Establishing, maintaining, and interpreting meaningful metrics has become an emerging industry issue. This topic has risen
to prominence based on an article (1) and a Federal Register notice (2) that explored the question of what types of metrics should be applied to pharmaceutical operations, giving meaningful
insight to their overall quality and compliance.
Susan J. Schniepp
There is no set requirement on what metrics a company should track to measure their overall performance. Each company should
determine which metrics to track based on their operations, number of facilities they operate and where they are located,
what types of products they manufacture, and what type of culture exists in their places of business.
DETERMINING WHICH METRICS TO TRACK
When establishing a metrics program, companies should evaluate numerous data input points including, but not limited to, product
quality attributes, manufacturing site performance, people metrics, and quality system metrics. For product-quality metrics,
companies should consider reporting on batch-specific data such as trending drug product, drug substance, and stability-test
results against customer complaint rates. Indirect product quality metrics could include environmental monitoring, water trend
results, and yield rates. When establishing site metrics the company could look at inspection history including internal audit
findings and maintenance history such as equipment age versus defect failure rates. People metrics should consider ongoing
job-specific training and education, skills and experience assessments, and employee turnover rate by job function and site.
Quality systems metrics might look at change control, investigation root-cause trends, and release-testing cycle times.
The metrics chosen must be meaningful and written to provide a clear analysis of ongoing activities. It is important for operations
and quality to agree on the metrics and how to report them to management to avoid overreaction to the data. It is not sufficient
to simply report the data. The interpretation of the data is of crucial importance because it may include a root-cause analysis
of its own.