Integration of Industry 4.0 technologies into Total Productive Maintenance practices

Tortorella, Guilherme Luz; Fogliatto, Flávio Sanson; Cauchick‐Miguel, Paulo A.; Kurnia, Sherah; Jurburg, Daniel

doi:10.1016/j.ijpe.2021.108224

Cited by 105 publications

(60 citation statements)

References 87 publications

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“…Despite I4.0's growing understanding, Fettermann et al (2018) and Tortorella et al (2021b) argued that academics and practitioners still present difficulties grasping its concepts. As integrating I4.0 technologies into manufacturers is an innovative process (Lorenz et al, 2020), it is subjected to the innovation uncertainties posited by DIT, which may occur regardless of the company's maturity in terms of existing management approaches, such as TPM.…”

Section: Tpm and Maintenance Performancementioning

confidence: 99%

“…, 2020). For example, Tortorella et al. (2021b) analyzed how I4.0 has been integrated into four manufacturers from different sectors, indicating that the barriers and drivers for such integration may vary.…”

Section: Introductionmentioning

confidence: 99%

“…I4.0 technologies enable adopting innovative maintenance strategies and optimizing current approaches (Silvestri et al, 2020). For example, Tortorella et al (2021b) analyzed how I4.0 has been integrated into four manufacturers from different sectors, indicating that the barriers and drivers for such integration may vary. Moreover, the use of I4.0 technologies and smart devices allows an increase in data generation, which requires further processing and analysis to support assertive maintenance decisions (Mourtzis et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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The impact of Industry 4.0 on the relationship between TPM and maintenance performance

Tortorella

Saurin²,

Fogliatto

et al. 2022

JMTM

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PurposeIn this paper, the authors examine the impact of Industry 4.0 (I4.0) technologies on the relationship between total productive maintenance (TPM) practices and maintenance performance.Design/methodology/approachData collection was carried out through a multinational survey with 318 respondents from different manufacturing companies located in 15 countries. Multivariate data techniques were applied to analyze the collected data. Diffusion of innovations theory (DIT) was the adopted theoretical lens for our research.FindingsThe authors’ findings indicate that I4.0 technologies that aim to process information to support decision-making and action-taking directly affect maintenance performance. Technologies oriented to sensing and communicating data among machines, people, and products seem to moderate the relationship between TPM practices and maintenance performance. However, the extent of such moderation varies according to the practices involved, sometimes leading to negative effects.Originality/valueWith the advances of I4.0, there is an expectation that several maintenance practices and performance may be affected. Our study provides empirical evidence of these relationships, unveiling the role of I4.0 for maintenance performance improvement.

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Section: Tpm and Maintenance Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The impact of Industry 4.0 on the relationship between TPM and maintenance performance

Tortorella

Saurin²,

Fogliatto

et al. 2022

JMTM

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“…4 Thus, the fault diagnosis of bearings of the CPs has a great importance. [5][6][7][8][9][10] The bearings are the reason for more than 41% of machine breakdowns, thus, this paper investigates various faults in bearings. The bearing structure and its components are shown in Figure 3.…”

Section: Introductionmentioning

confidence: 99%

Condition monitoring of water pump bearings using ensemble classifier

Irfan

Althobiani

Alwadie

et al. 2022

Advances in Mechanical Engineering

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The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% through MCA combination with machine learning and CNN algorithm was reported which is very low. The key factors of low accuracies were identified as low amplitudes of the harmonics in the MCA spectrum, the magnitude of environmental noise, and utilization of conventional feature extraction techniques. This problem has been tackled in this paper by developing a novel feature extractor (NFE) that extracts powerful features from the integrated current and voltage sensors data. The NFE has been derived using the threshold-based decision mechanism which has the capability to identify the location of the feature harmonic, feature extraction, measure the amplitude of the fault component, and compare it with the derived threshold. The experimental data has been collected for the bearing balls (BB), bearing cage (BC), inner race (IR) and the outer race (OR) faults, and the performance of the NFE has been tested on an ensemble classifier (CatBoost) and the better classification accuracy (99.2% for an individual feature and 100% with the combination of two or more features) of NFE has been achieved as compared to previously reported methods.

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“…Recently, I4.0 technologies has been further explored to develop its application into Total Productive Maintenance (TPM) practices in large manufacturing companies. In this study, benefits and barriers are highlighted related to the implementation of I4.0 into TPM that will be helpful for companies to develop their plan to employ digitalization of TPM (Tortorella et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Production and maintenance in industries: impact of industry 4.0

Kunju

Naveed

Anwar

et al. 2021

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Purpose Production industries are undergoing a digital transition, referred to as the fourth industrial revolution or Industry 4.0, as a result of rapidly expanding advances in information and communication technology. The purpose of this research is to provide a conceptual insight into the impact of unique capabilities from the fourth industrial revolution on production and maintenance tasks in terms of providing the existing production companies a boost by making recommendations on areas and tasks of great potential. Design/methodology/approach A survey and a literature review are among the research methods used in the research. The survey collected empirical data using a semi-structured questionnaire, which provided a broad overview of the company's present condition in terms of production and maintenance, resulting in more comprehensive and specific information regarding the study topics. Findings The study points out that, the implementation of I4.0-technology leads to an increase in production, asset utilization, quality, reduced machine down time in industries, and maintenance. Sensor technology, big data analysis, cloud technologies, mobile end devices, and real-time location systems are now being implemented to improve production processes and boost organizational competitiveness. Moreover, the study highlights that data acquired throughout the production process is utilized for quality control, predictive maintenance, and automatic production control. Furthermore, I4.0 solutions help companies to be more efficient with assets at each stage of the process, allowing them to have a stronger control on inventories and operational-optimization potential. Originality/value The findings of the study was supported by empirical data collected through survey that provides an intangible understanding of the importance of distinctive capabilities from the I4.0 revolution on production and maintenance tasks. In this study, some recommendations and guidelines to enhance these tasks are provided that are vital for existing production companies.

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Integration of Industry 4.0 technologies into Total Productive Maintenance practices

Cited by 105 publications

References 87 publications

The impact of Industry 4.0 on the relationship between TPM and maintenance performance

The impact of Industry 4.0 on the relationship between TPM and maintenance performance

Condition monitoring of water pump bearings using ensemble classifier

Production and maintenance in industries: impact of industry 4.0

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