Technical and Physical Problems of Engineering, 3, 87-91. google scholar
• Ateş, B. (2020). Gemi yapılarında gerilme yığılması öngörülerinin kaba ağ yapısı ve makine öğrenmesi ile gerçekleştirilmesi. Fen Bilimleri Enstitüsü google scholar
• Ayhan, S., & Erdoğmuş, Ş. (2014). Destek vektör makineleriyle sınıflandırma problemlerinin çözümü için çekirdek fonksiyonu seçimi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 9, 175-201. google scholar
• Basl, J., & Doucek, P. (2019). A metamodel for evaluating enterprise readiness in the context of Industry 4.0. Information, 10, 89. google scholar
• Bayazit, M., & Oğuz, B. (1994). Mühendisler için istatistik. Birsen Yayınevi, İstanbul, 197s. google scholar
• Becker, B. G., Klein, T., Wachinger, C., & Initiative AsDN (2018). Gaussian process uncertainty in age estimation as a measure of brain abnormality. NeuroImage, 175, 246-258. google scholar
• Bibby, L., & Dehe, B. (2018). Defining and assessing industry 4.0 maturity levels–case of the defense sector. Production Planning & Control, 29, 1030-1043. google scholar
• Boran, P. D. S. (2021). Makine öğrenmesi yöntemleri kullanılarak kısa dönem rüzgar gücü tahmini. Sakarya Üniversitesi google scholar
• Botha, A. P. (2018). Rapidly arriving futures: future readiness for Industry 4.0. South African Journal of Industrial Engineering, 29, 148-160. google scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140. google scholar
• Breiman, L. (2001). Using iterated bagging to debian regressions. Machine Learning, 45, 261-277. google scholar
• Brik, B., Bettayeb, B., Sahnoun, M. H., & Duval, F. (2019). Toward predicting system disruption in industry 4.0: Machine learning-based approach. Procedia Computer Science, 151, 667-674. google scholar
• Candanedo, I. S., Nieves, E. H., González, S. R., Martín, M. T. S., & Briones, A. G. (2018). Machine learning predictive model for industry 4.0. Knowledge Management in Organizations: 13th International Conference, KMO 2018, Žilina, Slovakia, August 6–10, 2018, Proceedings 13. Springer. pp 501-510 google scholar
• Canetta, L., Barni, A., & Montini, E. (2018). Development of a digitalization maturity model for the manufacturing sector. 2018 ieee international conference on engineering, technology and innovation (ICE/ITMC). IEEE. pp 1-7 google scholar
• Carvalho, N., Chaim, O., Cazarini, E., & Gerolamo, M. (2018). Manufacturing in the fourth industrial revolution: A positive prospect in sustainable manufacturing. Procedia Manufacturing, 21, 671-678. google scholar
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785 google scholar
• Chonsawat, N., & Sopadang, A. (2020). Defining SMEs’ 4.0 readiness indicators. Applied Sciences, 10, 8998. google scholar
• Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297. google scholar
• Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, google scholar
• Çınar, Z. M., Zeeshan, Q., & Korhan, O. (2021). A framework for industry 4.0 readiness and maturity of smart manufacturing enterprises: a case study. Sustainability, 13, 6659. google scholar
• Da Silva, V. L., Kovaleski, J. L., Pagani, R. N., Silva, J. D. M., & Corsi, A. (2020). Implementation of Industry 4.0 concept in companies: Empirical evidence. International Journal of Computer Integrated Manufacturing, 33, 325-342. google scholar
• Doğan, A. (2024). Prediction of major earthquakes that may affect İzmir using machine learning methods. Yerbilimleri, 45, 93-106. doi:10.17824/yerbilimleri.1402618 google scholar
• Doğan, A., Korkmaz, M., & Kirmaci, V. (2023). Estimation of Ranque-Hilsch vortex tube performance using machine learning techniques. International Journal of Refrigeration, 150, 77-88. https://doi.org/10.1016/j.ijrefrig.2023.01.021 google scholar
• Erol, S., Schumacher, A., & Sihn, W. (2016). Strategic guidance toward Industry 4.0–a three-stage process model. International conference on competitive manufacturing. pp 495-501 google scholar
• Finance, A. (2015). Industry 4.0 Challenges and solutions for the digital transformation and use of exponential technologies. Finance, audit tax consulting corporate: Zurich, Swiss:1-12. google scholar
