Reserch Of Quality Cost Management And Optimization In Manufacturing Industry

More and more enterprises'leadershipment have realized that Quality Cost Management is the key point to their companies'Business Management Cost controlling . According to the existing data, the cost of quality management covers 10-30% of the total sales revenue for most of those enterprises. This amazing number reminds us that how much you can improve your quality management cost structure then how much you can increase your market share in this competitive world. Obviously the task for us now is to change the existing way of quality cost statistics rule which only focus on the scrap cost of pure products to creating an optimized system facing the whole product life cycle. Thus we can reach the balance between cost and quality then enlarge companies'profits utmostly without losing customer satisfactory.First of all we utilize traditional quality cost management tool such as Tendency and Pareto Analyze to make thorough qualitative and quantitative analyze with specific company's existing QCM structure. After we reviewed their data collecting and tracking situation we did realize there is really problems and disadvantages in their former Quality Cost Database system.To solve this issue and finally find out a target for enterprise's leadership to improve their existing quality cost investment allocation, we tried several traditionally mathematics model in quality cost management area. And finally we determined to use the classic mathematics model which can come up with the lowest QC. After the calculation with computer , we find it fits the history data of our specific company very well .With the determination of target the lowest point of QC investment , we are able to use parameter inspection method to provide the best proportion of total quality cost investment revenue for specific company which can be devided into Prevention Cost , Inspection Cost , Internal Lost and External Lost.The simulation result in computer proves very well and finally prove the mathematics model we choose for our specific company is correct .