Implementation of prescriptive maintenance on the factory floor: strategic decision structured in swot analysis
DOI:
https://doi.org/10.15675/gepros.3007Palavras-chave:
Efficiency, Maintenance 4.0, Prescriptive maintenance, Factory floorResumo
Purpose: The objective of this research is to identify the important factors for implementing the prescriptive maintenance paradigm on the factory floor, considering three main factories as research objects (Company A: Steel; Company B: Mining; Company C: Pulp and Paper), located in the State of Espírito Santo, Southeast Region of Brazil.
Theoretical framework: The implementation of proactive strategies in factories, such as prescriptive maintenance policies, has become increasingly important due to the effects on factory floor productivity and the competitive development of its resources.
Methodology/Approach: The methodological procedures used were the combination of three data collection mechanisms, narrative bibliographic review, document analysis and participant observation. To achieve the general objective, the perceptions of experts and researchers were considered. The contributions arising from the application of SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis for the internal and external environments of the factories made it possible to understand the implications for the implementation of Prescriptive Maintenance in the factory floor.
Findings: Two important results emerged from the research, one model of general use that shows the importance of the implications of the SWOT Matrix in the positioning of decision making and another model specific to the context, to assist in the implementation of the proactive process of Prescriptive Maintenance, providing recommendations for managers and professionals who work on the factory floor.
Research, practical & social implications: The results of this study provide valuable information for formulating proactive maintenance policies aimed at increasing the efficiency of physical assets on the factory floor. They also have important practical implications for strategic maintenance management, for example, the SWOT analysis also suggests that implementing the prescriptive maintenance paradigm is not an easy task for any professional and that it is necessary to weigh all opportunities and threats before making any decision strategic.
Originality/ Value: Based on the data obtained, it is presented an academic contribution to the literature on the importance of implementing proactive maintenance policies on the factory floor, expanding and strengthening the theoretical foundation of intelligent maintenance, and also provided management information for decision-making in the process of implementing the Prescriptive Maintenance paradigm.
Keywords: Efficiency; Maintenance 4.;, Prescriptive maintenance; Factory floor.
Referências
Al-Najjar, B., Imad Alsyouf (2000a). Impact of integrated vibration-based maintenance on plant-LCC: A case study. In: McNulty, G.J. (Ed.), Third International (Refereed) Conference Quality, Reliability and Maintenance, Oxford, England, Professional Engineering Publishing Limited, Bury St. Edmund, London UK, 30–31 March, pp. 105–110. Doi: 10.1016/S0377-2217(03)00258-3
Amin, S., Razmi, J., Zhang, G., (2011). Supplier selection and order allocation based on fuzzy swot analysis and fuzzy linear programming. Expert Syst. Appl. Vol. 1, pp. 334 e 342. doi:10.1016/j.eswa.2010.06.071
Ansari F., Glawar R., Sihn W. :( 2017) Prescriptive Maintenance of CPPS by Integrating Multi-modal Data with Dynamic Bayesian Networks, Machine Learning for Cyber Physical Systems, Springer, (In Press). https://doi.org/10.1007/978-3-662-59084-3_1
