Journal ArticleDOI
Data-driven smart manufacturing
TLDR
The role of big data in supporting smart manufacturing is discussed, a historical perspective to data lifecycle in manufacturing is overviewed, and a conceptual framework proposed in the paper is proposed.About:
This article is published in Journal of Manufacturing Systems.The article was published on 2018-07-01. It has received 937 citations till now. The article focuses on the topics: Big data & Cloud computing.read more
Citations
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Industry 4.0 technologies: Implementation patterns in manufacturing companies
TL;DR: The findings show that Industry 4.0 is related to a systemic adoption of the front-end technologies, in which Smart Manufacturing plays a central role, and the implementation of the base technologies is challenging companies, since big data and analytics are still low implemented in the sample studied.
Journal ArticleDOI
The expected contribution of Industry 4.0 technologies for industrial performance
Lucas Santos Dalenogare,Guilherme Brittes Benitez,Néstor Fabián Ayala,Alejandro Germán Frank +3 more
TL;DR: In this article, the authors studied how the adoption of different Industry 4.0 technologies is associated with expected benefits for product, operations and side-effects aspects in the Brazilian industry.
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Industry 4.0, digitization, and opportunities for sustainability
TL;DR: In this paper, the authors present a systematic analysis of the sustainability functions of Industry 4.0, including energy sustainability, harmful emission reduction, and social welfare improvement, and show that sophisticated precedence relationships exist among various sustainability functions.
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Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems
TL;DR: The Industry 4.0 environment is scanned on this paper, describing the so-called enabling technologies and systems over the manufacturing environment.
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A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs)
TL;DR: In this paper, the authors present features that are characteristic for SMEs and identify research gaps needed to be addressed to successfully support manufacturing SMEs in their progress towards Industry 4.0.
References
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Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
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Beyond the hype
Amir H. Gandomi,Murtaza Haider +1 more
TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.
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Big Data: A Survey
Min Chen,Shiwen Mao,Yunhao Liu +2 more
TL;DR: The background and state-of-the-art of big data are reviewed, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid, as well as related technologies.
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The rise of big data on cloud computing
Ibrahim Abaker Targio Hashem,Ibrar Yaqoob,Nor Badrul Anuar,Salimah Binti Mokhtar,Abdullah Gani,Samee U. Khan +5 more
TL;DR: The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced, and research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance.
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Remaining useful life estimation - A review on the statistical data driven approaches
TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.