An Exploration of Organisational Readiness for Industry 4.0: A Predictive Maintenance Perspective
Abstract
Purpose: The aim of this paper is to examine the extent to which selected Slovenian companies are prepared to integrate the complex requirements of Industry 4.0 (I4.0) into their asset management practices, using the specific example of predictive maintenance.
Methodology/Approach: A research study was conducted on a sample of Slovenian manufacturing companies. Data was collected using a structured questionnaire to investigate the extent to which companies are engaged with new technologies and their current and future focus on their use in predictive maintenance.
Findings: The analysis of the empirical data shows that companies are aware of the benefits that can be achieved with I4.0 solutions. The results also show that the companies surveyed lack a clear vision and implementation roadmap for I4.0. The results also show that the majority of companies in the sample are still at an early stage of predictive maintenance strategy maturity.
Research Limitation/implication: The sample of responding companies is limited to the Slovenian manufacturing industry, and the subjective information comes from only one representative person in each company.
Originality/Value of paper: The paper is one of the first studies to highlight digitalisation and predictive maintenance in the context of I4.0.
References
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Amrita, M. A. and Akhilesh, K. B., 2020. Digital Masters: Blueprinting Digital Transformation. In Smart Technologies. Singapore: Springer.
Biard, G. and Nour, G. A., 2021. Industry 4.0 Contribution to Asset Management in the Electrical Industry. Sustainability, 13(18), 10369. https://doi.org/10.3390/su131810369
Borchardt, M., Pereira, G.M., Milan, G. S., Scavarda, A. R., Nogueira, E. O. and Poltosi, L.C., 2022. Industry 5.0 Beyond Technology: An Analysis Through the Lens of Business and Operations Management Literature. Organizacija, 55(4), p.p.305-321. https://doi.org/10.2478/orga-2022-0020
Bousdekis, A., Lepenioti, K., Apostolou, D. and Mentzas, G., 2019. Decision Making in Predictive Maintenance: Literature Review and Research Agenda for Industry 4.0. IFAC-PapersOnLine, 52(13), p.p.607-612. https://doi.org/10.1016/j.ifacol.2019.11.226
Breunig, M., Kelly, R., Mathis, R. and Wee, D., 2016. Industry 4.0 after the initial hype. Where manufacturers are finding value and how they can best capture it. [pdf] McKinsey Digital. [Online] Available at: https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/getting%20the%20most%20out%20of%20industry%204%200/mckinsey_industry_40_2016.pdf [Accessed 20 November 2023]
Brocal, F., González-Gaya, C., Komljenovic, D., Katina, P.D. and Sebastián, M.A., 2019. Emerging risk management in Industry 4.0: an approach to improve organisational and human performance in the complex systems, Complexity, 2019, p.p.1-13, https://doi.org/10.1155/2019/2089763
Bukhsh, Z. A. and Stipanovic, I., 2020. Predictive Maintenance for Infrastructure Asset Management. IT Professional, 22(5), p.p.40-45. https://doi.org/10.1109/MITP.2020.2975736
Cachada, A., Barbosa, J., Leitño, P., Gcraldcs, C. A., Deusdado, L., Costa, J., ... and Romero, L., 2018. Maintenance 4.0: Intelligent and predictive maintenance system architecture. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, Italy, 2018, pp. 139-146. https://doi.org/10.1109/ETFA.2018.8502489
Candón, E., Martínez-Galán, P., De la Fuente, A., González-Prida, V., Márquez, A. C., Gómez, J., Sola, A. and Macchi, M., 2019. Implementing intelligent asset management systems (IAMS) within an industry 4.0 manufacturing environment.
