The Cost Index Evaluation Model Based On Gray Correlation Analysis Research

With China’s accession to the WTO and WHO, Chinese tobacco enterprises arefacing great pressure and challenge. It has become a hot topic in tobacco industry toobtain maximum profits by constantly controlling the cost of production. However,because of lacking of intelligent information management system, current costmanagement methods commonly use words to describe the control strategy or usebasic quantitative formulas to assist in problem analysis, which hindered costmanagement’s development for refinement, digitization and intelligence. Taking theproduction data of a cigarette factory as an example, the article uses the greycorrelation analysis method to analyze costs affecting factors in cigarette production.And being clearer about the proportion of main influence factors and main factor,which provides improvement directions for the cost control management and on thisbasis to realize the real-time cost control system. In this article the main researchachievements have been come to light:Firstly, establish an assessment index system of production. This article mainlyfocuses on the study of production index in cigarette production. The key factors ofinfluencing production index are found finally by using grey correlation analysismethod to study those production data. And new assessment index system ofproduction is established.Secondly, improve the grey correlation analysis method. Grey correlationanalysis method has certain defects in counting, this article mainly analyzes andproposes improved measures for distinguish coefficient of grey relation analysismethod, a number of dimensionless methods and calculating the equalization ofcorrelation. Then, the article makes a comparison about multiple regression and greycorrelation analysis method in dealing with multi-factor problem.Finally, cost index evaluation model’s applications in real-time cost controlsystem. The article applies cost index evaluation model to real-time system. And themodel’s efficiency is proved by combining real data.