Strategic supplier selection: A domain driven data mining methodology

The fierce competitions in supply chain are changing the conventional buyer-seller relationship, which leads to undermine business strengths. To respond this transformation, strategic partnership has been gaining increasing attention. However, recent research points out that it still lacks the methodology to optimize the existing suppliers that would help buyer establish long-term relationship. This paper proposes a two-phase Domain Driven Data Mining (DDDM) methodology for strategic supplier selection. Phase one is termed domain knowledge acquisition and analysis, while phase two is supplier set optimization. Meanwhile, Analytic Hierarchy Process (AHP), set theory and association rule mining are included in the two phases. Furthermore, a case study is conducted to demonstrate the proposed supplier selection model. Simulation results indicate that it, is more efficient than single AHP or association rule algorithm. In short, the proposed model employs both qualitative subjective judgments and quantitative objective data to select strategic suppliers. It optimizes the existing suppliers, which would provide the basis for the buyer establishing long-term relationships with fewer suppliers.