• Geissbauer, R., Vedso, J., & Schrauf, S. (2016). Industry 4.0: Building the digital enterprise. google scholar
• Gerlitz, L. (2016). Design management as a domain of smart and sustainable enterprise: Business modeling for innovation and smart growth in Industry 4.0. Entrepreneurship and Sustainability, 3, 244. google scholar
• Gökalp, E., Şener, U., & Eren, P. E. (2017). Development of an assessment model for industry 4.0: industry 4.0-MM. International Conference on Software Process Improvement and Capability Determination. Springer. pp 128-142 google scholar
• Haber, R. E., Juanes, C., del Toro, R., & Beruvides, G. (2015). Artificial cognitive control with self-x capabilities: A case study of a micromanufacturing process. Computers in Industry, 74, 135-150. google scholar
• Hamidi, S. R., Aziz, A. A., Shuhidan, S. M., Aziz, A. A., & Mokhsin, M. (2018). SMEs maturity model assessment of IR4. 0 digital transformation. google scholar
• International Conference on Kansei Engineering & Emotion Research. Springer. pp 721-732 google scholar Haykin, S. (1999). Neural networks: A guided tour. Soft computing and intelligent systems: theory and applications 71. google scholar
• Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and Their Applications, 13, 18-28. google scholar
• Heo, Y., & Zavala, V. M. (2012). Gaussian process modeling for measurement and verification of building energy savings. Energy and Buildings, 53, 7-18. google scholar
• Hizam-Hanafiah, M., Soomro, M. A., & Abdullah, N. L. (2020). Industry 4.0 readiness models: a systematic literature review of model dimensions. Information, 11, 364. google scholar
• Horvat, D., Stahlecker, T., Zenker, A., Lerch, C., & Mladineo, M. (2018). A conceptual approach to analyzing manufacturing companies’ profiles concerning Industry 4.0 in emerging economies. Procedia Manufacturing, 17, 419-426. google scholar
• Horváth, D., & Szabó, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational and small- and medium-sized companies have equal opportunities? Technological Forecasting and Social Change, 146, 119-132. google scholar
• Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics & Proteomics, 15, 41-51. google scholar
• Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57, 829-846. google scholar
• Jalil, N. A., Hwang, H. J., & Dawi, N. M. (2019). Machines learning trends, perspectives and prospects in education sector. Proceedings of the 2019 3rd International Conference on Education and Multimedia Technology, 201-205. google scholar
• Jesus, C. D., & Lima, R. M. (2020). Literature search of key factors for developing generic and specific maturity models for Industry 4.0. Applied Sciences, 10, 5825. google scholar
• Kalayci, T. E. (2018) Kimlik hırsızı web sitelerinin sınıflandırılması için makine öğrenmesi yöntemlerinin karşılaştırılması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24, 870-878. google scholar
• Kannan, S. M., Suri, K., Cadavid, J., Barosan, I., Van Den Brand, M., Alferez, M., & Gerard, S. (2017). Toward industry 4.0: Gap analysis between current automotive MES and industry standards using model-based requirement engineering. 2017 IEEE International Conference on Software Architecture Workshops (ICSAW). IEEE. pp 29-35 google scholar
• Katuwal, G. J., Baum, S. A., & Michael, A. M. (2018). Early brain imaging can predict autism: Application of machine learning to a clinical imaging archive. BioRxiv:471169. google scholar
• Kavzoğlu, T., & Çölkesen, İ. (2010). Destek vektör makineleri ile uydu görüntülerinin sınıflandırılmasında kernel fonksiyonlarının etkilerinin incelenmesi. Harita Dergisi, 144, 73-82. google scholar
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30. google scholar
• Kuo, C. J., Ting, K. C., Chen, Y. C., Yang, D. L., & Chen, H. M. (2017). Automatic machine status prediction in the era of Industry 4.0: Case study of machines in a spring factory. Journal of Systems Architecture, 81, 44-53. google scholar
