Prioritizing Key Factors Affecting Digital Maturity Assessment in the Railway Industry Based on Fuzzy Best-Worst Method

Document Type : Scientific - Research

Authors
Samaneh Moradi 1
Mehrdad Hosseini Shakib 2
Ali Badizadeh 3
1 Phd candidate, Department of Industrial Management, Qa.C., Islamic Azad University, Qazvin, Iran
2 Associate Professor, Department of Industrial Management, Ka.C. , Islamic Azad University, Karaj, Iran
3 Assistant Professor, Department of Industrial Management, Qa.C. , Islamic Azad University, Qazvin, Iran
10.22119/jte.2025.539860.2745
Abstract
The railway transportation industry, as one of the vital transportation infrastructures, is experiencing a profound transformation towards digitalization and utilization of Industry 4.0 technologies. This research was conducted with the aim of prioritizing key factors affecting digital maturity assessment in the railway industry. In this study, first, through an expert panel including 15 specialists in the railway industry and Industry 4.0 technologies, eight main dimensions were identified including advanced Industry 4.0 technologies, cybersecurity and digital risk management, sustainability and environmental compatibility, practical applications and performance improvement, indigenous infrastructure and technical readiness, knowledge management and human capital development, digital economy and new business models, and digitalization challenges and obstacles. Then, using the Fuzzy Best-Worst Method, these dimensions and indicators related to each dimension were prioritized. The results showed that the advanced Industry 4.0 technologies dimension with a weight of 0.285 is in the first priority. In this dimension, the level of artificial intelligence utilization in operational processes with a weight of 0.312 was identified as the most important indicator. The calculated fuzzy consistency indices were less than 0.10, indicating the validity and reliability of the results. The findings of this research can be used as guidance for managers and policymakers in the railway industry in resource allocation and strategic planning for digital transformation.
Keywords
Digital maturity
Railway industry
Industry 4.0
Fuzzy Best-Worst Method
Prioritization
Subjects
- Abbasnejad, B., Soltani, S., Karamoozian, A., & Gu, N. (2024). A systematic literature review on the integration of Industry 4.0 technologies in sustainability improvement of transportation construction projects: state-of-the-art and future directions. Smart and Sustainable Built Environment.
https://doi.org/10.1108/SA SBE-11-2023-0335
 
- Ale, F., Daniyan, I., Aderoba, O., & Adediran, A. A. (2024). An Artificial Intelligence-based Integrated Framework for Lean and Smart Manufacturing: A Case Study of the Rail Industry. In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) (pp. 1-10). IEEE.
https://doi.org/10.1109/SEB4SDG60871.2024.10629752
 
- Awodele, I. A., Mewomo, M. C., Municio, A. M. G., Chan, A. P., Darko, A., Taiwo, R., ... & Awodele, O. A. (2024). Awareness, adoption readiness and challenges of railway 4.0 technologies in a developing economy. Heliyon, 10(4).
https://doi.org/10.1016/j.heliyon.2024.e25934
 
- Azambuja, A. J. G. de, Giese, T., Schützer, K., & Almeida, V. R. (2024). Digital twins in Industry 4.0 – Opportunities and challenges related to cyber security. Procedia CIRP, 121, 25-30.
https://doi.org/10.1016/j.procir.2023 .09.225
 
- Bianchi, G., Fanelli, C., Freddi, F., Giuliani, F., & La Placa, A. (2025). Systematic review railway infrastructure monitoring: From classic techniques to predictive maintenance. Advances in Mechanical Engineering, 17(1), 16878132241285631.
https://doi.org/10.1177/16878132241285631
 
- Bortolini, M., Calabrese, F., Galizia, F. G., Mora, C., & Ventura, V. (2022). Industry 4.0 technologies: a cross-sector industry-based analysis. In Sustainable Design and Manufacturing: Proceedings of the 8th International Conference on Sustainable Design and Manufacturing (KES-SDM 2021) (pp. 140-148). Springer Singapore.
https://doi.org/1 0.1007/978-981-16-6128-0_14
 