IFAC-PapersOnLine, 52(13), p.p.2488-2493. https://doi.org/10.1016/j.ifacol.2019.11.580
Crespo Marquez, A., Gomez Fernandez, J. F., Martínez-Galán Fernández, P. and Guillen Lopez, A., 2020. Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models. Energies, 13(15), 3762. https://doi.org/10.3390/en13153762
Fatorachian, H. and Kazemi, H., 2018. A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework. Production Planning & Control, 29(8), pp.1-12. https://doi.org/10.1080/09537287.2018.1424960
García, S.G. and García, M.G., 2019. Industry 4.0 implications in production and maintenance management: An overview. Procedia Manufacturing, 41, p.p.415-422. https://doi.org/10.1016/j.promfg.2019.09.027
Haarman, M., Mulders, M. and Vassiliadis, C., 2017. Predictive Maintenance 4.0-Predict the unpredictable. [online] Available at: https://www.pwc.be/en/documents/20171016-predictive-maintenance-4-0.pdf [Accessed 19 July 2023].
Hein-Pensel, F., Winkler, H., Brückner, A., Wölke, M., Jabs, I., Mayan, I. J., Kirschenbaum, A., Friedrich, J. and Zinke-Wehlmann, C., 2023. Maturity assessment for Industry 5.0: A review of existing maturity models. Journal of Manufacturing Systems, 66, p.p.200-210. https://doi.org/10.1016/j.jmsy.2022.12.009
Hizam-Hanafiah, M., Soomro, M. A. and Abdullah, N. L., 2020. Industry 4.0 readiness models: a systematic literature review of model dimensions. Information, 11(7), 364. https://doi.org/10.3390/info11070364
Hodkiewicz, M., 2015. Asset management-quo vadis (where are you going)?. International Journal of Strategic Engineering Asset Management, 2(4), p.p.313-327. https://dx.doi.org/10.1504/IJSEAM.2015.075411
Jasiulewicz-Kaczmarek, M., Legutko, S. and Kluk, P., 2020. Maintenance 4.0 technologies–new opportunities for sustainability driven maintenance. Management and production engineering review, 11(2), p.p.74-87. 10.24425/mper.2020.133730
Kagermann, H., 2015. Change through digitisation—Value creation in the age of Industry 4.0. In Management of permanent change. Wiesbaden: Springer Gabler.
Kans, M. and Galar, D., 2017. The impact of maintenance 4.0 and big data analytics within strategic asset management. Maintenance Performance and Measurement and Management 2016 (MPMM 2016). Luleå, Sweden, 28 November 2017. Luleå tekniska universitet.
Kern, T., Krhač Andrašec, E., Urh, B. and Senegačnik, M., 2020. Digital transformation reduces costs of the paints and coatings development process. Coatings, 10(7), 703. https://doi.org/10.3390/coatings10070703
Komljenovic, D., Abdul-Nour, G. and Boudreau, J.F., 2019. Decision-making in asset management under regulatory constraints. Asset Intelligence through Integration and Interoperability and Contemporary Vibration Engineering Technologies. Cham: Springer.
Kumar, U. and Galar, D., 2018. Maintenance in the era of industry 4.0: issues and challenges. In P.K. Kapur, Kumar, U. and A.K. Verma, 2018. Quality, IT and Business Operations. Singapore: Springer.
Kumar, S., Suhaib, M. and Asjad, M., 2020. Industry 4.0: complex, disruptive, but inevitable. Management and Production Engineering Review, 11(1), p.p.43-51. 10.24425/mper.2020.132942
Kumar, U. and Galar, D., 2018. Maintenance in the era of industry 4.0: issues and challenges. In: Kapur, P., Kumar, U., Verma, A. (eds) Quality, IT and Business Operations. Springer Proceedings in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5577-5_19
Maletič, D., Marques de Almeida, N., Gomišček, B. and Maletič, M., 2023. Understanding motives for and barriers to implementing asset management system: an empirical study for engineered physical assets. Production Planning & Control, 34(15), p.p.1497-1512. https://doi.org/10.1080/09537287.2022.2026672
Maletič, D., Maletič, M., Al-Najjar, B. and Gomišček, B., 2020. An analysis of physical asset management core practices and their influence on operational performance. Sustainability, 12(21), 9097. https://doi.org/10.3390/su12219097
Maletič, D., Maletič, M., Al-Najjar, B. and Gomišček, B., 2018. Development of a model linking physical asset management to sustainability performance: An empirical research. Sustainability, 10(12), 4759. https://doi.org/10.3390/su10124759
Maletic, D., Maletic, M., Al-Najjar, B. and Gomišcek, B., 2014. The role of maintenance in improving company's competitiveness and profitability: a case study in a textile company. Journal of Manufacturing Technology Management, 25(4), p.p.441-456. https://doi.org/10.1108/JMTM-04-2013-0033
Markulik, Š., Sinay, J. and Pačaiová, H., 2019. Quality Assurance in the Automotive Industry and Industry 4.0. In Smart Technology Trends in Industrial and Business Management (pp. 217-225). Cham: Springer.
