Gestão & Produção
Gestão & Produção
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Economic feasibility analysis for insourcing hydraulic maintenance services using the Monte Carlo method

Análise de viabilidade econômica para a primarização de serviços de manutenção hidráulica pelo método de Monte Carlo

Nuno Miguel de Matos Torre; Nilson Brandalise; Andrei Bonamigo

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Abstract: Maintenance plays an indispensable role in the productive sector of an organization. The increasing use of high-precision operations in the steel industry means that hydraulic systems demand greater attention. This study proposes an evaluation framework for analyzing the economic feasibility of insourcing hydraulic maintenance services, seeking to present tools for assisting managers in decision-making and optimizing maintenance strategies. This paper presents a cash flow study, where the Net Present Value (NPV), the Internal Rate of Return (IRR), and the Profitability Index (PI) are calculated. Subsequently, the Monte Carlo method is applied to perform a sensitivity analysis for viewing the probabilities and output results. The main contribution of this study is to enable the evaluation of the results considering the economic feasibility of insourcing or outsourcing maintenance contracts through the proposed framework. In this case, the economic viability of insourcing presents a cost reduction in maintenance services. This approach suggested an industrial case study, where the use of the Monte Carlo and cash flow methods are useful tools for decision-making, contributing to the optimization of resources among industrial managers.


Maintenance, Hydraulic systems, Insourcing, Monte Carlo


Resumo: A manutenção representa um papel indispensável no sector produtivo de uma organização. A crescente utilização de funções operacionais com alto nível de precisão na indústria siderúrgica, faz dos sistemas hidráulicos um dos itens que requer um maior índice de atenção. O objetivo deste estudo é efetuar uma análise de viabilidade económica para primarização de serviços de manutenção hidráulica, se tornando uma ferramenta para auxiliar os gestores na tomada de decisões, almejando otimizar as estratégias de manutenção. Este artigo apresenta um estudo de fluxo de caixa, onde é calculado o Valor Presente Líquido (VPL), a Taxa Interna de Retorno (TIR) e o Índice de Lucratividade (PI). Posteriormente utiliza-se o método de Monte Carlo para efetuar uma análise de sensibilidade, permitindo assim visualizar as probabilidades e resultados de saída. A principal contribuição deste estudo se dá pela utilização do framework proposto, o qual permite uma visualização da análise dos resultados, considerando a viabilidade económica para primarização ou terceirização dos contratos de manutenção. Neste caso, a viabilidade econômica de primarização apresenta uma redução de custos nos serviços de manutenção. Esta abordagem propôs um estudo de um caso industrial, onde a utilização dos métodos de Monte Carlo e do fluxo de caixa se tornam ferramentas úteis para a tomada de decisão, contribuindo assim para a optimização dos recursos por parte dos gestores industriais.


Manutenção, Sistemas hidráulicos, Primarização, Monte Carlo


Al-Amin, A. Q., Leal, W. Fo., & Kabir, M. A. (2018). The challenges of sustainability in business: how governments may ensure sustainability for offshore firms. Technological and Economic Development of Economy, 24(1), 108-140.

Asuquo, M. P., Wang, J., Zhang, L., & Phylip-Jones, G. (2019). Application of a multiple attribute group decision making (MAGDM) model for selecting appropriate maintenance strategy for marine and offshore machinery operations. Ocean Engineering, 179, 246-260.

Bellani, L., Compare, M., Zio, E., Sepe, M., Annunziata, F., & Carlevaro, F. (2020). Optimal part flow in maintenance service contracts of gas turbines. In Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference (pp. 1467-1474). Chennai: Research Publishing Services.

Benitez, G. B., & Lima, M. J. R. F. (2019). The real options method applied to decision making – an investment analysis. Brazilian Journal of Operations & Production Management, 16(4), 562-571.

Brandalise, N., Pereira, A. S. A., & Mello, L. C. B. B. (2019). Aid application multicriteria the decision based on AHP method and fuzzy logic in commercial land selection. Gestão & Produção, 26(3), e3243.

Bustamante, C. V. (2019). Strategic choices: accelerated startups’ outsourcing decisions. Journal of Business Research, 105, 359-369.

Campos, J. T. G. A. E. A., Ferreira, A. M. S., & Freires, F. G. M. (2021). Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency. Brazilian Journal of Operations & Production Management, 18(4), 1-19.

Colantoni, A., Villarini, M., Monarca, D., Carlini, M., Mosconi, E. M., Bocci, E., & Hamedani, S. R. (2021). Economic analysis and risk assessment of biomass gasification CHP systems of different sizes through Monte Carlo simulation. Energy Reports, 7, 1954-1961.

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.

Damanpour, F., Magelssen, C., & Walker, R. M. (2020). Outsourcing and insourcing of organizational activities: the role of outsourcing process mechanisms. Public Management Review, 22(6), 767-790.

