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The International Journal of Advanced Manufacturing Technology
Published in: The International Journal of Advanced Manufacturing Technology 1-2/2022

31-01-2022 | ORIGINAL ARTICLE

Hybrid big data analytics and Industry 4.0 approach to projecting cycle time ranges

Authors: Toly Chen, Yu-Cheng Wang

Published in: The International Journal of Advanced Manufacturing Technology | Issue 1-2/2022

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Abstract

This study proposes a hybrid big data analytics and Industry 4.0 (BD-I4) approach to enhancing the effectiveness of cycle time range projections for factory jobs. As a joint application of big data analytics and Industry 4.0, the BD-I4 approach is distinct from existing methods in this field. In this approach, each expert first constructs a fuzzy deep neural network to project the cycle time range of a job, an application of big data analytics (i.e., deep learning). Subsequently, the fuzzy weighted intersection operator is applied to aggregate the projected cycle times such that unequal authority levels can be considered, an application of Industry 4.0 (i.e., artificial intelligence). Applying the BD-I4 approach to a real case that the proposed methodology improved the projection precision by up to 72%, suggesting that instead of relying on a single expert, collaboration among multiple experts may be more effective and efficient.
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Appendix
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Metadata
Title
Hybrid big data analytics and Industry 4.0 approach to projecting cycle time ranges
Authors
Toly Chen
Yu-Cheng Wang
Publication date
31-01-2022
Publisher
Springer London
Published in
The International Journal of Advanced Manufacturing Technology / Issue 1-2/2022
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-022-08733-z

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