Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/105814
PIRA download icon_1.1View/Download Full Text
Title: Applying industrial internet of things analytics to manufacturing
Authors: Wu, CH
Ng, SCH
Kwok, KCM
Yung, KL 
Issue Date: Apr-2023
Source: Machines, Apr. 2023, v. 11, no. 4, 448
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.
Keywords: Data-driven analytics
Idle time
Industrial Internet of Things
Industry 4.0
Smart factory
Publisher: MDPI AG
Journal: Machines 
EISSN: 2075-1702
DOI: 10.3390/machines11040448
Rights: Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
The following publication Wu C-H, Ng SC-H, Kwok KC-M, Yung K-L. Applying Industrial Internet of Things Analytics to Manufacturing. Machines. 2023; 11(4):448 is available at https://doi.org/10.3390/machines11040448.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
machines-11-00448-v2.pdf4.05 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

8
Citations as of Jun 30, 2024

Downloads

3
Citations as of Jun 30, 2024

SCOPUSTM   
Citations

1
Citations as of Jul 4, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.