Applying Industrial Internet of Things Analytics to Manufacturing

Chun-Ho Wu; Stephen Chi-Hung Ng; Keith Chun-Man Kwok; Kai Leung Yung

doi:10.3390/machines11040448

Applying Industrial Internet of Things Analytics to Manufacturing

Chun-Ho Wu, Stephen Chi-Hung Ng, Keith Chun-Man Kwok, Kai Leung Yung

Research output: Journal article publication › Journal article › Academic research › peer-review

2 Citations (Scopus)

Abstract

The proliferation of Industry 4.0 (I4.0) technologies has created a new manufacturing landscape for manufacturing, requiring that companies follow I4.0 trends to stay competitive. However, in this novel digital automated environment, these companies must also ensure that lean manufacturing principles are upheld. This study proposes a data-driven framework for analysing raw data across
machines in manufacturing systems that can provide a comprehensive understanding of idle time and facilitate adjustments to reduce defect rates. This framework offers an alternative approach to improving manufacturing processes that involves utilising the power of I4.0 technologies in conjunction with lean manufacturing principles. This study’s examination of unprocessed data also provides guidance on improving legislation. The findings of this study provide direction for future research in the field of manufacturing and offer useful advice to businesses wishing to integrate I4.0 technologies into their operations

Original language	English
Article number	448
Number of pages	18
Journal	Machines
Volume	11
Issue number	4
DOIs	https://doi.org/10.3390/machines11040448
Publication status	Published - Apr 2023

Keywords

Industry 4.0
data-driven analytics
idle time
industrial Internet of Things
smart factory

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science (miscellaneous)
Mechanical Engineering
Control and Optimization
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

More information

10.3390/machines11040448

Cite this

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title = "Applying Industrial Internet of Things Analytics to Manufacturing",

abstract = "The proliferation of Industry 4.0 (I4.0) technologies has created a new manufacturing landscape for manufacturing, requiring that companies follow I4.0 trends to stay competitive. However, in this novel digital automated environment, these companies must also ensure that lean manufacturing principles are upheld. This study proposes a data-driven framework for analysing raw data acrossmachines in manufacturing systems that can provide a comprehensive understanding of idle time and facilitate adjustments to reduce defect rates. This framework offers an alternative approach to improving manufacturing processes that involves utilising the power of I4.0 technologies in conjunction with lean manufacturing principles. This study{\textquoteright}s examination of unprocessed data also provides guidance on improving legislation. The findings of this study provide direction for future research in the field of manufacturing and offer useful advice to businesses wishing to integrate I4.0 technologies into their operations",

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Applying Industrial Internet of Things Analytics to Manufacturing

Abstract

Keywords

ASJC Scopus subject areas

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