Publication Type
Journal Article
Version
publishedVersion
Publication Date
9-2015
Abstract
Analytics can be applied in procurement to benefit organizations beyond just prevention and detection of fraud. This study aims to demonstrate how advanced data mining techniques such as text mining and cluster analysis can be used to improve visibility of procurement patterns and provide decision-makers with insight to develop more efficient sourcing strategies, in terms of cost and effort. A case study of an organization’s effort to improve its procurement process is presented in this paper. The findings from this study suggest that opportunities exist for organizations to aggregate common goods and services among the purchases made under and across different prescribed procurement approaches. It also suggests that these opportunities are more prevalent in purchases made by individual project teams rather than across multiple project teams.
Keywords
procurement, text mining, clustering, data analytics, fraud detection.
Discipline
Management Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Information Systems and Management
Publication
International Journal of Advanced Computer Science and Applications
Volume
6
Issue
8
First Page
70
Last Page
80
ISSN
2158-107X
Identifier
10.14569/IJACSA.2015.060809
Publisher
SAI Organization / Science and Information (SAI) Organization
Citation
TAN, Melvin H. C. and LEE, Wee Leong.
Evaluation and Improvement of Procurement Process with Data Analytics. (2015). International Journal of Advanced Computer Science and Applications. 6, (8), 70-80.
Available at: https://ink.library.smu.edu.sg/sis_research/2913
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.14569/IJACSA.2015.060809
Included in
Management Information Systems Commons, Numerical Analysis and Scientific Computing Commons