A performance evaluation of supply chain management based on factor clustering analysis

Yehui Dong, Jiawei Gao and Shanyin Yao

International Journal of Manufacturing Technology and Management, 2023, vol. 37, issue 3/4, 315-333

Abstract: In order to overcome the problems of low significance coefficient and low evaluation accuracy of current methods, a performance evaluation method of supply chain management based on factor clustering analysis was proposed. Firstly, SPSS software is used to standardise the data of influencing factors of supply chain management performance, and the factors that are not suitable for factor molecules are removed. Then, a factor analysis model was built, and the evaluated eigenvalue, eigenvalue contribution and cumulative contribution after model rotation were calculated. The clustering analysis of multiple common factors and the distance between samples were combined with the contribution calculation results. Finally, the correlation between supply chain management performance evaluation indicators is analysed, the weight of evaluation indicators is calculated, and the performance evaluation results are obtained. Experimental results show that the significance coefficient of the proposed method is always higher than 0.8, and the accuracy is up to 98%.

Keywords: factor analysis; cluster analysis; supply chain management; performance appraisal. (search for similar items in EconPapers)
Date: 2023
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