Disruptive Maintenance Engineering 4.0

Navas, Miguel Angel; Sancho, Carlos; Carpio, José Manuel Murillo

doi:10.1108/ijqrm-09-2019-0304

Cited by 27 publications

(22 citation statements)

References 68 publications

(65 reference statements)

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“…As of the progression of DT-related studies, an emerging research stream in this cluster refers to the notion of disruptive maintenance from industry 4.0 where DT is combined with virtual reality, augmented reality and Big Data analytics to proactively generate data-driven predictive maintenance and enhance the monitor and control for unpredictable and remote tasks (Navas et al, 2020).…”

Section: Cluster 6: Dt-enabled Manufacturing Operations Managementmentioning

confidence: 99%

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Nguyen

Duong

Nguyen

et al. 2022

International Journal of Production Economics

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Section: Cluster 6: Dt-enabled Manufacturing Operations Managementmentioning

confidence: 99%

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Nguyen

Duong

Nguyen

et al. 2022

International Journal of Production Economics

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“…This process helps to prevent the unexpected, abrupt failure of equipment used in the manufacturing firm (Tiddens et al, 2020 ; Wakiru et al, 2021 ). Predictive maintenance techniques support management by providing the right information about the lifecycle of in-service equipment, which protects the process from impediments (Halim et al, 2021 ; Navas et al, 2020 ). This technique also optimizes the use of spare parts in the manufacturing unit.…”

Section: Theoretical Background and Development Of Conceptual Modelmentioning

confidence: 99%

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

et al. 2022

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Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms’ productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm’s predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

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“…This capability may enable industrial customers to produce spare parts by themselves, thus, enabling decentralization of the production and, consequently, reduction of the logistic cost and the environmental pollution. [8] Some authors [10][11][12] have investigated the introduction of the AM in the supply chain of spare parts production by evaluating the use feasibility and the entry challenges. Other works [13][14][15] have focused on optimization problems related to the optimum location and number of AM facilities.…”

Section: Spare Parts Productionmentioning

confidence: 99%

A Survey on the Integration between Additive Manufacturing and Maintenance

Abbate

Manco

Macchiaroli

et al. 2022

Macromolecular Symposia

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Industry 4.0 (I4.0) is an emerging paradigm that is affecting several industrial fields. Many innovations have been introduced to optimize and improve the production efficiency by means of new technologies. Additive manufacturing (AM) is an enabling technology of I4.0 that is leading to innovations related to the production systems, but companies are looking to AM also for maintenance purposes. The aim of this study is to find the existing innovations that link AM and maintenance and to discuss on their benefits and feasibility. A systematic literature review (SLR) approach has been used to this purpose. The outcome of the SLR highlights that there are three main fields in which AM technologies can be very useful for maintenance purposes.

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Disruptive Maintenance Engineering 4.0

Cited by 27 publications

References 68 publications

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

A Survey on the Integration between Additive Manufacturing and Maintenance

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