Application of BP neural network in decision-making of electronic products production process

For the production process of electronic products, according to the characteristics of its spare parts, such as complex supply chain, high defective rate and sensitive detection cost, BP neural network algorithm is introduced to combine validity analysis with principal component analysis to extract the key factors affecting cost-effectiveness through the distribution of cost and benefit intuitively presented by stacked histograms. Regression analysis reveals the quantitative relationship between variables such as defective rate of parts, purchase unit price, inspection cost and finished product cost. On this basis, an integer optimization model is constructed and the model simulation is carried out to design a comprehensive production decision-making scheme for maximizing profits. The solution covers differentiated inspection strategies, finished product sampling inspection, unqualified product processing and user return processes. Optimizing the production process through data-driven decision-making can achieve a balance between cost control and quality assurance.