Antonio Padovano, Francesco Longo, Letizia Nicoletti, Lucia Gazzaneo, Alessandro Chiurco, Simone Talarico.(2021). A prescriptive maintenance system for intelligent production planning and control in a smart cyber-physical production line. Procedia CIRP 104 (2021) 1819–1824. https://doi.org/10.1016/j.procir.2021.11.307
Aw, B. Y., Roberts, M. J., & Xu, D. Y. (2008). R&D investments, exporting, and the evolution of firm productivity. Vol. 2, pp. 451–456. DOI: 10.1257/aer.98.2.451
Aysa Ipek Erdogan, (2019). "Determinants of perceived bank financing accessibility for SMEs: evidence from an emerging market," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pp. 690-716. https://doi.org/10.1080/1331677X.2019.1578678
Ballestar, M. T., Grau-Carles, P., & Sainz, J. (2019). Predicting customer quality in ecommerce social networks: A machine learning approach. Review of Managerial Science, vol. 13(3), pp. 589–603. https://doi.org/10.1007/s11846-018-0316-x
Betz, U. A., Betz, F., Kim, R., Monks, B., & Phillips, F. (2019). Surveying the future of science, technology and business – A 35 year perspective. Technological Forecasting and Social Change, 144, pp. 137-147. https://doi.org/10.1016/j.techfore.2019.04.005
Bokrantz J, Skoogh A, Berlin C, Stahre J. Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. Int. J. of Production Economics, 2015; 191:154-169. https://doi.org/10.1016/j.ijpe.2017.06.010
Bousdekis, A.; Lepenioti, K.; Apostolou, D.; Mentzas, G. (2021). A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications. Electronics 2021, 10, 828. https://doi.org/10.3390/electronics 10070828
Britain, G. (2007). The ONS Productivity Handbook: A Statistical Overview and Guide, Palgrave Macmillan. ISBN 02305730109780230573017
Chi-Ho Jeon, Chang-Su Shim b, Yang-Hee Lee, Jennifer Schooling. (2024). Prescriptive maintenance of prestressed concrete bridges considering digital twin and key performance indicator. Engineering Structures 302 (2024) 117383. https://doi.org/10.1016/j.engstruct.2023.117383
Dan Li, Anna Landström, Åsa Fast-Berglund, Peter Almström. (2019). Human-Centred Dissemination of Data, Information and Knowledge in Industry 4.0. Procedia CIRP 84 (2019) 380–386. https://doi.org/10.1016/j.procir.2019.04.261
Daniewski, K., Kosicka, E., Mazurkiewicz, D., (2018). Analysis of the correctness of determination of the effectiveness of maintenance service actions. Management and Production Engineering Review, vol. 9 (2), pp. 20-25. DOI: 10.24425/119522
Doraszelski, U., & Jaumandreu, J. (2013). R&D and productivity: Estimating endogenous productivity. Review of Economic Studies, vol. 80(4), pp. 1338–1383. Doi: 10.1093/restud/rdt011
Dyson, R.G., (2004). Strategic development and SWOT analysis at the University of Warwick. Eur. J. Oper. Res. Vol. 152 (3), pp. 631 e 640. https://doi.org/10.1016/S0377-2217(03)00062-6
Errandonea I, Beltran ´ S, Arrizabalaga S. (2020). Digital Twin for maintenance: a literature review. Comput Ind 2020; 123. https://doi.org/10.1016/j.compind.2020.103316
Fang, Y., Tao, W., Tee, K.F. (2019). A new computational method for structural reliability with big data. Eksploatacja i Niezawodnosc – Maintenance and Reliability vol. 21 (1), pp. 159–163. http://dx.doi.org/10.17531/ein.2019.1.18
Gola, A., (2019). Reliability analysis of reconfigurable manufacturing system structures using computer simulation methods. Eksploatacja i Niezawodnosc – Maintenance and Reliability vol. 21 (1), pp. 90–102. DOI:10.17531/ein.2019.1.11
González, I.; Calderón, A.J.; Figueiredo, J.; Sousa, J. (2019). A literature survey on open platform communications (OPC) applied to advanced industrial environments. Electronics 2019, 8, 510. https://doi.org/10.3390/electronics8050510
Hadidi, L. A., Al-Turki, U. M., & Rahim, A. (2012). Integrated models in production planning and scheduling, Maintenance and quality: a review. International Journal of Industrial and Systems Engineering, 10(1), 21-50. DOI:10.1504/IJISE.2012.044042
