Research On Demand Forecast Model Of Auto Parts Company Considering Demand Fluctuation

In order to reduce the impact of market demand uncertainty,midstream and downstream enterprises in the automotive supply chain will amplify actual demand and provide it to upstream enterprises.The layer-by-layer transmission will incur bullwhip effect in the supply chain,resulting in high manufacturing costs and poor production stability.Therefore,so as to reduce the negative influence caused by the expansion of demand in the supply chain,companies in the automotive supply chain have begun to pay attention to customer demand forecast.This article takes Z auto parts company’s error of customer demand forecast and lack of scientific forecasting tools as a starting point,using different methods to establish demand forecast models determine the appropriate demand forecast model for Z company,and solves its problems in customer demand forecast.Finally,this article provides theoretical basis and model support for enterprises to carry out demand management and related decisions.This article builds a model based on historical demand data for forecasting research to seek the optimal forecasting model of Z company.Firstly,it analyzes the influencing factors of demand,and selects 15 influencing factor measurement indicators from 6 aspects.At the same time,this article obtains relevant data through enterprise research and related statistical Websites;then splits the data into training and test sets,and builds three prediction models of data mining based on the training set—support vector machine model,BP neural network model and random forest mode.The prediction results show that the prediction errors of the three models based on 15 factor inputs are still large,and affected by "noise" data.And the analysis model’s prediction accuracy decreases.Then this article determines 6 main influencing factors of 15 factors through the gray correlation analysis method,taking the 6 main influencing factors as input to establish a random forest prediction model based on large sample gray correlation analysis,supporting vector machine prediction model and BP neural network model.At the same time,in order to test the effectiveness of the data mining prediction model,this article uses the traditional time series forecasting method to establish the ARIMA model and the gray GM(1,N)model based on the characteristics of demand data for model comparison and analysis.Finally,the error evaluation index is used to analyze the prediction effects of the eight models of different prediction methods.The results show that the support vector machine prediction model based on large sample gray correlation analysis has the best effect and the best performance.Considering that the fluctuations of demand will affect the accuracy of the forecasting model,this paper also analyzes the applicability of each forecasting model under different fluctuations in demand.Use case reasoning(CBR)method to analyze the impact range of emergency events on car sales or output,and the impact range is[-80%,20%].Within this impact range,supposed that Z company encountered four kinds of emergency events in 2018 that caused different degree of demand fluctuations,the article compares the prediction errors of various forecasting models under different demand fluctuations to determine the optimal forecasting model under demand fluctuations.The experimental results show that the support vector machine prediction model based on large sample gray correlation analysis is the optimal prediction model under different demand fluctuations,which can better cope with the impact of demand fluctuations caused by unexpected events.The research results of this paper not only solve practical problems such as Z company’s large demand forecast error and lack of scientific forecasting tools,but also provide a certain theoretical reference for related manufacturing enterprises to conduct demand forecast research.