• Lee, C., & Lim, C. (2021). From technological development to social advance: A review of Industry 4.0 through machine learning. Technological Forecasting and Social Change, 167, 120653. google scholar
• Leyh, C., Bley, K., Schäffer, T., & Forstenhäusler, S. (2016). SIMMI 4.0-a maturity model for classifying the enterprise-wide it and software landscape focusing on Industry 4.0. 2016 federated conference on computer science and information systems (fedcsis). IEEE. pp 1297-1302 google scholar
• Liu, K., Hu, X., Wei, Z., Li, Y., & Jiang, Y. (2019). Modified Gaussian process regression models for cyclic capacity prediction of lithium-ion batteries. IEEE Transactions on Transportation Electrification, 5, 1225-1236. google scholar
• Luu, Q. H., Lau, M. F., Ng, S. P., & Chen, T. Y. (2021). Testing multiple linear regression systems with metamorphic testing. Journal of Systems and Software, 182, 111062. https://doi.org/10.1016/j.jss.2021.111062 google scholar
• Maimon, O., & Rokach, L. (2005). Data mining and knowledge discovery handbook. google scholar
• Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers and Industrial Engineering, 127, 925-953. google scholar
• Mehmet, A., & Doğansoy, G. A. (2022). Makine öğrenmesi ve derin öğrenme yöntemleri kullanılarak e-perakende sektörüne yönelik talep tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 37, 1325-1340. google scholar
• Memnun, D., & Kalaycı, Ş. (2006). SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri. Ankara: Asil Yayın Dağıtım. İlköğretim Online 12. google scholar
• Mendeş, M., & Akkartal, E. (2009). Regression tree analysis for predicting slaughter weight in broilers. Italian Journal of Animal Science, 8, 615-624. google scholar
• Methavitakul, B., & Santiteerakul, S. (2018). Analysis of key dimension and sub-dimension for Supply Chian of Industry to fourth Industry Performance Measurement. 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). IEEE. pp 191-195 google scholar
• Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small- and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194-214. google scholar
• Muñoz, L. P., Hagemann, M., Palandella, L., & Cagliari, P. (2023). Maturity SCAN 4.0: An analysis and discussion of results from scanning of digital capabilities adopted by Argentinian CPG companies. ESSN: 2701-6277, 854-863. google scholar
• Müller, J. M., Voigt, K. I. (2018). The impact of industry 4.0 on supply chains in engineer-to-order industries-an exploratory case study. IFAC-PapersOnLine, 51, 122-127. google scholar
• Opitz, D., & Maclin, R. (1999). Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11, 169-198. google scholar
• Özen, N. S., Saraç, S., & Koyuncu, M. (2021). COVID-19 Vakalarının Makine Öğrenmesi Algoritmaları ile Tahmini: Amerika Birleşik Devletleri Örneği. Avrupa Bilim ve Teknoloji Dergisi, 134-139. google scholar
• Pessl, E., Sorko, S. R., & Mayer, B. (2017). Roadmap Industry 4.0–implementation guideline for enterprises. International Journal of Science, Technology and Society, 5, 193-202. google scholar
• Rahamaddulla, S. R. B., Leman, Z., Baharudin, B. H. T. B., & Ahmad, S. A. (2021). Conceptualizing smart manufacturing readiness-maturity model for small and medium enterprise (sme) in malaysia. Sustainability, 13, 9793. google scholar
• Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. Taylor and Francis. pp 4773-4778. google scholar
• Rais, R. B. (2021). Sharing the Pie The Fourth Industrial Revolution and the Sharing/Platform Economy: Distributive justice implications. Beyond Free Market. Routledge. pp 125-140 google scholar
• Rasmussen, C. E., & Williams, C. (2006). Gaussian processes for machine learning, vol. 1. MIT press Cambridge MA. google scholar
• Rauch, E., Dallasega, P., & Unterhofer, M. (2019). Requirements and barriers for introducing smart manufacturing in small- and mediumsized enterprises. IEEE Engineering Management Review, 47, 87-94. google scholar
