A Text Mining Based Approach for Mining Customer Attribute Data on Undefined Quality Problem
Understanding how the consumer perceives quality is a key issue in supply chain management However, as the market structure continues to deepen, traditional evaluation methods using SEVRQUAL are unable identify all issues related to customer quality and unable to supply solutions.The maturation of data mining technology, however, has opened the possibilities of mining customer attribute data on quality problems from unstructured data.Based on the consumer perspective,this research uses an unsupervised machine learning text mining approach and the Recursive Neural Tensor Network to resolve the attribution process for undefined quality problems.It was found that the consumer quality perception system has a typical line-of-sight that can assist consumers quickly capture the logical structure of the quality problem.Although attributions related to quality problems are very scattered, a highly unified view was found to exist within each group, and a strategy to solve the undefined quality problem was agreed through group consensus by 61% of the consumers.
text mining supply chain management quality control
Qing Zhu Yiqiong Wu Yuze Li Renxian Zuo
International Business School, Shaanxi Normal University, Xian, 710119, China Department of Mechanical and Industrial Engineering, University of Toronto, ON, Canada
国际会议
The Seventeenth Wuhan International Conference on E-Business(第17届武汉电子商务国际会议)
武汉
英文
276-289
2018-05-25(万方平台首次上网日期,不代表论文的发表时间)