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A Classification Method of Inventory Spare Parts Based on Improved Super Efficient DEA-ABC Model

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Learning Technologies and Systems (SETE 2020, ICWL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12511))

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Abstract

Enterprises generally have the problem of high spare parts inventory costs at present. One of the main reasons for this problem is that the classification standard of spare parts inventory is single and the classification result is unreasonable. Based on the analysis of commonly used inventory spare parts classification methods, this paper proposes an improved ABC classification method based on super efficient DEA. It integrates the input-output efficiency of super efficient DEA into the supply chain of spare parts procurement and outbound use. In the process, the Delphi method is used to investigate the inventory management personnel, and the statistical results of the survey are added to super efficient DEA model as the weight restriction conditions, and than the improved super efficient DEA-ABC classification model was constructed. This model achieves a combination of subjective and objective, which increases the scientificity and practicality of the classification results. Finally, taking the inventory classification of subway spare parts in a certain city as an example, the effectiveness of the method is verified.

Supported by the National Natural Science Foundation of China (Grant No. 71771078) and the Social Science Research Project of Hebei Provincial Department of Education of China (Grand No. SD101020).

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Author information

Authors and Affiliations

  1. Hebei University of Science and Technology, Shijiazhuang, 050000, China

    Na Xu

  2. Shijiazhuang Rail Transit Co., Ltd., Shijiazhuang, 050000, China

    Wei Xu

Authors
  1. Na Xu
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  2. Wei Xu
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Corresponding author

Correspondence to Na Xu .

Editor information

Editors and Affiliations

  1. Ningbo Institute of Industrial Technology, Ningbo, China

    Chaoyi Pang

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Monash University, Clayton, VIC, Australia

    Guanliang Chen

  4. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania

    Elvira Popescu

  5. Department of Physics, Zhejiang University, Hangzhou, China

    Lu Chen

  6. School of Computer Science, South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Zhejiang University, Ningbo, China

    Bailing Zhang

  8. Universidad Internacional De La Rioja, Logroño, Spain

    Silvia Margarita Baldiris Navarro

  9. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

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Xu, N., Xu, W. (2021). A Classification Method of Inventory Spare Parts Based on Improved Super Efficient DEA-ABC Model. In: Pang, C., et al. Learning Technologies and Systems. SETE ICWL 2020 2020. Lecture Notes in Computer Science(), vol 12511. Springer, Cham. https://doi.org/10.1007/978-3-030-66906-5_20

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  • DOI: https://doi.org/10.1007/978-3-030-66906-5_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66905-8

  • Online ISBN: 978-3-030-66906-5

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