The Open Cybernetics & Systemics Journal

2014, 8 : 513-518
Published online 2014 December 31. DOI: 10.2174/1874110X01408010513
Publisher ID: TOCSJ-8-513

Reduction and Optimization of Supplier Risk Indicators Based on Rough Set

Hongjun Guan and Aiwu Zhao
School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan Shandong, 250014, China.

ABSTRACT

The determination of supplier risk indicators is complex. Using vast data from SAP system of the enterprise, risk warning indicators can be reduced and optimized by the method of rough set. First of all, extract historical data from SAP system, and determine the discrete rules as excellent, good, moderate, and poor for each risk indicators to construct knowledge set which can be used for rough set operation. Then, using rough set theory to divide decision attribute set into equivalence classes, reduce non essential attributes, and calculate the dependence and importance degree for each essential attributes. After the normalization for all essential attributes, the reduced and optimized indicators for supplier risk evaluation system can be reached.

Keywords:

Attribute reduction, discretization, optimization, rough set, supplier risk.