- Brezavšček, A., & Baggia, A. (2025). Recent Trends in Information and Cyber Security Maturity Assessment: A Systematic Literature Review. Systems, 13(1), 52.
https://doi.org/10.3390/systems13010052
 
- Bustos, A., Rubio, H., Soriano-Heras, E., & Castejon, C. (2021). Methodology for the integration of a high-speed train in maintenance 4.0. Journal of Computational Design and Engineering, 8(6), 1605-1621.
https://doi.org/10.1093/jcde/qwab064
 
- Chellaswamy, C., Geetha, T. S., Vanathi, A., & Venkatachalam, K. (2020). An IoT based rail track condition monitoring and derailment prevention system. International Journal of RF Technologies, 11(2), 81-107.
https://doi.org/10.3233/RFT-190210
 
- Chowdhury, S.; Alzarrad, A. (2023). Applications of Text Mining in the Transportation Infrastructure Sector: A Review. Information, 14, 201.
https://doi.org/10.3390/info14040201
 
- Dalewska, M., & Mrugalska, B. (2025). Innovative Transport: Environmental, Social, and Economic Aspects. In Human Perspectives of Industry 4.0 Organizations (pp. 224-235). CRC Press.
 
- Dong, K., Romanov, I., McLellan, C., & Esen, A. (2022). Recent text-based research and applications in railways: A critical review and future trends. Engineering Applications of Artificial Intelligence 116 (2022) 105435.
https://doi.org/10.1016/j.engappai.2022.105435
 
- Errandonea, I., Goya, J., Alvarado, U., Beltrán, S., & Arrizabalaga, S. (2021, November). IoT approach for intelligent data acquisition for enabling digital twins in the railway sector. In 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) (pp. 164-168). IEEE.
https://doi.org/10.1109/ISCSIC54682.2021.00039
 
- Francisca, R., Codina, E., & Montero, L. (2022). A combined and robust modal-split/traffic assignment model for rail and road freight transport. European Journal of Operational Research, 303(3).
https://doi.org/10.1016/j.ejor.2022.03.008
 
- Franz, M. L. C., Ayala, N. F., & Larranaga, A. M. (2024). Industry 4.0 for passenger railway companies: A maturity model proposal for technology management. Journal of Rail Transport Planning & Management, 32, 100480.
https://doi.org/10.1016/j.jrtpm.2024.100480
 
- Galizia, F. G., Bortolini, M., & Calabrese, F. (2023). A cross‐sectorial review of industrial best practices and case histories on Industry 4.0 technologies. Systems Engineering, 26(6), 908-924.
https://doi. org /10.1002/sys.21697
 
- Gao, X., Zhang, Y., & Peng, J. (2022). NDE 4.0 in railway industry. In Handbook of Nondestructive Evaluation 4.0 (pp. 951-977). Cham: Springer International Publishing.
https://doi.org/10.1007/978-3-030-3206-6_13
 
- Gerhátová, Z., Zitrický, V., & Klapita, V. (2021). Industry 4.0 implementation options in railway transport. Transportation Research Procedia, 53, 23-30.
https://doi.org/10.1016/j.trpro.2021.02.003
 
- Gerhátová, Z., Zitrický, V., & Gašparik, J. (2021). Analysis of Industry 4.0 elements in the transport process at the entrance of the train from Ukraine to Slovakia. Transportation Research Procedia, 55, 165-171.
https://doi.org/1 0.10 16/j.trpro.2021.06.018
 
- Harrod, S. S. (2025). Railway Signal Digitalization with the European Rail Traffic Management System and Positive Train Control: Industry 4.0 Expectations and Reality. Transportation Research Record, 03611981241265841.
https://doi.org/10.1177/03611981241265841
 