Hall, B. H., Lotti, F., & Mairesse, J. (2009). Innovation and productivity in SMEs: e Empirical evidence from Italy. Small Business Economics, vol. 33(1), pp. 13–33. https://doi.org/10.1007/s11187-009-9184-8
Huh, J.H.; Lee, H.G. Simulation and Test Bed of a Low-Power Digital Excitation System for Industry 4.0 (2018). Processes 2018, 6, 145. https://doi.org/10.3390/pr6090145
J. Friederich and S. Lazarova-Molnar (2024). Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities. Journal of Manufacturing Systems, Vol.72, pp. 38–58. https://doi.org/10.1016/j.jmsy.2023.11.001
Kangas, J., Kurttila, M., Kajanus, M., Kangas, A., (2003). Evaluating the management strategies of a forestland estated the SOS approach. J. Environ. Manag. Vol. 69 (4), pp. 349 e 358. https://doi.org/10.1016/j.jenvman.2003.09.010
Karim, R., Westerberg J., Galar D., Kumar U., 2016. Maintenance Analytics–The New Know in Maintenance. IFAC-PapersOnLine 49 (28): 214–219. doi:10.1016/j.ifacol.2016.11.037
Kim J, Ahn Y, Yeo H. (2016). A comparative study of time-based maintenance and condition-based maintenance for optimal choice of maintenance policy. Struct Infrastruct Eng 2016; 12:1525–36. https://doi.org/10.1080/ 15732479.2016.1149871
Li D, Landström A, Fast-Berglund A, Almström P. (2019). Human-centred dissemination of data, information and knowledge in Industry 4.0. Proc CIRP 2019; 84:380–6. https://doi.org/10.1016/j.procir.2019.04.261
Lucas Santos Dalenogarea, Guilherme Brittes Beniteza, Néstor Fabián Ayalab, Alejandro Germán Franka. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics vol. 204, pp. 383–394. https://doi.org/10.1016/j.ijpe.2018.08.019Get rights and content
Lucas-Estañ, M.C.; Sepulcre, M.; Raptis, T.P.; Passarella, A.; Conti, M. (2018). Emerging trends in hybrid wireless communication and data management for the industry 4.0. Electronics 2018, 7, 400. https://doi.org/10.3390/electronics7120400
Maleki, H., & Yang, Y. (2017). An uncertain programming model for preventive maintenance scheduling. Grey Systems: Theory and Application, vol. 7(1), pp. 111–122. https://doi.org/10.1108/GS-07-2016-0015
Mckone, K., Elliott, W., (1998). TPM: planned and autonomous maintenance: bridging the gap between practice and research. Production and Operations Management vol. 7 (4), pp. 335–351. https://doi.org/10.1111/j.1937-5956.1998.tb00128.x
Mohaiad Elbasheer, Francesco Longo, Giovanni Mirabelli, Antonio Padovano, Vittorio Solina, Simone Talarico (2022). Integrated Prescriptive Maintenance and Production Planning: a Machine Learning Approach for the Development of an Autonomous Decision Support Agent. IFAC PapersOnLine 55-10 (2022) 2605–2610. DOI:10.1016/j.ifacol.2022.10.102
Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., Ueda, K., (2016). Cyber-physical systems in manufacturing. CIRP Ann. - Manuf. Technol. Vol. 65, pp. 621–641. https://doi.org/10.1016/j.cirp.2016.06.005
Muhammad Imran Khan. (2018). Evaluating the strategies of compressed natural gas industry using an integrated SWOT and MCDM approach. Journal of Cleaner Production vol. 172, pp. 1035 e 1052. https://doi.org/10.1016/j.jclepro.2017.10.231
Nguyen Ngoc H, Lasa G, Iriarte I. (2022). Human-centred design in Industry 4.0: Case study review and opportunities for future research. J Intell Manuf 2022; 33(1):35–76. DOI:10.1007/s10845-021-01796-x
Nikolaou, I.E., Evangelinos, K.I., (2010). A SWOT analysis of environmental management practices in Greek Mining and Mineral Industry. Resour. Policy vol. 35 (3), pp. 226 e 234. https://doi.org/10.1016/j.resourpol.2010.02.002
Nikolic, B., Ignjatic, J., Suzic, N., Stevanov, B., Rikalovic, A., (2017). Predictive manufacturing systems in industry 4.0: Trends, benefits and challenges. In: Proceedings of the 28th DAAAM International Symposium, pp. 796–802. DOI: 10.2507/28th.daaam.proceedings.
