A Study On Inspection Model Based On Production And Warranty Cost

With the market economy and technological development, the competition among enterprises is heating up, product costs have a direct impact on the market sharing, manufacturing enterprises tend to reduce product cost and improve product quality to meet market and customer's requirements. On the other hand, the product quality control is often dependent on the production process inspection. Whether the product inspection strategies are good or bad have a direct impact on the production costs and after-sales service costs. Product inspection has the efficacy of identifying the quality of products, feeding back the quality information, monitoring the production process conditions, determining the process capability and influencing the product failure rate.Different production inputs and after-sales service have a direct impact on selecting the different product inspection strategies. Therefore, study inspection strategies problems from a cost perspective are mainly related to two major factors, which are production costs and product after-sales warranty costs. Production costs mainly include manufacturing cost, inspection cost, reworking cost, and penalty cost components, product warranty costs are also affected by the impact of a number of product warranty policies. In considering the conditions of a certain cost factors, selecting the optimal inspection strategies will enable the lowest total cost of products, and then achieve the goal of improving product competitiveness.This paper studied the multi-process manufacturing system inspection allocation problem under the free repair warranty. Three inspection options considered in this work are no inspection, full inspection and sampling inspection. First, we introduced the theory of product inspection and their research status. Then conducted the cost analysis, built a cost model and then the analysis of the model features is performed, pointed out that dynamic programming algorithm is an effective way to solve the model and gave a detailed analysis of the solution. At last, for the axletree manufacture system, this paper used dynamic programming algorithm to solve the problem. The model is programmed and solved by LINGO optimization software for the optimal solution. The corresponding analysis of the results is conducted. In any mathematical model there is an issue of randomness, by just running the model once, one cannot predict how valid the results might be, and thus statistical analysis should be performed. In this article, two-level fractional factorial experimental designs are used to study and analyze the systems, evaluate and analyze the effect on total product costs and determine which variables are most influential.