Variable-Scale Clustering Based on the Numerical Concept Space

Wang, Ai; Gao, Xuedong; Yang, Minghan

doi:10.1007/978-981-15-5682-1_20

Cited by 1 publication

(2 citation statements)

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“…On the other hand, the value space (VS) storages all the possible management object values under each analysis hierarchy (scale). Wang et al [ 23 ] improved the scale space model by proposing the maximum value regulation on the value space, which enables the model to represent temporal observation scale for solving time-sensitive decision-making problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to maintain the consistency of scale transformation process, the single-strategy principle [ 23 ] announces that only one-type scale transformation strategy is allowed to be utilized during once scale transformation of the multiscale data model. Since the customized production model pushes intelligent manufacturing enterprises to adopt a more refined cost management solution, the conservative scale transformation strategy would be taken in our research (see Section 3.2 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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A Variable-Scale Data Analysis-Based Identification Method for Key Cost Center in Intelligent Manufacturing

Wang

Gao

2022

Computational Intelligence and Neuroscience

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%