Extracting the Rules of KPIs for Equipment Management Based on Rough Set Theory

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Please use this identifier to cite or link to this item: http://nhuir.nhu.edu.tw/handle/987654321/23537

Title:	Extracting the Rules of KPIs for Equipment Management Based on Rough Set Theory
Authors:	王佳文;Wang, Jia Wen;Cheng, Ching Hsue
Contributors:	電子商務管理學系
Keywords:	Key Performance Indicators;Rough Set Theory (RST)
Date:	2011-08
Issue Date:	2015-10-05 17:09:00 (UTC+8)
Abstract:	This paper practically collects manufacturing supplier dataset in Taiwan. The dataset includes production records, and there are 18 attributes such as production scheduling, scheduled downtime, process, etc. For comparison, decision tree, naive bayesian, and multi-layer perceiving are utilized to compare with the proposed procedure in classification accuracy. The results show that the correct rate of rough set theory is not only superior to decision trees, naive bayesian, and multi-layer perceiving (MLP), and the proposed procedure can be easy to understand and produce fewer rules. In managerial implication, the results can generate predictive model, and classifiable rule which can help manufacturing to find out key related factors in equipment throughput and use the rules generated as a capacity planning assessment.
Relation:	Advanced Materials Research vol. 314-316 pp.2358-2361
Appears in Collections:	[ Department of Electronic Commerce Management] Periodical Articles

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