A two-phased semantic optimization modeling approach on supplier selection in eProcurement

摘要

The eProcurement planning is crucial to reduce purchase cost while selecting the right suppliers and it contributes to improve corporate competitiveness. This eProcurement planning research describes a framework for the integration of a knowledge-based system capable of identifying a goal model from a Primitive Model. The Primitive Model is screened by the screening factors reflecting the purchase strategy. As a result, by using the framework for supplier selection and allocation (SSA), a purchaser is able to reduce the costs and time required to select the right suppliers and to alleviate anxiety for ‘out-of-favor’ suppliers. This approach is based on two-phased semantic optimization model modification that semantically builds a goal model through model identification and candidate supplier screening based on model identification rules and supplier screening rules. This approach contributes significantly to construction of an optimization model from the perspective of model management and it provides a useful environment for efficient eProcurement from the perspective of a purchaser.