Research On The Key Issues Of Supplier Relationship Management In Coal Enterprise

Given the current economic environment and the severe coal market situation, it is difficult for the coal enterprises to maintain the invincible position in the fierce competition only by strengthening their internal management and optimizing their internal processes. The coal enterprises must make full use of external suppliers' resources, establish partnerships with the suppliers so as to reduce overall costs and improve their operational performance and enhance their competitive advantages. With the application of the theories of supply chain management, suppliers' relationship management, and data-mining, this paper focuses on the research on the three key issues of the suppliers' classification, performance evaluation, procurement quantity allocation, connects these three key issues and puts them into a decision-making framework. In other words, this paper classifies the suppliers and proposes diversified management and performance evaluation strategies, conducts the effective performance evaluation on suppliers and finally optimizes procurement quantity allocation based on the suppliers' performance evaluation result.Chapter 1: Introduction. This chapter first introduces the research background, analyzes the necessity for coal enterprises to strengthen supplier relationship management so far as the current severe situation of coal supply is concerned, and points out the significance of the research. Then, through comprehensive analysis and review of the key issues, the suppliers classification and assessment, and the status quo of the distribution of the numbers of purchases in the supplier relationship management at home and abroad, the paper makes clear the inadequacy in the current research and points out the research space of this paper. Finally, based on what is mentioned above, the main research contents of this paper and the framework for the overall research is presented.Chapter 2: The Basic Theory of Supplier Relationship Management. This chapter explains the definition of the supplier relationship management and supply chain management. And then it introduces the basic concepts, the theory, the definition and the related formulas of the data mining method used herein, laying a theoretical foundation for the follow-up research work.Chapter 3: Research on Coal Enterprise Suppliers Classification Based on SOFM Neural Network. This chapter first explains the basic concept and the significance of supplier classification and the main ideas in the research on coal enterprises supplier classifications, pointing out the need to reduce the number of clusters of the potential supplier relationship classification and that coal enterprises supplier classification should be combined with the characteristics of their supplies and procurement. The characteristics of the coal business supplies and procurement targets are analyzed. The two-dimensional appreciation rate – the competitiveness matrix model is improved, the production operation factors are added into it based on the material characteristics of the coal enterprises, the effect of the suppliers on the coal production business is added into the matrix model, which is extended to be a three-dimensional appreciation rate- the competitiveness matrix model that considers three production operation factors, according to the three dimensions: the supplier's appreciation rate, competitiveness and impact on production operations, the suppliers are divided into eight categories: strategic suppliers, influential traditional suppliers, high appreciation rate suppliers of production configuration, low appreciation rate suppliers of production configuration, highly competitive suppliers of value-added type, low appreciation rate suppliers of competitive type, traditional competitive suppliers and general suppliers, and proposes the corresponding strategies of management and performance evaluation for the results of the suppliers' classification. Subsequently, based on the analysis of the researches on suppliers' classification at home and abroad and combining the coal industry production business property index, the coal enterprises supplier classification index system is established, and the indicators' quantitative criteria and methods are elaborated. Finally, after elaborating SOFM neural network features and algorithm, the model of coal enterprise suppliers' dynamic classification are proposed based on SOFM neural network, and the basic ideas and detailed building steps are put forward.Chapter 4: Research on Performance Evaluation System of Coal Enterprise Suppliers Based on Variable Precision Neighborhood Rough Set. This chapter first discusses the important matters in the system construction: the purpose and necessity of supplier evaluation system construction, strategies to conduct supplier evaluation and build the system and the methods, etc., pointing out that the suppliers' evaluation should help to carry out the coal business development strategy, help to improve the whole coal supply chain performance, meanwhile the number of indicators of performance evaluation system. Next, it introduces the basic concepts and framework of the Supply Chain Operations Reference Model(SCOR), analyzes the risk factors of supply chain concerning the suppliers in the SCOR model and the performance indicators associated with the procurement function, puts forward the evaluation system framework of purchasing function performance based on the SCOR model, identifies eight dimensions of evaluation index system, namely the cost, quality, time, flexibility, innovation, organization and relationship factors, strategic factors and cultural factors. Then, based on the analysis of the researches on the evaluation of domestic and foreign suppliers, an evaluation system for coal enterprise supplier performance is established, and the indicators' quantitative criteria and methods are elaborated. Finally, the performance evaluation model of coal enterprise suppliers based on variable precision neighborhood rough set is constructed, and meanwhile the experience factors of the procurement strategy and decision-makers' subjective preference are added into the model, the comprehensive weight is obtained through combining the objective weight and subjective weight of decision-makers derived from the model, making it adaptable to the changes in the procurement strategy and market environment, and then the algorithms and specific steps adopted in the model construction are elaborated.Chapter 5: Research on the Optimal Allocation Model of Coal Enterprises' Centralized Procurement Quantity of Multi- Mining and Multi-Materials Based on the Genetic Algorithm NSGA-II. This chapter first explains the meaning and significance of the purchasing quantity allocation optimization of coal enterprises. Next, it analyzes the key factors in purchasing quantity allocation optimization of coal enterprises, namely the analysis of model building process, decision-making criteria and the determination of the methods to be used, and proposes the establishment of a data-driven model building process. Then it elaborates on the basic concept of the theory of the deterministic multi-objective programming and stochastic multi-objective programming and the conditions and methods for the transformation of stochastic multi-objective programming into a deterministic multi-objective planning and introduces the traditional multi-objective programming solution method, analyzes the inadequacy of traditional methods in obtaining Pareto optimal solution set, proposes the multi-objective programming solution method based on genetic algorithm NSGA-II, and elaborates on the solving steps of the genetic algorithm NSGA-II. Finally, based on the analysis of the effect of the decision-making variables, decisions-making objectives and demand fluctuations of the purchases quantity allocation optimization model equations on the purchases quantity allocation, the optimal allocation model of coal enterprises' centralized procurement quantity of multi- mining and multi-materials are established under the circumstances of both the identified and the stochastic demands. Meanwhile the model's optimization function and constraints are elaborated.Chapter 6: An Empirical Study. This chapter, taking, for example, three coal companies, elaborates respectively the methods of data processing and specific processes in the supplier classification, the supplier's performance evaluation and the method and supplier procurement quantity allocation model through three numerical examples, and analyses the results, verifies the model's feasibility.Chapter 7: Conclusion and Prospect.This chapter summarizes the paper's main points and innovations. The main innovations proposed herein are as follows:(1) The three-dimensional appreciation rate- the competitiveness matrix model that is added by the dimension of the production operation effects for the coal enterprise supplier classification, and the index system for suppliers' classification, and the construction of the coal enterprises classification model based on SOFM neural network.(2) The analysis of the risk factors and the evaluation index system for the suppliers' procurement function performance based on the SCOR model, and, on the basis of the rough set theory, the construction of the coal enterprises supplier performance evaluation model based on the variable precision neighborhood rough set, the introduction of the policymakers' preference coefficient concept that embodies the procurement strategy and policymakers' subjective preference into the model, the determination of the final comprehensive weight through combining the objective and the subjective weight.(3) The research on the supplier procurement allocation optimization model and the methods for the centralized procurement quantity of multimining and multi-materials of the coal enterprises that take price discount into consideration under the circumstances of both the identified and the stochastic demands. The research focuses on the construction of determining and stochastic multi-objective mathematical programming models and the description of the elitist non-dominated sorting genetic algorithm NSGA-II.