Supplier selection: A hybrid model using DEA, decision tree and neural network
Supplier selection: A hybrid model using DEA, decision tree and neural network
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DOI：
10.1016/j.eswa.2008.12.039
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发表时间：
2009-07
期刊：
影响因子：
--
通讯作者：
D. Wu
中科院分区：
文献类型：
--
作者：
D. Wu
供应商评价与选择问题作为采购管理的重要职责，一直受到实务界和研究者的高度关注。这一管理决策是一个挑战，因为它涉及复杂性和各种标准。本文提出了一种基于数据包络分析（DEA）、决策树（DT）和神经网络（NN）的供应商绩效评价混合模型。该模型由两个模块组成：模块1应用DEA，并根据所得的效率得分将供应商分为有效和无效的集群。模块2利用企业绩效相关数据训练DT，NN模型，并将训练后的决策树模型应用于新供应商。我们的结果产生了良好的分类和预测准确率。
As the most important responsibility of purchasing management, the problem of vendor evaluation and selection has always received a great deal of attention from practitioners and researchers. This management decision is a challenge due to the complexity and various criteria involved. This paper presents a hybrid model using data envelopment analysis (DEA), decision trees (DT) and neural networks (NNs) to assess supplier performance. The model consists of two modules: Module 1 applies DEA and classifies suppliers into efficient and inefficient clusters based on the resulting efficiency scores. Module 2 utilizes firm performance-related data to train DT, NNs model and apply the trained decision tree model to new suppliers. Our results yield a favorable classification and prediction accuracy rate.