Skip-lot Destructive Sampling with Bayesian Inference

This paper examines a quality control problem where testing is destructive. A skip-lot model is developed using a Bayesian approach to infer the process state between inspections. The model is then used to 1) maximize the s-expected return per lot produced and 2) determine the inspection interval, sample size, and acceptance number. Numerical evaluation is used to compare this model with a previous model of the situation. Examples suggest that this formulation of the skip-lot problem which accounts for the posterior distribution of process state for each lot and the revenue received appreciably reduces destructive sampling.