Research on Sampling Inspection Method for Supply Chain Components Based on Binomial Distribution

Abstract

In the context of accelerated growth in global manufacturing, conventional full inspection faces challenges meeting enterprise demands for effective supplier component quality control, while statistical sampling based on binomial distribution ensures reliability and reduces costs by rationally setting confidence levels and error ranges. This study proposes an autonomous method for determining the minimum sample size based on binomial distribution and hypothesis testing to ascertain the acceptance or rejection of spare parts contingent on the defective rate. Specifically, spare parts should be rejected if the defective rate exceeds the nominal value at 95% confidence, and accepted if it does not exceed this value at 90% confidence. This paper derives the formula for calculating the minimum sample size using test theory based on normal distribution approximation. The findings indicate that when the defective rate constitutes 10% of the nominal value, the minimum sample size necessary to receive the spare parts with 95% confidence is 139, and the minimum sample size required to receive the spare parts with 90% confidence is 98. This significant efficiency enhancement over comprehensive inspections is complemented by sensitivity analysis of the defective rate, confidence level, and random error, supporting rational production decisions under resource constraints.