Evaluation Model of Enterprise Lean Management Effect Based on Data Mining

Abstract

This article discusses the task allocation decision of single management ability and multiple management ability in view of the operation stage of the specific project of enterprise lean management service supply chain. First of all, the basic process of the management service integrator’s task allocation to the functional management service provider is given. Based on factors such as business satisfaction and other factors, we construct a management task allocation model based on service income price discounts in the case of fuzzy demand. On this basis, we further dig out the external and internal related factors of supply risk, use enterprise research and data mining methods to verify theoretical hypotheses and identify related factors of supply risk, analyze the impact of the characteristics of cooperation with different types of suppliers in data mining to determine the key factors of supply risk for different types of supply and demand cooperation, and determine the focus of supply risk control for different types of suppliers for data mining. In the process of simulation experiment, the method of maximum satisfaction of fuzzy multiobjective programming is adopted, and the influence of factors such as the preference of decision-makers and price discounts on the result of task allocation is analyzed in combination with specific examples. Finally, on the basis of fully considering the characteristics of management services, we build a comprehensive evaluation index system for management service providers in terms of service capabilities, service quality, informatization capabilities, and cooperation risks and comprehensively consider the uncertain factors in the decision-making process. The experimental results show that the proposed interval data mining solution method based on the degree of possibility can realize the ranking of management service providers by combining the subjective wishes of decision-makers with the objective evaluation of data mining.