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On-line Access: 2021-05-12

Received: 2020-10-17

Revision Accepted: 2020-12-31

Crosschecked: 2021-04-25

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Tao Peng


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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.5 P.382-395


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

Author(s):  Hao-nan Wang, Qi-qi He, Zheng Zhang, Tao Peng, Ren-zhong Tang

Affiliation(s):  State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   tao_peng@zju.edu.cn

Key Words:  Value stream mapping (VSM), Root cause analysis, Automated decision-making, Lean production tools, Industry 4.0

Hao-nan Wang, Qi-qi He, Zheng Zhang, Tao Peng, Ren-zhong Tang. Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm[J]. Journal of Zhejiang University Science A, 2021, 22(5): 382-395.

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T1 - Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm
A1 - Hao-nan Wang
A1 - Qi-qi He
A1 - Zheng Zhang
A1 - Tao Peng
A1 - Ren-zhong Tang
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000480

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 decision-making 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.


创新点:1. 提出了一个利用VSM来激发传统精益工具活力的系统框架.它解决了传统VSM在动态复杂环境下需要持续的现场调查和基于经验的响应,从而影响决策效率的问题.2. 提出了一个随着零部件加工逐步生成的能够反映动态生产现场的VSM.3. 使用决策树算法来进行根本原因的自动化分析,提高了生产现场的决策效率.
方法:1. 讨论VSM在自动化根本原因分析中的必要性和局限性,并对精益生产工具进行文献综述,特别是在工业4.0中基于VSM和VSM的决策.2. 提出系统性框架(该框架遵循数据-信息-知识层次模型),演示如何在生产车间中收集数据,并将其处理为信息,然后解释为知识.3. 以中国某家具制造企业为例,介绍框架的执行过程并采用决策树算法对根本原因进行自动分析.
结论:1. 自动化VSM可以很好地利用生产数据,满足多品种、小批量生产的要求,并能及时识别和分析现场的浪费.2. 框架可以作为一个指导方针,激发其他精益工具在工业4.0环境下的活力,以便进行可靠和有效的决策.


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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