Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

Research on Suppliers' Performance Clustering Method with the Contracts Evaluation Data

Authors
Lidong Liu, Wengbing Chang, Shenghan Zhou
Corresponding Author
Lidong Liu
Available Online March 2017.
DOI
https://doi.org/10.2991/msam-17.2017.35How to use a DOI?
Keywords
clustering method; k-means; supplier performance
Abstract

This paper proposed suppliers' performance clustering method with the re-analysis on contracts evaluation data. The study summarizes the basic definitions of the equipment manufacturing industry evaluation methods. Then the k-means clustering method based on the data re-analysis was defined. Finally the study gives an empirical example to present the main process of the proposed clustering method with 422 contracts evaluation data. The result shows that it is feasible.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
Series
Advances in Intelligent Systems Research
Publication Date
March 2017
ISBN
978-94-6252-324-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-17.2017.35How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Lidong Liu
AU  - Wengbing Chang
AU  - Shenghan Zhou
PY  - 2017/03
DA  - 2017/03
TI  - Research on Suppliers' Performance Clustering Method with the Contracts Evaluation Data
BT  - Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
PB  - Atlantis Press
SP  - 156
EP  - 160
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-17.2017.35
DO  - https://doi.org/10.2991/msam-17.2017.35
ID  - Liu2017/03
ER  -