Intelligent Decision-Making and Application for Quality Optimization of Enterprise Spare Parts Production

This study addresses the issue of quality control in spare parts during electronic product manufacturing. It employs Monte Carlo simulation, binomial distribution, Z-tests, sampling inspection, and statistical analysis techniques to construct a comprehensive quality evaluation and decision-making framework covering the entire process from spare parts to finished products. By designing optimal sampling inspection schemes and developing cost-benefit mathematical models, the study simulates and identifies optimal strategies under different production scenarios, such as sampling sizes and inspection and disassembly strategies. Through multi-stage quality control analysis and defect rate reassessment, the effectiveness of these strategies is validated. The study concludes that enterprises can achieve the highest average profit under specific decision-making combinations. Although a comprehensive quality management framework is proposed, the model still has limitations, such as fixed parameter assumptions and insufficient consideration of supply chain disruptions. Future work will explore the introduction of Bayesian methods and random effects models to further enhance the model's adaptability and accuracy, while also considering the impact of dynamic supply chain factors on production strategies.