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  • 标题:A Variable-Scale Data Analysis-Based Identification Method for Key Cost Center in Intelligent Manufacturing
  • 本地全文:下载
  • 作者:Ai Wang ; Xuedong Gao
  • 期刊名称:Computational Intelligence and Neuroscience
  • 印刷版ISSN:1687-5265
  • 电子版ISSN:1687-5273
  • 出版年度:2022
  • 卷号:2022
  • DOI:10.1155/2022/1897298
  • 语种:English
  • 出版社:Hindawi Publishing Corporation
  • 摘要:Although the competitive advantages brought by intelligent manufacturing technology for enterprises have been preliminarily shown, a lack of matched management capacity still greatly limits its effect. This paper focuses on the cost management capacity problem of intelligent manufacturing enterprises. The multiscale cost data model is established on the basis of the three-dimensional cost system model, which contains actual cost, standard cost, and testing cost. According to the scale transformation theory, we propose the dynamic updating mechanism of standard cost. The key cost center identification methods, respectively, for the production performance assessment scenario (KCCI_PPA) and the business decision-making scenario (KCCI_BDM) are also put forward, which could overcome the subjective determination limitation of initial observation scale in the traditional variable-scale data analysis method. Experiments with both industrial statistical and enterprise real datasets verify the efficiency and accuracy of the proposed KCCI_PPA and KCCI_BDM method.
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