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

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...

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Bibliographic Details
Published inLearning Technologies and Systems Vol. 12511; pp. 214 - 224
Main Authors Xu, Na, Xu, Wei
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2021
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
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Summary: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.
Bibliography: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).
ISBN:303066905X
9783030669058
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-66906-5_20