Optimizing Dynamic Programming Algorithm Based on Bayesian Model to Solve Enterprise Decision-Making Problem

In the current highly competitive business landscape, enterprises face a series of complex decision-making challenges during the efficient production process of popular electronic products. Taking this as the entry point, this paper deeply analyzes these problems, aiming to build a scientific theoretical foundation for enterprise decision-making, optimize the production process, reduce costs, and enhance economic benefits. This paper first uses statistical hypothesis testing to design a sampling scheme to evaluate the defective rate of parts. Then, the dynamic programming algorithm is employed, and a defective rate optimization model based on Bayesian update is incorporated to comprehensively optimize the detection and disposal decisions of parts, finished products, and defective products in the enterprise production process. The research results show that the optimization model can effectively deal with the risk of defective rate fluctuations and ensure the consistency and reliability of decision-making results. Through case analysis, it can be seen that this model can customize accurate decision-making schemes for enterprises in various scenarios and significantly improve economic benefits. In summary, this study provides crucial theoretical support and practical guidance for the improvement of enterprise management level and industrial upgrading, demonstrating significant value in practical applications.