Decision-Making Spare Parts Assembly Production Process Based on Dynamic Programming Algorithm

When enterprises produce best-selling electronic products, their production decisions have a crucial impact on cost control and profitability. This study focuses on the sampling and testing of spare parts and decision-making at each stage of the production process, and adopts statistical hypothesis testing and efficacy analysis to derive the key data: when the defective rate of spare parts has a nominal value of 10%, and the defective rate is deemed to have exceeded the nominal value and rejected at 95% confidence, the minimum number of samples tested is 133 and the number of rejected defective products is 19; when the defective rate is deemed to have not exceeded the nominal value and received at 90% confidence, the minimum number of samples tested is 81 and the number of received defective products is 4. In addition, we set up 0 - 1 decision variables to analyse the cost and profit function, and obtain the optimal decision and maximum profit in each case after exhaustive calculation, such as in case 1, when we test parts 1 and 2, do not test the finished products and do not dismantle the unqualified products, the profit can reach to 12,420. The decision-making model constructed in the study is accurate and practical, which helps enterprises to make scientific decisions and improve economic efficiency.