Decision-Making Research on Supply Chain Quality Inspection Based on Binomial Distribution and Monte Carlo Simulation

Zihang Jia; Zhenyao Du

doi:10.54097/hyh43p61

Authors

Zihang Jia
Zhenyao Du

DOI:

https://doi.org/10.54097/hyh43p61

Keywords:

Monte Carlo Simulation, Z-test, Binomial Distribution, Hypothesis Testing, Profit Function.

Abstract

In modern industrial production, enterprises face the dual challenge of ensuring supply chain quality reliability while controlling costs. Traditional single-link quality detection methods are inadequate for multi-link collaborative decision-making in supply chains. This study proposes a quality inspection method combining binomial distribution and Monte Carlo simulation for enterprise production processes. Integrating hypothesis testing based on normal distribution determines whether spare part defective rates exceed supplier-declared standards, guiding batch acceptance decisions. The model introduces a random allowable error E to adjust significance levels dynamically and errors, enhancing performance analysis. The study also considers inspection, dismantling costs, and substandard product exchange losses. Using Monte Carlo simulation, a revenue model is constructed for supply chain decision-making, providing accurate success rate solutions for parts, semi-finished, and finished products, aiming to maximize enterprise profit. Results show that precise quality control and cost management significantly improve economic efficiency and market competitiveness. For instance, selling a single product yields maximum profits increased from 9.60 yuan to 19.36 yuan (102% increase), with the second-best option reaching 15.19 yuan, a gap of 4.17 yuan compared to the optimal solution. This study offers robust theoretical guidance for quality control in enterprise production processes.

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