Are QM models aligned with Industry 4.0? A perspective on current practices

Asif, Muhammad

doi:10.1016/j.jclepro.2020.120820

Cited by 76 publications

(79 citation statements)

References 62 publications

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“…Industry 4.0 is characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrial product-service systems [28]. In this respect, industry 4.0 phenomenon is bringing unprecedented disruptions for all traditional production/ service systems and business models (value chains) and hotfooting the need for redesign and digitization of activities [29][30][31][32]. Tout ensemble is retrofitting and/or redefining the patterns of value creation and annexations, production networks, and supplier base and customer interfaces [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

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Section: Introductionmentioning

confidence: 99%

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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“…The studies reviewed do not consider Industry 4.0 a critical infrastructure sector from a state or societal perspective. However, the Industry 4.0 phenomenon offers opportunities and challenges to all business models (Asif, 2020;Ghadge et al, 2020). The available study identified 26 drivers associated with Industry 4.0 that impact improved business process management (BPM).…”

Section: The Relationship Between Industry 40 and Critical Infrastructurementioning

confidence: 99%

The Role of Integral Model of Critical Infrastructure Safety in Industry 4.0

Wiśniewski¹

2021

ERSJ

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Purpose: This paper aims to create a dedicated tool for managing the level of availability of services delivered by critical infrastructure (CI) facilities, which will affect the Sustainable Development Goals. The paper established a link between sustainability, Industry 4.0, and critical infrastructure. The literature analysis has indicated that the security of Industry 4.0 is discussed almost exclusively in the perspective of cyber security. Design/Methodology/Approach: Verification of the tool was carried out based on simulations and computational experience using data PKN ORLEN Refinery Inc. Findings: As a result of the work carried out, an Integral Model of Critical Infrastructure Safety (IMCIS) was obtained, which allows us to present CI's current characteristics. It was established that the canon of characterization of CI objects consists of a set of resources, a set of functionalities, a set of threats, a set of safeguards, a set of threat dependencies, and a set of dependencies of objects recognized as CI. Practical Implications: A universal application of the IMCIS is to estimate the level of risk before and after implementing safeguards and generating adverse event scenarios. The identified threats are the decision-making areas of the decision problem, whose solution is indicated by a set of safeguards reducing the risk level to an acceptable level. Originality/Value: The solution can be used to identify a set of safeguards that, on the one hand, will reduce the cost of the business operator to a minimum and, on the other hand, achieve the required level of availability of CI functionality. In addition, IMCIS allows analyzing all threats that may affect the infrastructure of Industry 4.0, taking into account many independent decision-making centers.

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“…It describes the relationship between all the segments of the known data, including the results from predictive models. A comparison of the features of Industry 4.0 against quality models and some recommendations for future quality models are proposed in [16].…”

Section: Predictive Analyticsmentioning

confidence: 99%

The Use of Artificial Intelligence in Industry 4.0

Tsochev¹,

Sharabov²

2020

PECR

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This article presents a brief overview of the effect of new technologies, how they are changing the manufacturing process, and how the machines are starting to get a lot smarter thanks to the artificial intelligence. The focus is over the examination of Industry 4.0 and how it revolutionized the whole manufacturing segment and what promise of a better, more efficient future it brings. This analysis focuses primarily on how artificial intelligence is integrated, what benefits it brings, and how big of an improvement it is over basic programming. Part of the research is based on 771 publications tracked over the past three to five years. Publications are within some of the well-known databases Scopus, Web of Science, and IEEE. We will examine the basic use case scenarios where AI is crucially needed and how a new generation of the factory can look and feel like a living human being. Keywords: Industry 4.0, artificial intelligence, predictive analytics, predictive maintenance, industrial robotics, computer vision.

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Are QM models aligned with Industry 4.0? A perspective on current practices

Cited by 76 publications

References 62 publications

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

The Role of Integral Model of Critical Infrastructure Safety in Industry 4.0

The Use of Artificial Intelligence in Industry 4.0

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