Research on enterprise production quality control strategy based on hypothesis testing and dynamic programming

Abstract

In the process of large-scale production of enterprises, the cost of product testing is high. This study aims to construct a production optimization decision method based on dynamic programming model for enterprises facing problems such as unbalanced capacity utilization, high inventory cost and unreasonable planning in the production process. First, this paper uses hypothesis testing to determine the reasonable sample sampling measurement, and constructs the production optimal decision model through dynamic programming. Then, based on the hypothesis testing model, the minimum sample size under different confidence levels was determined: 271 sample sizes for 90% confidence, 385 sample sizes for 95% confidence, and the optimal control of detection times was realized. Finally, through the reverse solution of multi-stage programming, the total cost was minimized as the goal, and six optimal detection strategies were obtained. The research shows that this method can effectively guarantee product quality and control cost, provide scientific basis for production decision-making of enterprises, and help enterprises to enhance competitiveness.