Research On Methods Of Supplier Relationship Management Based On Data Mining

This dissertation focuses on studying the supplier relationship management solving methods of the key problems, which are supplier categorization, supplier evaluation and supplier selection, based on data mining. The e-supply chain environment is the research background; the supplier relationship management is the research object; the data mining is the research instrument in the dissertation. Also, the systematic data mining solution of supplier relationship management is proposed. The main creative contributions of this dissertation are as follows:(1) Dynamic supplier categorization method based on FW-Kmeans. It studies on building the dynamic supplier categorization model with FW-Kmeans algorithm to analyses supplier behavior data. This model could retain the ability of processing large-scale data of k-means. On the other hand, it overcomes the shortcoming on processing spare data of the subspace clustering algorithms. By means of comparing and adjusting the decision results, the new method could obtain the dynamic and reasonable solution, so that it is better than the traditional static categorization methods.(2) Supplier performance evaluation system based on rough set theory. It studies on creating the supplier evaluation system structure based on balanced scorecard, and the indicators are selected with KPI to adapt to the e-supply chain environment. The integrated assessing method, which is based on rough set to solve the important problems of the system, is proposed. Rough set has several advantages on dispensing with transcendent knowledge, processing imperfect information according as the granularity of knowledge, getting the minimal expression of knowledge and preserving the key information, objectively reducing attributes and evaluating weights. Compared to the traditional operation research methods, the new evaluation system has legible hierarchy, and easy to understand and implement, so that could evaluate suppliers systematically and effectively.(3) The supplier selection model based on genetic DEA programming. This model combines the advantages of both data envelopment analyses (DEA) and genetic algorithm. DEA is based on relative efficiency evaluation. It avoids determining the priority weights, and could accept various input and output indicators. On the other hand, genetic algorithm has learning, evolutionary, multi-direction and all-around searching characters, so that it could solve the multi-dimensional inputs and outputs, multi-objective programming problem of supplier selection efficiently. The new model is prior to the traditional multi-objective programming on the extensibility, adaptability and efficiency of decision making.(4) Supplier relationship management solution based on data mining in e-supply chain. The solution contains an integrated framework of SRM intelligent decision support system (IDSS). The IDSS performs the system analysis and design on four parts that are the overview of the system development, data modeling and analysis, process design and interface design. In contrast with the current procurement information system, the new solution and the IDSS are systematic, networking and intelligent. The enterprise will benefit from the IDSS implementation to obtain superiority in the e-supply chain environment.