Mays, N., C. Pope, and J. Popay., 2005. Systematically reviewing qualitative and quantitative evidence to inform management and policymaking in the health field. Journal of Health Services Research & Policy, 10(1), p.p.6–20. https://doi.org/10.1258/1355819054308576
Mesarosova, J., Martinovicova, K., Fidlerova, H., Chovanova, H. H., Babcanova, D. and Samakova, J., 2022. Improving the level of predictive maintenance maturity matrix in industrial enterprise. Acta Logistica, 9(2), p.p.183-193. 10.22306/al.v9i2.292
Mohan, R., Roselyn, J.P. and Uthra, R.A., 2023. LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation. Journal of Quality in Maintenance Engineering, 29(4), p.p. 763-798. https://doi.org/10.1108/JQME-07-2022-0041
Mohapatra, A. G., Mohanty, A., Pradhan, N. R., Mohanty, S. N., Gupta, D., Alharbi, M., ... and Khanna, A., 2023. An Industry 4.0 implementation of a condition monitoring system and IoT-enabled predictive maintenance scheme for diesel generators. Alexandria Engineering Journal, 76, p.p.525-541. https://doi.org/10.1016/j.aej.2023.06.026
Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E. and Loncarski, J., 2018. Machine learning approach for predictive maintenance in industry 4.0. 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Oulu, Finland, 2018, pp. 1-6, 10.1109/MESA.2018.8449150
Pittaway, L., M. Robertson, K. Munir, D. Denyer, and Neely, A., 2004. Networking and innovation: A systematic review of the evidence. International Journal of Management Reviews, 5–6(3–4), p.p.137–168. https://doi.org/10.1111/j.1460-8545.2004.00101.x
Poor, P., Basl, J. and Ženíšek, D., 2020. Assessing the predictive maintenance readiness of enterprises in West Bohemian region. Procedia Manufacturing, 42, p.p.422-428. https://doi.org/10.1016/j.promfg.2020.02.098
Psarommatis, F., May, G. and Azamfirei, V., 2023. Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework. Journal of Manufacturing Systems, 68, p.p.376-399. https://doi.org/10.1016/j.jmsy.2023.04.009
Schmidt, B., Gandhi, K., Wang, L. and Galar, D., 2017. Context preparation for predictive analytics–a case from manufacturing industry. Journal of Quality in Maintenance Engineering, 23(3), p.p.341-354. https://doi.org/10.1108/JQME-10-2016-0050
Schuman, C.A. and Brent, A.C., 2005. Asset life cycle management: towards improving physical asset performance in the process industry. International Journal of Operations & Production Management, 25(6), p.p.566-579, https://doi.org/10.1108/01443570510599728
Sekaran, U. and Bougie, R., 2016. Research methods for business: A skill building approach. John Wiley & Sons.
Ślusarczyk, B., 2018. Industry 4.0: Are we ready? Polish Journal of Management Studies, 17(1), p.p.232-248. https://doi.org/10.17512/pjms.2018.17.1.19
Sony, M. and Naik, S., 2020. Key ingredients for evaluating Industry 4.0 readiness for organisations: a literature review. Benchmarking: An International Journal, 27(7), p.p.2213-2232. https://doi.org/10.1108/BIJ-09-2018-0284
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Authors
Maletič, D., & Maletič, M. (2024). An Exploration of Organisational Readiness for Industry 4.0: A Predictive Maintenance Perspective. Quality Innovation Prosperity, 28(1), 26–46. https://doi.org/10.12776/qip.v28i1.1984
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