Decision-Making problem for maximizing profit in electronic products

There must be strict quality control to survive in manufacturing today, especially for long-lasting electronic components. This project uses traditional techniques like binomial distributions and Bayesian models, decision trees and dynamic programming, to model defect rates. It used Monte Carlo simulation and developed a profit maximization plan. The Bayesian model uses stale numbers and new samples in an attempt to inform decisions about defect rates. The study determined that 2,642 samples (95% confidence) or 1,341 samples (90% confidence) need to be used in order to confirm if defect levels exceed expectations, optimizing efficiency and accuracy. Costing allows for optimum low-cost and high-profit options to be determined. Step-by-step modeling tries inspection and repair costs to minimally decrease defects and costs. Monte Carlo and sensitivity analysis provide enhanced decision-making flexibility. Skipping inspection checks on parts that have been inspected and fixing defective returned products in the factory can at times be most lucrative, and manufacturers can use a simple formula to attain both quality and profit.