Research On Inventory Optimization Of YM E-commerce Company Based On Demand Forecasting

With the rapid development of e-commerce in few recent years,consumer behavior had been upgraded,personal customization and product reputation have become the important factors that consumers pay attention to.The impact on single commodity attribute,enterprise brand awareness and product stickiness are significantly weakened.According to statistics,the revenue growth of e-commerce giants such as jd.com and Alibaba slowed down after 2016.Emerging small and medium-sized e-commerce enterprises and brands have gradually eroded the e-commerce consumer market monopolized by major giants by building their own business platforms and attracting user traffic around social media.It can be seen that the e-commerce industry has completely separated from the blue ocean competition,and the competition pattern has changed from "one super and many strong" to a mature market of"letting a hundred flowers bloom".In this context,the most important core competitiveness of an enterprise is no longer to shorten the capital flow cycle and reduce the product production cost pursued in the traditional enterprise supply chain management,but to quickly respond to market turbulence through consumer behavior analysis and reduce its own business risk while meeting the personalized needs of consumers.Therefore,with the help of data analysis technology to mine data value,enterprises can adapt to market changes and reduce cost waste in business processes.It is an indispensable hard strength for e-commerce enterprises in the current increasingly competitive Red Sea market environment.Based on the above background,according to the bullwhip effect theory,this thesis analyzes the cost waste in the process of enterprise operation,and uses the artificial intelligence technology widely used in various fields to predict the sales demand,so as to help enterprises accurately manage inventory and reduce operating costs.Due to the complex market environment,there are many factors affecting demand forecasting.Therefore,this thesis focuses on the characteristics of the new era,such as sharing economic information interaction,integration of production and marketing,social commerce,and mobile commerce,to study the industry segmentation attributes of goods and consumers’ decision-making habits.And then the lasso regression is used to screen the features,eliminate the invalid data set and obtain the effective features.Then the prediction of machine learning model and random forest model is carried out,and the simulation calculation training is carried out by using R and Rstiduo software.The prediction results are compared with the traditional prediction methods to verify the effectiveness and rationality of these methods.Finally,according to the current situation and existing problems of inventory management of YM e-commerce company,combined with the prediction results,this thesis puts forward record point inventory management mode for YM e-commerce company to optimize inventory.And from the aspects of supplier selection,market demand prediction,inventory management and marketing,this paper formulates the guarantee measures to optimize inventory,which has a certain reference function for improving the market competitiveness of e-commerce enterprises.