主成分分析
小贩
采购
计算机科学
选择(遗传算法)
组分(热力学)
过程(计算)
装瓶线
集合(抽象数据类型)
运筹学
业务
营销
机器学习
数学
人工智能
工程类
机械工程
物理
瓶子
热力学
操作系统
程序设计语言
作者
Alberto Petroni,Marcello Braglia
标识
DOI:10.1111/j.1745-493x.2000.tb00078.x
摘要
SUMMARY Purchasing managers need to periodically evaluate supplier performance in order to retain those suppliers which meet their requirements in terms of several performance criteria. The evaluation element typically consists of identifying the attributes, criteria, or factors relevant to the decision and then measuring or rating each vendor by considering each of the relevant factors. A critical part of the overall supplier selection process is the determination of the relative importance of each of the factors. This article presents an alternative decision model to evaluate the relative performance of suppliers that have multiple outputs and inputs. This approach is based on a multivariate statistical method, principal component analysis, that uses information obtained from eigenvalues to combine different ratio measures defined by every input and every output. The method has been employed to aggregate multiple performance measures for a real‐world data set of suppliers of a medium‐sized firm operating in the bottling machinery industry.
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