Literature review of Industry 4.0 and related technologies

Öztemel, Ercan; Gürsev, Samet

doi:10.1007/s10845-018-1433-8

Cited by 1,489 publications

(919 citation statements)

References 413 publications

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“…necessary for linking the strategic and operational levels [55], expressed in terms of configuration attributes that, in turn, allows the establishment of the documents that promote proper manufacturing execution and, with this, efficient performance [56]. If we consider that the strategic-operational alignment factor affects the performance of a manufacturing organization [57], then it becomes necessary to take it into account in order to reach high levels of sustainability (by avoiding poor efficiency in both managerial and transformation processes). Table 1 summarizes the contribution of the authors presented in this literature review.…”

Section: Sustainability and Manufacturing Efficiencymentioning

confidence: 99%

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A Comprehensive Framework for the Analysis of Industry 4.0 Value Domains

Martínez-Olvera

Mora-Vargas

2019

Sustainability

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Industry 4.0 aims to ensure the future competitiveness of the manufacturing industry by providing Companies with the ability to react to rapid product changes and disturbances, efficiently and reliably, through re-configurability. In this paper, we explore the value creation process within Industry 4.0, with special emphasis on its relationship with mass customization and the sustainability issue. Based on the identified research gaps and opportunities derived from a literature review of relevant concepts, we propose the development of the Customer-Product-Process-Resource (CPPR) 4.0, a comprehensive framework that puts the value proposition-creation-capture cycle proper of an Industry 4.0 environment, in the context of a manufacturing organization’s customer-product-process-resources views. The usefulness of the proposed framework is exemplified by using it to derive system dynamics model of the mass customization paradigm. A discussion of the managerial implications of the obtained results for both the sustainability and the case of Small-to-Medium Enterprises (SMEs) is offered at the end of the paper.

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Section: Sustainability and Manufacturing Efficiencymentioning

confidence: 99%

“…(1) Sustainability is understood as a potential benefit of the implementation of the Industry 4.0 paradigm, the use of high efficiency systems being its main sustainability feature, according to Reference [57]. From here comes the need to establish the achievement of sustainability in the context of manufacturing efficiency.…”

Section: Research Featuresmentioning

confidence: 99%

A Comprehensive Framework for the Analysis of Industry 4.0 Value Domains

Martínez-Olvera

Mora-Vargas

2019

Sustainability

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“…Internet of thinks (IoT), for instance, can be viewed as an important class of CPS where physical objects are interconnected in a network with identified address [5]. Besides Industry 4.0 and related technologies [6], applications of CPS include social robots for educational purposes [7], medical services and healthcare [8], and many other fields.…”

Section: Introductionmentioning

confidence: 99%

Reduced Dilation-Erosion Perceptron for Binary Classification

Valle

2020

Mathematics

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Dilation and erosion are two elementary operations from mathematical morphology, a non-linear lattice computing methodology widely used for image processing and analysis. The dilation-erosion perceptron (DEP) is a morphological neural network obtained by a convex combination of a dilation and an erosion followed by the application of a hard-limiter function for binary classification tasks. A DEP classifier can be trained using a convex-concave procedure along with the minimization of the hinge loss function. As a lattice computing model, the DEP classifier assumes the feature and class spaces are partially ordered sets. In many practical situations, however, there is no natural ordering for the feature patterns. Using concepts from multi-valued mathematical morphology, this paper introduces the reduced dilation-erosion (r-DEP) classifier. An r-DEP classifier is obtained by endowing the feature space with an appropriate reduced ordering. Such reduced ordering can be determined using two approaches: One based on an ensemble of support vector classifiers (SVCs) with different kernels and the other based on a bagging of similar SVCs trained using different samples of the training set. Using several binary classification datasets from the OpenML repository, the ensemble and bagging r-DEP classifiers yielded in mean higher balanced accuracy scores than the linear, polynomial, and radial basis function (RBF) SVCs as well as their ensemble and a bagging of RBF SVCs.

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“…The digitization introduced around the 1970s helped use advanced electronics and information technology for process automation, which formed the era of Industry 3.0. The current (r)evolution, termed Industry 4.0, is driven by the emergence of concepts, such as cloud-computing, smart sensors, internet of things (IoT), big data analytics, augmented reality, and human-machine interfaces [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

et al. 2020

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Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

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Literature review of Industry 4.0 and related technologies

Cited by 1,489 publications

References 413 publications

A Comprehensive Framework for the Analysis of Industry 4.0 Value Domains

A Comprehensive Framework for the Analysis of Industry 4.0 Value Domains

Reduced Dilation-Erosion Perceptron for Binary Classification

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

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