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Estimation of Lead Time in the RFID-Enabled Real-Time Shopfloor Production with a Data Mining Model

In: The 19th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Ray Y. Zhong

    (The University of Hong Kong)

  • George Q. Huang

    (The University of Hong Kong)

  • Qing-yun Dai

    (Guangdong University of Technology)

  • Tao Zhang

    (Huaiji Dengyun Auto Parts (Holding) Co., Ltd.)

Abstract

Lead time estimation (LTE) is difficult to carry out, especially within the RFID-enabled real-time manufacturing shopfloor environment since large number of factors may greatly affect its precision. This paper proposes a data mining approach with four steps each of which is equipped with suitable mathematical models to analysis the LTE from a real-life case and then to quantitatively examine its key impact factors such as processing routine, batching strategy, scheduling rules and critical parameters of specification. Experiments are carried out for this purpose and results imply that batching strategy, scheduling rules and two specification parameters largely influence the LTE, while, processing routine has less impact in this case.

Suggested Citation

  • Ray Y. Zhong & George Q. Huang & Qing-yun Dai & Tao Zhang, 2013. "Estimation of Lead Time in the RFID-Enabled Real-Time Shopfloor Production with a Data Mining Model," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), The 19th International Conference on Industrial Engineering and Engineering Management, edition 127, chapter 0, pages 321-331, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-38391-5_33
    DOI: 10.1007/978-3-642-38391-5_33
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    Cited by:

    1. Chuang Wang & Pingyu Jiang, 2019. "Deep neural networks based order completion time prediction by using real-time job shop RFID data," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1303-1318, March.

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