MAINTENANCE STRATEGY SELECTION OF HYDRAULIC SYSTEMS IN THE STEEL INDUSTRY: A DESIGN SCIENCE RESEARCH APPROACH
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Published
Nov 11, 2025
Nuno M. M. Torre
Valerio A. P. Salomon
Fernando A. S. Marins
https://orcid.org/0000-0003-4260-2686
Valerio A. P. Salomon
Fernando A. S. Marins
Abstract
The steel industry is a major global player in the world economy and significantly contributes to a country’s development. Maintenance is indispensable for the productivity of steel industry assets. The growing use of high-precision operations in these organizations makes hydraulic systems a critical concern. Multi-criteria decision-making (MCDM) methods can facilitate decision-making, particularly with decisions about the best maintenance policies/strategies to be employed. The Analytic Hierarchy Process (AHP) is a consolidated and appropriate method for dealing with multiple factors and uncertainty. This research proposes a model to support decision-making for selecting a maintenance strategy for hydraulic systems in steel plants. The development of this model followed the Design Science Research (DSR) methodology, which has five stages. The main scientific contribution of this research is to demonstrate that the AHP allows a landscape with a qualitative approach regarding the maintenance strategy selection of hydraulic systems in the steel industry, which enables the development of a hierarchical framework that incorporates four maintenance strategies, criteria, and sub-criteria identified in the current literature. The criteria of cost, safety, reliability, quality, and feasibility were examined to determine the best maintenance strategy to be applied. Predictive maintenance was selected as the priority strategy, while safety was the criterion with the highest added value. The sensitivity analysis confirmed the robustness of the framework, showing that classifications remained stable even when the weights of the criteria varied.
How to Cite
M. M. Torre, N., A. P. Salomon, V., & A. S. Marins, F. (2025). MAINTENANCE STRATEGY SELECTION OF HYDRAULIC SYSTEMS IN THE STEEL INDUSTRY: A DESIGN SCIENCE RESEARCH APPROACH. International Journal of the Analytic Hierarchy Process, 17(3). https://doi.org/10.13033/ijahp.v17i3.1304
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Keywords
steel industry, maintenance, hydraulic systems, design science research, framework, AHP
References
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Bauer, M. W., Gaskell, G. (2017). Pesquisa qualitativa com texto, imagem e som: um manual prático. Editora Vozes Limitada.
Behnia, F., Ahmadabadi, H. Z., Schuelke-Leech, B. A., & Mirhassani, M. (2023). Developing a fuzzy optimized model for selecting a maintenance strategy in the paper industry: An integrated FGP-ANP-FMEA approach. Expert Systems with Applications, 232, 120899. https://doi.org/10.1016/j.eswa.2023.120899
Canco, I., Kruja, D., & Iancu, T. (2021). AHP, a reliable method for quality decision making: A case study in business. Sustainability, 13(24), 13932. https://doi.org/10.3390/ su132413932
Carpitella, S., Certa, A., Izquierdo, J., & La Fata, C. M. (2018). A combined multi-criteria approach to support FMECA analyses: A real-world case. Reliability Engineering & System Safety, 169, 394–402. https://doi.org/10.1016/j.ress.2017.09.017
Carpitella, S., Mzougui, I., Benítez, J., Carpitella, F., Certa, A., Izquierdo, J., & La Cascia, M. (2021). A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm. Reliability Engineering & System Safety, 205, 107265. https://doi.org/10.1016/j.ress.2020.107265
Carstensen, A. K., & Bernhard, J. (2019). Design science research–a powerful tool for improving methods in engineering education research. European Journal of Engineering Education, 44(1-2), 85–102. https://doi.org/10.1080/03043797.2018.1498459
Chaudhuri, A., Gerlich, H. A., Jayaram, J., Ghadge, A., Shack, J., Brix, B. H., ... & Ulriksen, N. (2021). Selecting spare parts suitable for additive manufacturing: a design science approach. Production Planning & Control, 32(8), 670–687. https://doi.org/10.1080/09537287.2020.1751890
Chen, L., Feng, H., & Xie, Z. (2016). Generalized thermodynamic optimization for iron and steel production processes: Theoretical exploration and application cases. Entropy, 18(10), 353. https://doi.org/10.3390/e18100353
