Smart manufacturing must embrace big data

By Andrew Kusiak. Nature, 2017

544 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/9e6aee8a492c009c91bbc52a7bc0e4d481dc2d4a

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In Industrial and Manufacturing Engineering

Citations in the corpus

Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing (2019, background): “These include generic technologies such as the Industrial Internet, cloud platforms, and industrial big data [12,51,52,71,79,90], as well as technologies that are required to achieve system integration, management, and decision-making, such as enterprise-level intelligent decision-making technologie”
The internet of things for smart manufacturing: A review (2019, background): “The “ smartness ” level depends, to a great extent, on data-driven innovations that (Kusiak, 2017; Smart Manufacturing Leadership Council, 2018): enable all information about the manufacturing process to be available whenever it is needed, wherever it is needed, and in an easily comprehensible form…”
Toward New-Generation Intelligent Manufacturing (2018, background): “…and rapid changes in the information environment, there has been an accelerating development of strategic break-throughs in new-generation AI technologies; these include big data intelligence, human-machine hybrid-augmented intelligence, crowd intelligence, and cross-media intelligence [24,40,41].”
Literature review of Industry 4.0 and related technologies (2018, unknown): “Both Kusiak et al. (2010) and Francis and Kusiak (2017) studied predictive models, neural networks, autoregressive integrated moving average.”
Digital Twin in Industry: State-of-the-Art (2019, background): “Big data analytics can make full use of the data to discover failure causes, streamline a supply chain, optimize product performance, and enhance the production efficiency [1].”
A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions (2019, background): “As highlighted by Kusiak (2017), it is important to extract useful and valuable information from big data, and one of the most important methods in SM is BDA (Section 1).”
Smart manufacturing (2018, background): “Lu, Morris, and Frechette (2016) reported on the standards that may impact products, systems and business aspects of smart manufacturing.”
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, background): “Kusiak [108] highlights ﬁve gaps in manufacturing innovation in the digital transformation era: i.) adopt data strategies, ii.) improve data collection, use and sharing, iii.) design pre-353 dictive models, iv.) study general predictive models and v.) connect facto-354 ries and control processes.”
Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0 (2020, background): “(Kusiak 2017; Tao et al. 2018; Wang et al. 2018a) provided a literature analysis of data-driven smart manufacturing, citing representative references.”