• Rong, S., & Bao-Wen, Z. (2018). The research of regression model in machine learning field. MATEC Web of Conferences. EDP Sciences. 01033, 2019. google scholar
• Saad, S. M., Bahadori, R., & Jafarnejad, H. (2021). The smart SME technology readiness assessment methodology in the context of industry 4.0. Journal of Manufacturing Technology Management. google scholar
• Sajjad, A., Ahmad, W., Hussain, S., Chuddher, B. A., Sajid, M., Jahanjaib, M., Ali, M. K., & Jawad, M. (2024). Assessment by Lean Modified Manufacturing Maturity Model for Industry 4.0: A Case Study of Pakistan's Manufacturing Sector. IEEE Transactions on Engineering Management, 71, 6420-6434. https://doi.org/10.1109/TEM.2023.3259005 google scholar
• Schaupp, E., Abele, E., & Metternich, J. (2017). Potentials of digitalization in tool management. Procedia CIRP, 63, 144-149. google scholar
• Schiltz, F., Masci, C., Agasisti, T., & Horn, D. (2018). Using regression tree ensembles to model interaction effects: a graphical approach. Applied Economics, 50, 6341-6354. google scholar
• Schölkopf, B., Williamson, R. C., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in neural information processing systems 12. google scholar
• 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. google scholar
• Shoaran, M., Haghi, B. A., Taghavi, M., Farivar, M., & Emami-Neyestanak, A. (2018). Energy-efficient classification for resource-constrained biomedical applications. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 8, 693-707. google scholar
• Skalli, D., Charkaoui, A., & Cherrafi, A. (2023). Design of Industry 4.0 Maturity Model and Readiness Assessment Under the Digital Transformation Toward the Implementation of LSS4.0. In T. Masrour, I. El Hassani, and N. Barka, Artificial Intelligence and Industrial Applications Cham. google scholar
• Soman, K., Loganathan, R., & Ajay, V. (2009). Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., New Delhi. google scholar
• Stentoft, J., Adsbøll Wickstrøm, K., Philipsen, K., & Haug, A. (2021). Drivers and barriers for Industry 4.0 readiness and practice: empirical evidence from small and medium-sized manufacturers. Production Planning & Control, 32, 811-828. google scholar
• Temel, G. O. (2004). Sınıflama ve Regresyon Ağaçları. Yayımlanmamış Yüksek Lisans Tezi, Mersin Üniversitesi, Sağlık Bilimleri Enstitüsü, Bioistatistik Anabilim Dalı, Mersin 11:23-24. google scholar
• Tolun, S. (2008). Destek vektör makineleri: Banka başarısızlığının tahmini üzerine bir uygulama. İktisadî Araştırmalar Vakfı google scholar
• Ünal, C., Sungur, C., & Yildirim, H. (2022). Application of the maturity model in industrial corporations. Sustainability, 14, 9478. google scholar
• Valentin, B. A. (2017). Methods Of Assessment And Training Of A Company Toward The Enterprise 4.0. Annals of DAAAM & Proceedings 28. google scholar
• Városiné Demeter, K., Losonci, D., Szász, L., & Rácz, B. G. (2018). Assessing Industry 4.0 readiness: a multi-country industry level analysis. google scholar
• Vazire, S. (2018). Implications of the credibility revolution for productivity, creativity, and progress. Perspectives on Psychological Science, 13, 411-417. google scholar
• Wank, A., Adolph, S., Anokhin, O., Arndt, A., Anderl, R., & Metternich, J. (2016). Using a learning factory approach to transfer Industrie 4.0 approaches to small-and medium-sized enterprises. Procedia Cirp, 54, 89-94. google scholar
• Wei, J., Chu, X., Sun, X. Y., Xu, K., Deng, H. X., Chen, J., Wei, Z., & Lei, M. (2019). Machine learning in materials science. InfoMat, 1, 338-358. google scholar
• Yesiloglu-Gultekin, N., & Dogan, A. (2024). Estimation of the elastic modulus of basaltic rocks using machine learning methods. Earth Science Informatics, doi:10.1007/s12145-024-01472-7 google scholar
• Yücel, Y. B. (2017). Yaşam memnuniyetini etkileyen faktörlerin sınıflama ve regresyon ağacı ile belirlenmesi. Fen Bilimleri Enstitüsü. google scholar
• Zhou, G., Zhang, J., Su, J., Shen, D., & Tan, C. (2004). Recognizing names in biomedical texts: a machine learning approach. Bioinformatics, 20, 1178-1190. google scholar