Dai, J., Tang, J., Huang, S., & Wang, Y. (2019). Signal-based intelligent hydraulic fault diagnosis methods: Review and prospects. Chinese Journal of Mechanical Engineering, 32(1), 75. https://doi.org/10.1186/s10033-019-0388-9
Depczyński, R., Secka, J., Cheba, K., D’Alessandro, C., & Szopik-Depczyńska, K. (2023). Decision-making approach in sustainability assessment in steel manufacturing companies—systematic literature review. Sustainability, 15(15), 11614. https://doi.org/10.3390/su151511614
Di Bona, G., Cesarotti, V., Arcese, G., & Gallo, T. (2021). Implementation of Industry 4.0 technology: New opportunities and challenges for maintenance strategy. Procedia Computer Science, 180, 424–429. https://doi.org/10.1016/j.procs.2021.01.258
Dindorf, R., & Wos, P. (2022). A case study of a hydraulic servo drive flexibly connected to a boom manipulator excited by the cyclic impact force generated by a hydraulic rock breaker. IEEE Access, 10, 7734-7752. https://doi.org/10.1109/ACCESS.2022.3143257
Dresch, A., Lacerda, D. P., Antunes Jr, J. A. V., Dresch, A., Lacerda, D. P., & Antunes, J. A. V. (2015). Design science research. Springer International Publishing.
Ge, Y., Xiao, M., Yang, Z., Zhang, L., Hu, Z., & Feng, D. (2017). An integrated logarithmic fuzzy preference programming based methodology for optimum maintenance strategies selection. Applied Soft Computing, 60, 591–601. https://doi.org/10.1016/j.asoc.2017.07.021
Goecks, L. S., Souza, M. D., Librelato, T. P., & Trento, L. R. (2021). Design Science Research in practice: review of applications in Industrial Engineering. Gestão & Produção, 28(4), e5811. https://doi.org/10.1590/1806-9649-2021v28e5811
He, Y., Han, X., Gu, C., & Chen, Z. (2018). Cost-oriented predictive maintenance based on mission reliability state for cyber manufacturing systems. Advances in Mechanical Engineering, 10(1), 1687814017751467. https://doi.org/10.1177/1687814017751467
International Organization for Standardization. (2022). Hydraulic fluid power — General rules and safety requirements for systems and their components (ISO Standard No. 4413:2022)
International Organization for Standardization. (2015). Quality management systems — Requirements (ISO Standard No. 9001:2015).
Jocelyn, S., Chinniah, Y., & Ouali, M. S. (2016). Contribution of dynamic experience feedback to the quantitative estimation of risks for preventing accidents: A proposed methodology for machinery safety. Safety Science, 88, 64–75. https://doi.org/10.1016/j.ssci.2016.04.024
Kannan, A. K., Balamurugan, S. A. A., & Sasikala, S. (2021). A customized metaheuristic approaches for improving supplier selection in intelligent decision making. IEEE Access, 9, 56228-56239. https://doi.org/10.1109/ACCESS.2021.3071454
Khan, K., Sohaib, M., Rashid, A., Ali, S., Akbar, H., Basit, A., & Ahmad. T. (2021). Recent trends and challenges in predictive maintenance of aircraft’s engine and hydraulic system. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43, 1–17. https://doi.org/10.1007/s40430-021-03121-2
Khan, S., Farnsworth, M., McWilliam, R., & Erkoyuncu, J. (2020). On the requirements of digital twin-driven autonomous maintenance. Annual Reviews in Control, 50, 13–28. https://doi.org/10.1016/j.arcontrol.2020.08.003
Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: anatomy of a research project. European Journal of Information Systems, 17(5), 489–504. https://doi.org/10.1057/ejis.2008.40
Kuechler, W., & Vaishnavi, V. (2012). A framework for theory development in design science research: multiple perspectives. Journal of the Association for Information systems, 13(6), 3. https://doi.org/10.17705/1jais.00300
Lacerda, D. P., Dresch, A., Proença, A., & Antunes Júnior, J. A. V. (2013). Design Science Research: A research method to production engineering. Gestão & produção, 20, 741–761. https://doi.org/10.1590/S0104-530X2013005000014
Lopes, I. D. S., Figueiredo, M., & Sá, V. (2020). Criticality evaluation to support maintenance management of manufacturing systems. International Journal of Industrial Engineering and Management, 11(1), 3–18. https://doi.org/10.24867/IJIEM-2020-1-248
Mattioli, J., Perico, P., & Robic, P. O. (2020, September). Improve total production maintenance with artificial intelligence. In 2020 Third International Conference on Artificial Intelligence for Industries (AI4I) (pp. 56-59). IEEE. https://doi.org/10.1109/AI4I49448.2020.00019
Manson, N. J. (2006). Is operations research really research?. Orion, 22(2), 155–180.
