Improving the Level of Predictive Maintenance Maturity Matrix in Industrial Enterprise

Abstract

Predictive maintenance is a maintenance strategy that applies advanced statistical methods and artificial intelligence to determine the appropriate maintenance time. The article focuses on future recommendations for industry and logistics to achieve a higher level of predictive maintenance maturity, which requires real-time monitoring of the state of the company's machinery and equipment. The article's main objective is to propose recommendations to increase effectiveness by improving the predictive maintenance maturity matrix from the current level to a higher level in the industrial enterprise. The current state of maturity has been indicated using the modified model of predictive maintenance and following recommendations from the document Manual for companies for the introduction of artificial intelligence. Simultaneously within the analysis, a predictive maintenance simulation was performed on a selected production line, including essential machines and equipment. The study also identified the individual assumptions (processes, data, infrastructure, personnel, applications, organization) necessary to implement predictive maintenance successfully. The presented case study results contribute to understanding how individual assumptions can be obtained for predictive maintenance improvement and how innovative solutions in the context of Industry 4.0 and Logistics 4.0 can be achieved in enterprises.

An Exploration of Organisational Readiness for Industry 4.0: A Predictive Maintenance Perspective

“…In this respect, this study contributes to the literature on I4.0 maturity models (Sütőová, Šooš and Kóča, 2020).) and PdM maturity models (Mesarosova et al, 2022).…”

Exploring current and future trends of predictive maintenance 4.0 in Tanzania: A systematic literature review

“…Taking into consideration the relevance of these three pillars, an analysis of pillars is conducted in the following. (Ardito et al, 2018;Calabrese, 2022;Lass & Gronau, 2020;Morenas & ..., 2022;Okeagu & Mgbemena, 2022;Orošnjak et al, 2021;Rai et al, 2021;Rehman et al, 2018;Rodríguez et al, 2022;Sahba et al, 2021;Terrissa et al, 2016;Vijayaraghavan & Leevinson, 2019) PdM maturity levels 9 (Achouch et al, 2022;Kans, 2010;Rosenius, 2020) (Achouch et al, 2022;Errandonea et al, 2022;Mesarosova & Martinovicova, 2022;Schuh et al, n.d.;) Adoption factors for industry 4.0 PdM 10 (Achouch et al, 2022;Bettiol et al, 2020;Burger, 2022;Haarman et al, 2018;Maintenance, 2017;X. T. Nguyen & Luu, 2020;Oliveira et al, 2013;Parhi et al, 2022;Robatto Simard et al, 2023;Zonta & da Costa, 2020) Decision support tools for PdM 4.0 Adoption 7 (Carnero,2006;Faiz and Edirisinghe, 2009;Van Horenbeek and Pintelon, 2013;Nguyen, Do and Grall, 2015;Bousdekis et al, 2017Bousdekis et al, , 2019…”

Improving Material Flows in an Industrial Enterprise: A Comprehensive Case Study Analysis

“…The digital enterprise is a prominent area of study, with research mostly concentrating on the incorporation of digital technologies throughout the organization [12]. The digital enterprise also leverages tools such as cloud computing, big data analysis, and artificial intelligence, which have yet to be fully explored [13].…”