- Kljaić, Z., Pavković, D., Cipek, M., Trstenjak, M., Mlinarić, T. J., & Nikšić, M. (2023). An Overview of Current Challenges and Emerging Technologies to Facilitate Increased Energy Efficiency, Safety, and Sustainability of Railway Transport. Future Internet, 15(11), 347.
https://doi.org/10.3390/fi15110347
 
- Koul, P. (2025). Green manufacturing in the age of smart technology: A comprehensive review of sustainable practices and digital innovations. Journal of Materials and Manufacturing, 4(1), 1-20.
https://doi.org/10.5281/zenodo.1458 6656
- Kumar, P., Balachandra, P., & Garza-Reyes, J. A. (2023). Industry 4.0 maturity and readiness assessment: An empirical validation using confirmatory composite analysis. Production Planning & Control, 35(1), 1-18.
https://doi.org/10.1080/09537287.2023.2210545
 
- Laiton-Bonadiez, C., Branch-Bedoya, J. W., Zapata-Cortes, J., Paipa-Sanabria, E., & Arango-Serna, M. (2022). Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review. Sensors, 22(7), 2491.
https://doi.org/10.3390/s22072491
 
- López-Aguilar, P., Batista, E., Martínez-Ballesté, A., & Solanas, A. (2022). Information security and privacy in railway transportation: A systematic review. Sensors, 22(20), 7698.
https://doi.org/10.3390/s22207698
 
- Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
https://doi.org/ 10.1016/ j.omega.2014.11.009
 
- Sedaju, A., Qamari, I. N., & Tjahjono, H. K. (2024). Industry 4.0 Human Machine Integration in Rail Transportation Manufacture: Narrative Approach for Employee and Leader Value. In Strengthening Sustainable Digitalization of Asian Economy and Society (pp. 302-330). IGI Global.
https://doi.org/10.4018/979-8-3693-942-0.ch017
 
- Senna, P. P., Barros, A. C., Bonnin Roca, J., & Azevedo, A. (2023). Development of a digital maturity model for Industry 4.0 based on the technology-organization-environment framework. Computers & Industrial Engineering, 185, 109645.
https://doi.org/10.1016 /j.ci e. 2023.109645
 
- Singh, P., Elmi, Z., Meriga, V. K., Pasha, J., & Dulebenets, M. A. (2022). Internet of Things for sustainable railway transportation: Past, present, and future. Cleaner Logistics and Supply Chain, 4, 100065.
https://doi.org/10.1016/j.clscn.2022.100065
 
- Voronko, I. (2024). The security of IoT systems in railway transport. Transport Systems and Technologies, (43), 90-99.
https://doi.org/10.32703/2617-9059-2024-43-7
 
- Wee, X. B., Herrera, M., Hadjidemetriou, G. M., & Parlikad, A. K. (2022). Simulation and criticality assessment of urban rail and interdependent infrastructure networks. Transportation Research Record: Journal of the Transportation Research Board, 03611981221103594.
- https://doi.org/10.1177/03611981221103594
 
- Zamany, A., Khamseh, A. and Iranbanfard, S. (2023). Technology Transfer in the Industry 5.0 Era: An Integrated Model of Artificial Intelligence and Human Factors. Innovation Management Journal, 12(4), 111-140.
https://doi.org/10.22034/imj.2024.450323.2803 (In Persian)
 
- Zhao, Y., Yu, X., Chen, M., Zhang, M., Chen, Y., Niu, X., ... & Li, W. J. (2020). Continuous monitoring of train parameters using IoT sensor and edge computing. IEEE Sensors Journal, 21(14), 15458-15468.
https://doi.org/10.1109/JSEN.2020.3026643.
Quarterly Journal of Transportation Engineering
Volume 17, Issue 3 - Serial Number 68
Winter 2026
Pages 5615-5636

  • Receive Date 07 August 2025
  • Revise Date 06 September 2025
  • Accept Date 27 September 2025