Application Research Of Data Mining Technology In Supply Chain Management Of Operator

With the development of economic globalization and the innovation of communication technology,mobile operators face the increased pressure,such as the exhaustion of user growth space,the bottleneck of traffic growth,the decline of main business revenue,etc.Under the background,domestic telecom operators begin to realize that sustainable supply chain management will become the competitive advantage resources of enterprises.It is hoped that the supply chain management mechanism of low cost and high benefit will be constructed by purchasing the elaborating management,reasonable control inventory,cost control and so on,to provide strong support for enterprise decision-making and strengthen the core competitiveness of the enterprise.For new opportunities and new challenges of telecom industrial revolution,has turned attention to scientific management and fine management,promoting development with scientific management,wanting benefit with the fine management,building a supply chain management system with data mining technology.What is more,a new type of supply chain management system based on market demand with the management of big data analysis has been formed by shifting demand management mode and actively exploring the change law of market supply and demand.In this paper,the supply chain management of operator is presented.According to the current outstanding problems of enterprise supply chain management,the application of data mining technology is explored in supply chain management system,and the supply chain data analysis platform is designed.This paper focused on purchasing requisition module.The time series algorithm and ARIMA model are introduced.The collaborative application effectively research between supply chain demand and purchasing amount is studied,to promote the supply chain management.It can provide effective management means and reliable data support for purchasing on demand,reasonable inventory control and cost.The main contents of this paper are as follows:(1)Based on purchasing data in the procurement system,combining with the product of the past usage scenarios and various products at different times of the actual usage,a time series algorithm is used for predicting the procurement requirements of centralized purchasing and the secondary centralized purchasing product,and for rolling forecast for the product requirements of the next cycle.According to the analysis results,the suppliers can make reasonable supply plan within the supply period,to guarantee the business demand of the whole province with the best supply performance.(2)In previous purchasing work,scale centralized purchasing products were separated into small batches product procurement.In order to resolve the problem,various local companies on purchasing data platform are analyzed.And it is considered whether there is a splitting behavior that the total accumulative purchasing amount of local branches for the similar product is more than 100,000 RMB in a year for.Then the corresponding management strategy is formulated according to the results of the analysis.(3)Due to the quality cost and the reasonable planning and use of the of logistics distribution cost,a lot of money can be saved for the company.To better control the cost of purchasing department,the usage of the purchasing cost is analyzed with purchasing data analysis platform.For the quality cost control,it is detected whether product quality inspection task is issued by sampling proportion according to the requirements of Group company.For logistics distribution behavior,it is monitored whether there is no purchase order associated with the transportation order,or whether the transportation cost is too high in the proportion of the order amount.Then reasonable control strategies for monitoring related results are developed to further reduce procurement costs.