Drug quality, safety, and efficacy are dependent on thoroughly validated manufacturing processes. The life-cycle approach to process validation, as recommended by the FDA guidance, spans process design, qualification, and verification. Similar recommendations are made by the European Medicines Agency. Such recommendations demand evidence-based assurance that a process can deliver a quality product consistently. Integral to such methods is the critical decision of how many batches to examine for the qualification stage. In this chapter, we address this question using a Bayesian assurance and sample size determination method. Our approach incorporates prior knowledge of process performance, using both expert opinion and data. We illustrate the method with a problem in which potency uniformity data is evaluated with a process capability metric. We simulate qualification data using the posterior predictive distribution in order to decide on the number of batches needed for a given level of assurance.