The Analysis of Performance Evaluation and Clustering Algorithm of Construction Industry

• Main Authors: Yueh-Ling Lee, 李悅綾
• Other Authors: Ching-Hwang Wang
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/7xv4ze

碩士 === 國立臺灣科技大學 === 營建工程系 === 100 === Making good use of performance assessment can increase company competitive advantage by providing self-evaluation and ranking and improving business strategy. This study first chooses representative evaluation indicators that apply to the construction industry characteristics and uses principal component analysis method to decide the weighting. This computation model of performance evaluation is built on weighting method and assisted by rating scale concept to create a reliable evaluation standard. Subsequently, this model categorizes evaluation indicators characteristics into four clusters by novel clustering algorithm and studies each cluster’s traits to provide performance evaluation accordingly. Finally, the combination of computation model and novel clustering algorithm results forms this research’s performance evaluation model. The rating scale above 69.08 can be characterized as active capital management, good inventory turnover and good profit margin. The rating scale between 49.91 and 69.08 can be characterized as conservative capital management, fine inventory turnover and fine profit margin. The rating scale between 30.04 and 49.91 can be characterized as high financial leverage, acceptable inventory turnover and acceptable profit margin. The rating scale below 30.04 can be characterized as poor capital management, poor inventory turnover and poor profit margin. The scaling model in this research can provide detailed indicator component and performance evaluation of the assessed company.