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Seru: The Organizational Extension of JIT for a Super-Talent Factory

Author

Listed:
  • Kathryn E. Stecke

    (University of Texas at Dallas, USA)

  • Yong Yin

    (Yamagata University, Japan)

  • Ikou Kaku

    (Akita Prefectural University, Japan)

  • Yasuhiko Murase

    (Akita Prefectural University, Japan)

Abstract

The Toyota production system (TPS) or lean has long been regarded as a powerful approach for managing manufacturing factories. However, in the early 1990s, the TPS was found not to work when it was applied to Japanese electronics companies. TPS is fit for a stable, but not volatile, business environment such as that which the electronics industry belongs. This volatile environment can be described as one with short product life cycles, uncertain product types, and fluctuating production volumes (sometimes mass, sometimes batch, and sometimes very small volumes). Seru, a new production organization, was developed to cope with this environment. Many leading global companies such as Samsung, Sony, Canon, Panasonic, LG, and Fujitsu have adopted seru. Seru overcame a lot of disadvantages inherent in TPS and brought amazing benefits to seru users. For example: 1) Seru requires a much smaller workforce, 2) It can greatly reduce space requirements, and 3) It can reduce lead time, setup time, WIP inventories, finished-product inventories, and cost. This article introduces seru’s history and defines various seru types. A seru pyramid is constructed to compare seru with the TPS. A JIT organization system is introduced. The authors show why applying it can bring great productivity, efficiency, and flexibility to a production organization.

Suggested Citation

  • Kathryn E. Stecke & Yong Yin & Ikou Kaku & Yasuhiko Murase, 2012. "Seru: The Organizational Extension of JIT for a Super-Talent Factory," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 3(1), pages 106-119, January.
  • Handle: RePEc:igg:jsds00:v:3:y:2012:i:1:p:106-119
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    Citations

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    Cited by:

    1. Ye Wang & Jiafu Tang, 2022. "Optimized skill configuration for the seru production system under an uncertain demand," Annals of Operations Research, Springer, vol. 316(1), pages 445-465, September.
    2. Zhang, XiaoLi & Liu, ChenGuang & Li, WenJuan & Evans, Steve & Yin, Yong, 2017. "Effects of key enabling technologies for seru production on sustainable performance," Omega, Elsevier, vol. 66(PB), pages 290-307.
    3. Yu, Yang & Tang, Jiafu & Gong, Jun & Yin, Yong & Kaku, Ikou, 2014. "Mathematical analysis and solutions for multi-objective line-cell conversion problem," European Journal of Operational Research, Elsevier, vol. 236(2), pages 774-786.
    4. Yu, Yang & Tang, Jiafu & Sun, Wei & Yin, Yong & Kaku, Ikou, 2013. "Reducing worker(s) by converting assembly line into a pure cell system," International Journal of Production Economics, Elsevier, vol. 145(2), pages 799-806.
    5. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Chen, Jie, 2022. "A column generation-based exact solution method for seru scheduling problems," Omega, Elsevier, vol. 108(C).
    6. Chang Liu & Zhen Li & Jiafu Tang & Xuequn Wang & Ming-Jong Yao, 2022. "How SERU production system improves manufacturing flexibility and firm performance: an empirical study in China," Annals of Operations Research, Springer, vol. 316(1), pages 529-554, September.
    7. Zhang, Zhe & Song, Xiaoling & Huang, Huijung & Zhou, Xiaoyang & Yin, Yong, 2022. "Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong’s learning effect," European Journal of Operational Research, Elsevier, vol. 297(3), pages 866-877.
    8. Li, Dongni & Jiang, Yuzhou & Zhang, Jinhui & Cui, Zihua & Yin, Yong, 2023. "An on-line seru scheduling algorithm with proactive waiting considering resource conflicts," European Journal of Operational Research, Elsevier, vol. 309(2), pages 506-515.
    9. Zhe Zhang & Xiaoling Song & Huijun Huang & Yong Yin & Benjamin Lev, 2022. "Scheduling problem in seru production system considering DeJong’s learning effect and job splitting," Annals of Operations Research, Springer, vol. 312(2), pages 1119-1141, May.

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