Mining pattern of supplier with the methodology of domain-driven data mining

Supplier selection has a critical effect on the competitiveness of the entire supply chain network. It is not only a significant work in supply chain management but also a complex decision making problem which includes both qualitative and quantitative factors. Research results indicate that the supplier selection process appears to satisfy different evaluation criteria and business model in deciding the success of the supply chain. Supplier selection problem related to organization strategy and it needs more critical analysis. This paper proposes a novel approach that combines expert domain knowledge with Apriori algorithm of data mining to discover the pattern of supplier under the methodology of Domain-Driven Data Mining (D3M). Apriori algorithm of data mining with the help of Intuitionistic Fuzzy Set Theory (IFST) is employed during the process of mining. The overall patterns obtained help in deciding the final selection of suppliers. Finally, AHP is used to efficiently tackle both quantitative and qualitative decision factors involved in ranking of suppliers with the help of pattern achieved. An example searching for pattern of supplier is used to demonstrate the effective implementation procedure of proposed method. The proposed method can provide the guidelines for the decision makers to effectively select their suppliers in the current competitive business scenario.

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