Abstract

Enterprises in response to intense market competition must reduce their production costs. For the fastener business, because raw material costs accounted for the final product price more than 60%, it is necessary to control the cost of raw materials. And in recent years, price fluctuations in domestic and international steel market is very intense, which adds to the fastener raw material purchasing decisions difficult. In order to improve the quality and efficiency of the purchasing decision, we introduce the data mining methods on historical fastener orders in ERP data analysis to gain useful knowledge of raw materials purchasing decisions. This paper introduces the actual purchasing decision needs of fastener business, then introduced the classic data mining algorithms of association rules, and according to the actual work of enterprises improved Apriori algorithm, an optimization algorithm is proposed: BApriori, and through the experiment of a fastener actual data proved the effectiveness of the algorithm is BApriori. We based on the classic data mining standard: CRISP-DM, CWM and PMML, design and implement an association rule data mining platform Bminer, and apply it to the practice of a fastener.

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