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

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 enterprise...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022, Vol.2022, p.1897298-10
Main Authors: Wang, Ai, Gao, Xuedong
Format: Article
Language:English
Subjects:
Activity based costing
Business competition
Cost analysis
Data analysis
Data models
Decision making
Direct labor costs
Efficiency
Energy consumption
Identification methods
Intelligent manufacturing systems
Internet of Things
Iron
Literature reviews
Management decisions
Manufacturing
Methods
Performance assessment
Problem solving
Production methods
Steel production
Three dimensional models
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/1897298