Mirhosseini, M., & Keynia, F. (2021). Asset management and maintenance programming for power distribution systems: A review. IET Generation, Transmission & Distribution, 15(16), 2287–2297. https://doi.org/10.1049/gtd2.12177
Noura, H. N., Chu, T. Allal Z., Salman, O., & Chahine, K. (2024). A comparative study of ensemble methods and multi-output classifiers for predictive maintenance of hydraulic systems. Results in Engineering, 24, 102900. https://doi.org/10.1016/j.rineng.2024.102900
Özcan, E., Yumuşak, R., & Eren, T. (2021). A novel approach to optimize the maintenance strategies: a case in the hydroelectric power plant. Eksploatacja i Niezawodność, 23(2), 324–337. http://doi.org/10.17531/ein.2021.2.12
Ohta, R. (2018). Selection of industrial maintenance strategy: Classical AHP and fuzzy AHP applications. International Journal of the Analytic Hierarchy Process, 10(2), 254–265. https://doi.org/10.13033/ijahp.v10i2.551
Patil, A., Soni, G., Prakash, A. and Karwasra, K. (2022). Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches. International Journal of Quality & Reliability Management, 39(3), 675–703. https://doi.org/10.1108/IJQRM-04-2021-0105
Qin, W., Zhuang, Z., Liu, Y., & Xu, J. (2022), Sustainable service oriented equipment maintenance management of steel enterprises using a two-stage optimization approach. Robotics and Computer-Integrated Manufacturing, 75, 102311. https://doi.org/10.1016/j.rcim.2021.102311
Rashidi, M., Ghodrat, M., Samali, B., Kendall, B., & Zhang, C. (2017). Remedial modelling of steel bridges through application of analytical hierarchy process (AHP). Applied Sciences, 7(2), 168. https://doi.org/10.3390/app7020168
Rahimi, M., Sadinejad, S., & Khalili-Damghani, K. (2014). Selecting the most appropriate maintenance strategies using fuzzy analytic network process: a case study of Saipa vehicle industry. Decision Science Letters, 3(2), 237–242. https://doi.org/10.5267/j.dsl.2013.10.003
Rios, M. P., Caiado, R. G. G., Vignon, Y. R., Corseuil, E. T., & Santos, P. I. N. (2024). Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive Approach. Applied Sciences, 14(23), 10902. https://doi.org/10.3390/app142310902
Sarda, K., Acernese, A., Nolè, V., Manfredi, L., Greco, L., Glielmo, L., & Del Vecchio, C. (2021). A multi-step anomaly detection strategy based on robust distances for the steel industry. IEEE Access, 9, 53827–53837. https://doi.org/10.1109/ACCESS.2021.3070659
Saaty, T. L. (1974). Measuring the fuzziness of sets. Journal of Cybernetics, 4(4), 53–61. https://doi.org/10.1080/01969727408546075
Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-I
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