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

被引：0

作者：

Dong Y. ^{[1
]}

Gao J. ^{[1
]}

Yao S. ^{[1
]}

机构：

[1] Department of Economics and Management, Hunan Institute of Traffic Engineering, Heng Yang

来源：

International Journal of Manufacturing Technology and Management | 2023年 / 37卷 / 3-4期

关键词：

cluster analysis; factor analysis; performance appraisal; supply chain management;

D O I：

10.1504/IJMTM.2023.133473

中图分类号：

学科分类号：

摘要：

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%. Copyright © 2023 Inderscience Enterprises Ltd.

引用

页码：315 / 333

页数：18

共 16 条

[1]

Agrawal S.C., Jalal A.S., A joint cumulative distribution function and gradient fusion based method for dehazing of long shot hazy images, Journal of Visual Communication and Image Representation, 77, 11, pp. 103087-103092, (2021)

[2]

Aljawarneh N.M., Taamneh M.M., Alhndawi N., Alomari K.A.A., Masad F., Fog computing-based logistic supply chain management and organizational agility: the mediating role of user satisfaction, Uncertain Supply Chain Management, 9, 3, pp. 767-778, (2021)

[3]

Cao Y., Research on performance evaluation of agricultural products circulation supply chain based on BP neural network, Business Economics Research, 21, 11, pp. 134-137, (2021)

[4]

Farsi M., Bailly A., Bodin D., Penella V., Pinault P., An optimisation framework for improving supply chain performance: case study of a bespoke service provider, Procedia Manufacturing, 49, 2, pp. 185-192, (2020)

[5]

Jia Q.F., Simulation of back-end centralized redundant supply chain decision model, Computer Simulation, 37, 21, pp. 345-348, (2020)

[6]

Kaur A., Kumar Y., A new metaheuristic algorithm based on water wave optimization for data clustering, Evolutionary Intelligence, 20, 1, pp. 1-25, (2021)

[7]

Khorasani S.T., Cross J., Maghazei O., Lean supply chain management in healthcare: a systematic review and meta-study, International Journal of Lean Six Sigma, 11, 1, pp. 1-34, (2020)

[8]

Li C., Zeng J., Jin X, Chu W.W., Quality index evaluation model of MPP sold under the e-commerce platform, Mathematical Problems in Engineering, 20, 12, pp. 1-9, (2021)

[9]

Liu Z., Dang Y.G., Wei L., The performance evaluation model of university-industry cooperation based on improved grey incidence, Mathematics in Practice and Theory, 50, 9, pp. 1-9, (2020)

[10]

Mathu K., Supply chain management as a competitive advantage of fast moving consumer goods SMEs in South Africa, Journal of Energy and Natural Resources, 10, 1, pp. 33-2, (2021)

← 1 2 →