Abstract
The commoditization initiatives triggered in manufacturing companies the concern with the rigidity of their offers, and the appeal of their offers to their customers, leading to servitization strategies. This research highlights the importance of preventive maintenance, and builds on the extraction and processing of data for a PSS business model. Data from https://www.kaggle.com was used to analyze the performance of a production process with three products with differentiated levels of quality. Data analysis was of exploratory nature using descriptive statistics and Pearson’s correlation through, using Python via Google Collaboratory. The main failures were exposed allowing the elaboration of a preventive maintenance plan. Through multivariate statistical analyses, we demonstrate that in a PSS business model, having machine suppliers at your disposal that combine the sale of the item with the provision of predictive maintenance services is a differential that will help to make assertive a preventive d skillful decision, avoiding maintenance unnecessary and waste of time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goedkoop, M., Van Halen, C., Te Riele, H., Rommens, P.: Product Systems Service. Product Innovation Technology Management, Netherlands (1999)
Lee, J., Shin, H.-D., Hong, S.: Servitization of global manufacturing business. J. Ind. Compet. Trade 21(4), 565–584 (2021). https://doi.org/10.1007/s10842-021-00367-3
Rondi, E., De Massis, A., Kraus, S.: Servitization through open service innovation in family firms: exploring the ability-willingness paradox. J. Bus. Res. (2021). https://doi.org/10.1016/j.jburses.2021.06.040
Reim, W., Sjodin, D., Parida, V.: Circular business model implementation: a capability development case study from the manufacturing industry. Bus. Strategy Environ. 30(6), 2745–2757 (2021)
Borgi, T., Hidri, A., Neef, B., Nauncer, M.S.: Data analytics for predictive maintenance of industrial robots. In: International Conference on Advanced Systems and Electric Technologies (2017). https://doi.org/10.1109/ASET.2017.7983729
Gupta, D., Rani, R.: A study of big data evolution and research challenges. J. Inf. Sci. 45(3), 322–340 (2019)
Huang, S.C., McIntosh, S., Sobolevsky, S., Hung, P.C.K.: Big data analytics and business intelligence in industry. Inf. Syst. Front. https://doi.org/10.1007/s10796-017-9804-9
Carvalho, T.P., Soares, F., Vita, R., Francis, P.R., Basto, J.P., Alacalá, S.G.S.: A systematic review of machine learning methods applied to predictive maintenance (2019). https://doi.org/10.1016/j.cie.2019.106024
Perez, R.S., Rock, A.D., Leitao, P., Barata, J.: IDARTS – towards intelligent data analysis and real-time supervision for industry 4.0 (2018). https://doi.org/10.1016/j.compind.2018.07.004
Naik, P., Schroeder, A., Kapoor, K.K., Ziaee Bigdeli, A., Baines, T.: Behind the scenes of digital servitization: actualizing IoT-enabled affordances. Ind. Mark. Manage 89, 232–244 (2020). https://doi.org/10.1016/J.INDMARMAN.2020.03.010
Awan, U., Shamim, S., Khan, Z., Zia, N.U., Shariq, S.M., Khan, M.N.: Big data analytics capability and decision-making: the role of data-driven insight on circular economy performance. Technol. Forecast. Soc. Change 168 (2021). https://doi.org/10.1016/j.techfore.2021.120766
Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. Manag. Inf. Syst. 36(4) (2012). https://doi.org/10.2307/41703503
Tabesh, P.: Who’s making the decisions? How managers can harness artificial intelligence and remain in charge. J. Bus. Strategy 43(6), 373–380 (2022). https://doi.org/10.1108/JBS-05-2021-0090
Razali, M.N., et al.: Big data analytics for preventive maintenance management. Plan. Malays. 19(3), 423–437 (2021). https://doi.org/10.21837/PM.V19I17.1019
Sang, G.M., Xu, L., de Vrieze, P.: A predictive maintenance model for flexible manufacturing in the context of industry 4.0. Front. Big Data 4 (2021). https://doi.org/10.3389/fdata.2021.663466
Silva, D., Lopez, E.L., Junior, S.S.B.: Quantitative research: elements, paradigms and definitions. J. Manag. Secr. https://doi.org/10.7769/gesec.v5i1.297
Filho, D.B.F., Rock, E.C., Junior, J.A.S., Paranhos, R., Neves, J.A.B., Silva, M.B.: Unraveling the mysteries of Pearson’s correlation coefficient: the return. Polit. Res. Noteb. (8), 66–95 (2014)
KAGGLE. Machine Predictive Maintenance Classification (2022). www.kaggle.com/datasets/shivamb/machine-predictive-maintenance-classification. Accessed 13 Nov 22
Dubois, P.F., Hinsen. K., Hugunin, J.: Numerical Python (1996). https://doi.org/10.1063/1.4822400
Matzka, S.: Explainable artificial intelligence for predictive maintenance applications. In: 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), pp. 69–74. IEEE, September 2020
GuimarĂŁes, AM Statistics: Correlation analysis using Python and R. https://medium.com/omixdata/estat%C3%ADstica-an%C3%A1lise-de-correla%C3%A7%C3%A3o-using-python-er-d68611511b5a. Accessed Dec 2022
Acknowledgements
This work was financially supported by the research unit on Governance, Competitiveness and Public Policy (UIDB/04058/2020)+(UIDP/04058/2020), funded by national funds through FCT - Fundação para a Ciência e a Tecnologia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kuroki, A., Cardoso, V.H., Neto, G.C.O., Amorim, M. (2024). The Implementation of Preventive Maintenance in a Product-Service System (PSS) Business Model. In: Silva, F.J.G., Ferreira, L.P., Sá, J.C., Pereira, M.T., Pinto, C.M.A. (eds) Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems. FAIM 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-38165-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-38165-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-38164-5
Online ISBN: 978-3-031-38165-2
eBook Packages: EngineeringEngineering (R0)