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2013 (Vol. 5, Issue: 19)
Article Information:

Production Performance Diagnosis of Manufacturing Workshop based on Fuzzy-Gray Correlation Degree Evaluation

Xu Wen-Jie, Yao Jin, Ni Yan-Ting and Li Jing-Min
Corresponding Author:  Xu Wen-Jie 

Key words:  Comprehensive evaluation, fuzzy-gray correlation degree, production performance diagnosis, production performance, rough set, ,
Vol. 5 , (19): 4685-4690
Submitted Accepted Published
September 26, 2012 November 11, 2012 May 10, 2013

The need for manufacturing enterprises to explore the shortcomings of their workshop production performance has become aggrandized as the fierce competition in manufacturing industry. The traditional evaluation of the production performance focused on the contrast of different enterprises. However, the problems of the worse workshops or enterprises are still unknown. Many manufacturing enterprises not only want to find out the competitive position of their workshop, but also want to find out the problems in their workshop and then improve them to enhance the production performance. This study built a Fuzzy-Gray Correlation Degree (FGCD) evaluation model based on rough set to aid enterprise to detect the problems in workshops. The FGCD model provided a comprehensive evaluation indicators system to present the competitiveness of the workshop from different views. In order to present an objective and accurate model for manufacturing enterprises, a combined empowerment method based on rough set theory for indicators has been applied. Finally, the case was used to support the available of production performance diagnosis of workshop. The model can determine the condition of production performance from different indicators and diagnose the corresponding workshop problems in manufacturing process.
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  Cite this Reference:
Xu Wen-Jie, Yao Jin, Ni Yan-Ting and Li Jing-Min, 2013. Production Performance Diagnosis of Manufacturing Workshop based on Fuzzy-Gray Correlation Degree Evaluation.  Research Journal of Applied Sciences, Engineering and Technology, 5(19): 4685-4690.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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