基于数据挖掘的油田企业供应商选择

选择合适的供应商,有效控制油田企业成本,是油田企业提升盈利水平、持续健康发展的重中之重。在大数据时代下,伴随着供应链全球化的趋势日益增强,供应商的数量愈加巨大,以往对供应商的选择方式已经不适用于企业科学有效地选择供应商的需求。文章基于数据挖掘的理念,研究某油田企业现有的某一领域供应商评价指标分值,并以此作为数据样本,应用聚类分析中的K-means算法对供应商评价指标分值进行聚类分析。将供应商进行分类,挖掘供应商评价指标分值的分布特点及规律,构建适合油田企业的科学、规范、高效的供应商选择体系,摒弃传统选择方式无法避免的主观性、随机性和偶然性等弊端,为油田企业决策者挑选最合适的供应商作为战略合作伙伴提供了科学、有效的依据。

Supplier Selection of Oilfield Enterprises Based on Data Mining

Choosing the appropriate supplier and controlling the cost of oilfield enterprises effectively are the most important task for oilfield enterprises to improve their profitability and achieve sustainable and healthy development.In the era of big data,with the growing trend of supply chain globalization and the increasing number of suppliers,past selection of suppliers is no longer suitable for enterprises to select suppliers scientifically and effectively.Based on the concept of data mining,we study existing evaluation index scores of a certain field of an oilfield enterprises,and taking it as the data sample,apply the K-means algorithm in cluster analysis to analyze supplier evaluation index score.Categorizing suppliers and excavating the distribution characteristics and regularity of supplier evaluation index score,we establish a scientific,standardized and efficient supplier selection system suitable for oilfield enterprises.Therefore,it can avoid the inevitable disadvantages of the subjectivity,randomness and contingency in traditional selection methods,and provide scientific and effective basis for oilfield enterprise decision makers to select the most appropriate supplier as a strategic partner.