One of important problems in supply chains management is supplier selection. In a company, there are massive data from various departments so that extracting knowledge from the company’s data is too complicated. Many researchers have solved this problem by some me
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One of important problems in supply chains management is supplier selection. In a company, there are massive data from various departments so that extracting knowledge from the company’s data is too complicated. Many researchers have solved this problem by some methods like fuzzy set theory, goal programming, multi objective programming, the liner programming, mixed integer programming, analytic hierarchy process (AHP), analytic network process model, TOPSIS, etc. Past research gaps are lack of attention to enterprise historical data and extract knowledge from them, review the past performance of suppliers and use effect of the their past performance to their future work. The aim of this paper is to solve supplier selection problem based on historical data by a novel model. The proposed model has tried to uncover hidden relation in massive unstructured industrial data and has used them to extract knowledge for optimizing decision making and predicting in supply chain management by BI tools. The model is based on FP-Growth algorithm integrated with AHP. Moreover, the proposed model is a multi-criteria decision making model (MCDM) with four criteria: quality, priority, delay on delivery and cost that have chosen from literature review. The criteria have been weighed by AHP and finally the model has been validated by industrial group’s historical data.
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