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

Ai Wang1, Xuedong Gao1

  • 1University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces novel methods for enhancing cost management in intelligent manufacturing. It proposes dynamic standard cost updates and key cost center identification to improve enterprise efficiency and decision-making.

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Area of Science:

  • Manufacturing Engineering
  • Management Science
  • Data Science

Background:

  • Intelligent manufacturing offers competitive advantages but is hindered by inadequate management capacity.
  • Cost management is a critical challenge for enterprises adopting intelligent manufacturing technologies.

Purpose of the Study:

  • To address the cost management capacity limitations in intelligent manufacturing.
  • To develop a multiscale cost data model and dynamic standard cost updating mechanism.
  • To propose new methods for identifying key cost centers.

Main Methods:

  • Established a multiscale cost data model based on a three-dimensional cost system (actual, standard, testing costs).
  • Proposed a dynamic updating mechanism for standard costs using scale transformation theory.
  • Developed key cost center identification methods: KCCI_PPA for production performance assessment and KCCI_BDM for business decision-making.

Main Results:

  • The proposed methods overcome limitations of subjective scale determination in traditional variable-scale data analysis.
  • Experimental validation using industrial and enterprise datasets confirmed the efficiency and accuracy of KCCI_PPA and KCCI_BDM.
  • Demonstrated improved cost management capacity for intelligent manufacturing enterprises.

Conclusions:

  • The developed multiscale cost data model and dynamic updating mechanisms enhance cost management in intelligent manufacturing.
  • The KCCI_PPA and KCCI_BDM methods provide objective and accurate identification of key cost centers.
  • Implementing these strategies can significantly improve the effectiveness of intelligent manufacturing adoption.