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IoT-enabled dynamic lean control mechanism for typical production systems

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Abstract

The emergence and subsequent popularization of lean has been one of the most significant developments in the history of operations management. However, there is a lack of systematic theory on the control framework underlying lean production. It is therefore difficult to conduct more in-depth research on Lean theory, specifically in the context of emerging technologies as smart manufacturing or Industry 4.0. In this study, process control theory is used to re-define several major lean methods and tools. Then a Lean-Oriented Optimum-State Control Theory (L-OSCT) is proposed that integrates these lean methods and tools into optimum-state control theory. On the level of method and mechanism, we adopt a recently emerged synchronization approach to obtain global-wide leanness of a large-scale system. L-OSCT provides dynamic process control in industrial networking systems. At last, a case study in a large-size paint making company in China is used to validate the effectiveness of the approach.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (51475095, 51875251), Natural Science Foundation of Guangdong Province (2016A030311041, 2017A030313401), 2015 Guangdong Special Support Scheme (2014TQ01X706), the National Ministry of Education “Blue Fire Plan” (Huizhou) Industry-Academia-Research Joint Innovation Fund (2018–2021) and the Fundamental Research Funds for the Central Universities (11618401).

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Authors and Affiliations

  1. School of Management, Jinan University, Guangzhou, 510632, China

    Kai Zhang & Congdong Li

  2. School of Electrical and Information Engineering, Jinan University (Zhuhai Campus), Zhuhai, 519070, China

    Ting Qu & Matthias Thürer

  3. Institute of Physical Internet, Jinan University (Zhuhai Campus), Zhuhai, 519070, China

    Kai Zhang, Ting Qu, Matthias Thürer, Yang Liu, Congdong Li & George Q. Huang

  4. Guangdong CIMS Provincial Key Lab, Guangdong University of Technology, Guangzhou, 510006, China

    Dajian Zhou

  5. Department of Management and Engineering, Linköping University, 581 83, Linköping, Sweden

    Yang Liu

  6. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Duxian Nie

  7. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China

    George Q. Huang

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  1. Kai Zhang
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Correspondence to Ting Qu.

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Zhang, K., Qu, T., Zhou, D. et al. IoT-enabled dynamic lean control mechanism for typical production systems. J Ambient Intell Human Comput 10, 1009–1023 (2019). https://doi.org/10.1007/s12652-018-1012-z

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