Padovano, A., Longo, F., Nicoletti, L., Gazzaneo, L., Chiurco, A., & Talarico, S. (2021). A prescriptive maintenance system for intelligent production planning and control in a smart cyberphysical production line. Procedia CIRP, 104, 1819-1824. https://doi.org/10.1016/j.procir.2021.11.307
Peng, Y., Dong, M., Zuo, M.J., (2010). Current Status of Machine Prognostics in Condition-Based Maintenance: A review. International Journal of Advanced Manufacturing Technolo-gy, vol. 50(4), pp.297-313. DOI 10.1007/s00170-009-2482-0
Riccardo Manzini, Riccardo Accorsi, Teresa Cennerazzo, Emilio Ferrari, Fausto Maranesi. (2015). The scheduling of maintenance. A resource-constraints mixed integer linear programming model. Computers & Industrial Engineering, Volume 87, September 2015, Pages 561-568. https://doi.org/10.1016/j.cie.2015.06.006
Richard W. Puyt, Finn Birger Lie, Celeste P.M. Wilderom. (2023). The origins of SWOT analysis. Long Range Planning 56 (2023) 102304. https://doi.org/10.1016/j.lrp.2023.102304
Riis, J., Luxhoj, J., Uffe, T., (1997). A situational maintenance model. International Journal of Quality and Reliability Management vol. 14 (4), pp. 349–366. https://doi.org/10.1108/02656719710170620
Robert Glawar, Fazel Ansari, Csaba Kardos, Kurt Matyas, Wilfried Sihn. (2019). Conceptual Design of an Integrated Autonomous Production Control Model in association with a Prescriptive Maintenance Model (PriMa). Procedia CIRP vol. 80, pp. 482–487. https://doi.org/10.1016/j.procir.2019.01.047
Sikorska JZ, Hodkiewicz M, Ma L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 2011; 1803-1836. https://doi.org/10.1016/j.ymssp.2010.11.018
Simões, J. M., Gomes, C. F., & Yasin, M. M. (2011). A literature review of maintenance performance measurement: A conceptual framework and directions for future research. Journal of Quality in Maintenance Engineering, vol. 17(2), pp. 116–137. DOI:10.1108/13552511111134565
Stachowiak, A. (2015). Availability and reliability of resources in agile manufacturing systems, Safety and Reliability: Methodology and Application, pp. 2425-2432. DOI: 10.1201/b17399-331
Syed Meesam Raza Naqvi, Mohammad Ghufran, Safa Meraghni, Christophe Varnier, Jean-Marc Nicod, Noureddine Zerhouni. (2022). Human knowledge centered maintenance decision support in digital twin environment. Journal of Manufacturing Systems 65 (2022) 528–537. https://doi.org/10.1016/j.jmsy.2022.10.003
Syverson, C. (2011). What determines productivity? Journal of Economic Literature, vol. 49(2), pp. 326–365. DOI: 10.1257/jel.49.2.326
Terrados, J., Almonacid, G., Hontoria, L., (2007). Regional energy planning through SWOT analysis and strategic planning tools. Impact on renewables development. Renew. Sustain. Energy Rev. vol. 11 (6), pp. 1275 e 1287. https://doi.org/10.1016/j.rser.2005.08.003
Thoben, K.-D., Wiesner, S., Wuest, T., (2017). Industriy 4.0” and Smart manufacturing - a review of research issues and application examples. Int. J. Autom. Technol. Vol. 11, pp. 4–16. DOI:10.20965/ijat.2017.p0004
Tsang, A. H. (2002). Strategic dimensions of maintenance management. Journal of Quality in Maintenance Engineering, vol. 8(1), pp. 7–39. https://doi.org/10.1108/13552510210420577
Yun, W. Y., Kim, G. R., & Yamamoto, H. (2012). Economic design of a load-sharing consecutive k-out-of-n:F system. IIE Transactions, vol. 44(1), pp. 55–67. https://doi.org/10.1080/0740817X.2011.590442
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: a review. Engineering, 3(5), 616-630. https://doi.org/10.1016/J.ENG.2017.05.015
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