Etges, A. P. B. S., Souza, J. S., & Kliemann, F. J. No. (2017). Risk management for companies focused on innovation processes. Production, 27, e20162209.

Foroozesh, N., Mousavi, S. M., Mojtahedi, M., & Gitinavard, H. (2022). Maintenance policy selection considering resilience engineering by a new interval-valued fuzzy decision model under uncertain conditions. Scientia Iranica, 29(2), 783-799.

Hazen, G., & Magni, C. A. (2021). Average internal rate of return for risky projects. The Engineering Economist, 66(2), 90-120.

Husniah, H., Herdiani, L., Kusmaya, & Supriatna, A. K. (2018). Fuzzy usage pattern in customizing public transport fleet and its maintenance options. Journal of Physics: Conference Series, 1013, 012186.

Kandil, N., Hammami, R., & Battaïa, O. (2022). Insourcing versus outsourcing decision under environmental considerations and different contract arrangements. International Journal of Production Economics, 253, 108589.

Kim, Y., Shin, K., Ahn, J., & Lee, E. (2017). Probabilistic cash flow-based optimal investment timing using two-color rainbow options valuation for economic sustainability appraisement. Sustainability, 9(10), 1781.

Kothari, C. R., & Garg, G. (2019). Research methodology methods and techniques (4th ed.). New Delhi: New Age International.

L’Ecuyer, P. (2018). Randomized Quasi-Monte Carlo: an introduction for practitioners. In A. B. Owen & P. W. Glynn (Eds.), Monte Carlo and Quasi-Monte Carlo methods (Springer Proceedings in Mathematics & Statistics, 241, pp. 29-52). Cham: Springer.

Lee, C.-Y., & Ahn, J. (2020). Stochastic modeling of the levelized cost of electricity for solar PV. Energies, 13(11), 3017.

Li, S., Yang, Z., Tian, H., Chen, C., Zhu, Y., Deng, F., & Lu, S. (2021). Failure analysis for hydraulic system of heavy-duty machine tool with incomplete failure data. Applied Sciences, 11(3), 1249.

Moro, S. R., Cauchick-Miguel, P. A., & Mendes, G. H. S. (2021). Literature analysis on product-service systems business model: a promising research field. Brazilian Journal of Operations & Production Management, 19(1), 1-18.

Nurrohkayati, A. S., & Vanany, I. (2021). Economic analysis development and provider elective decision model on auto-ID technology investment. Journal of Physics: Conference Series, 2111, 012023.

Oliveira, R. No., Gastineau, P., Cazacliu, B. G., Le Guen, L., Paranhos, R. S., & Petter, C. O. (2017). An economic analysis of the processing technologies in CDW recycling platforms. Waste Management, 60, 277-289. PMid:27567131.

Quatrini, E., Costantino, F., Pocci, C., & Tronci, M. (2020). Predictive model for the degradation state of a hydraulic system with dimensionality reduction. Procedia Manufacturing, 42, 516-523.

Rosłon, J., Książek-Nowak, M., Nowak, P., & Zawistowski, J. (2020). Cash-flow schedules optimization within life cycle costing (LCC). Sustainability, 12(19), 8201.

Saad, S. M., & Murray, C. (2022). Development of a maintenance strategy to optimise maintenance in a world scale bioethanol production facility. In M. Shafik & K. Case (Eds.), Advances in manufacturing technology XXXV (Advances in Transdisciplinary Engineering, 25, pp. 354-360). Amsterdam: IOS Press.

Shahin, A., Aminsabouri, N., & Kianfar, K. (2018). Developing a decision making grid for determining proactive maintenance tactics: a case study in the steel industry. Journal of Manufacturing Technology Management, 29(8), 1296-1315.

Sinenko, S., & Savin, I. (2020). Assessment of sufficiency of financial resources according to plan of investment project of cooperation. E3S Web of Conferences, 217, 07026.

Singh, S., Berndt, C. C., Raman, R. K. S., Singh, H., & Ang, A. S. M. (2023). Applications and developments of thermal spray coatings for the iron and steel industry. Materials, 16(2), 516. PMid:36676253.

Sjöstrand, K., Lindhe, A., Söderqvist, T., & Rosén, L. (2019). Cost-benefit analysis for supporting intermunicipal decisions on drinking water supply. Journal of Water Resources Planning and Management, 145(12), 04019060.

Söderberg, L., Bengtsson, L., & Kaulio, M. (2017). A model for outsourcing and governing of maintenance within the process industry. Operations Management Research, 10(1-2), 20-32.

Torre, N., Leo, C., & Bonamigo, A. (2023). Lean 4.0: an analytical approach for hydraulic system maintenance in a production line of steel making plant. International Journal of Industrial Engineering and Management, 14(3), 186-199.

Züst, S., Huonder, M., West, S., & Stoll, O. (2021). Life-cycle oriented risk assessment using a Monte Carlo simulation. Applied Sciences, 12(1), 8.

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