Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm

For efficient use of value stream mapping (VSM) for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies, a systematic framework of VSM to rejuvenate traditional lean tools is proposed. It addresses the issue that traditional VSM requires intensi...

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Bibliographic Details
Published in:Journal of Zhejiang University. A. Science 2021-05, Vol.22 (5), p.382-395
Main Authors: Wang, Hao-nan, He, Qi-qi, Zhang, Zheng, Peng, Tao, Tang, Ren-zhong
Format: Article
Language:English
Subjects:
Algorithms
Automation
Batch production
Civil Engineering
Classical and Continuum Physics
Decision analysis
Decision making
Decision trees
Engineering
Industrial Chemistry/Chemical Engineering
Information processing
Literature reviews
Mapping
Mechanical Engineering
Onsite
Root cause analysis
Waste identification
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Summary:For efficient use of value stream mapping (VSM) for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies, a systematic framework of VSM to rejuvenate traditional lean tools is proposed. It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience, which hinders decisionmaking efficiency in dynamic and complex environments. The proposed framework follows the data-information-knowledge hierarchy model, and demonstrates how data can be collected in a production workshop, processed into information, and then interpreted into knowledge. In this paper, the necessity and limitations of VSM in automated root cause analysis are first discussed, with a literature review on lean production tools, especially VSM and VSM-based decision making in Industry 4.0. An implementation case of a furniture manufacturer in China is presented, where decision tree algorithm was used for automated root cause analysis. The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis. The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.
ISSN:1673-565X
1862-1775
DOI:10.1631/jzus.A2000480