I do not have experience in the industry but I found that the usage of the confidence, reliability, and acceptance quality limits (AQLs) to determine sample sizes for process validation are used to ensure that validation activities will yield valid results based upon an organization’s risk acceptance determination threshold, industry practice, guidance documents, and regulatory requirements. The Bayes Success-Run Theorem is used to determine an appropriate risk-based sample size for process validations. The Bayes Success-Run Theorem is implemented as follows:
R = (1-C) ^ (1/n)
where: R = Reliability (or probability of success)
C = confidence level
n = sample size for “0” failures allowed on test
Transposed the formula becomes n= ln(1-C)/ln(R)

For example, if we want to be 95% confident that a process is 95% reliable how many parts do we need to produce that are defect-free?

n= ln (1-.95)/ln(.95) = 59 parts with “0” failures allowed on test

The confidence and reliability levels to determine the appropriate sample size will depend on the risks associated with the product.
Even though, I included a mathematical example, I think it really helped understand how can we determine an appropriate risk-based sample size for the upcoming project.
sources: http://www.pharmaceuticalonline.com/doc/risk-based-approaches-to-establishing-sample-sizes-for-process-validation-0001
http://www.qualitymag.com/articles/91991-sample-sizes-how-many-do-i-need