  Ensuring product quality is critical to combating the global challenge of
substandard and falsified medical products. Post-market surveillance is a
quality-assurance activity in which products are tested at consumer-facing
locations. Regulators in low-resource settings use post-market surveillance to
evaluate product quality across locations and determine corrective actions.
Part of post-market surveillance is developing a sampling plan, which specifies
the number of tests to conduct at each location. With limited resources, it is
important to base decisions for test allocations on the utility of the samples
tested. We propose a Bayesian approach to generate a comprehensive utility for
sampling plans. This sampling plan utility integrates regulatory risk
assessments with prior testing data, available supply-chain information, and
valuations of regulatory objectives. We illustrate the value of this metric
with a case study based on de-identified post-market surveillance data from a
low-resource setting. To do so, we develop a fast method for calculating
utility that is used in a greedy heuristic to form sampling plans. The
resulting plans focus surveillance on locations more likely to impact regulator
decisions. These locations can be distinct from locations with higher assessed
risk.

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