In mid-June, I attended a fascinating conference on statistics for measuring, analyzing, and optimizing the validation process for Good Manufacturing Practices compliance in the pharmaceutical, biotech and medical device industries. The second annual Statistics in Validation summit brought together organizational stakeholders to examine how best to implement FDA regulatory guidance concerning the use of statistical methods in manufacturing.
Statistical analysis methods have long played a pivotal role in ensuring consistent product quality and minimal consumer risk. Analytical tools can help engineers understand the manufacturing process, as well as why and how it can be expected to yield desired robust quality with known certainty. In short, statistical analysis supports the FDA’s purpose of providing guidance for applying accepted best practices to guarantee manufactured pharmaceutical products and medical devices are safe for consumers.
Neither the FDA nor the pharmaceutical and medical device industries takes their responsibilities lightly – at all! That’s why all relevant process variables are carefully evaluated for their influence on product variability and quality over the entire manufacturing lifecycle. Detailed Standard Operating Procedures (SOPs) are drafted to ensure that processes are stable, yielding repeatable predicted quality and minimal risk.
Understandably, these segments are slow to adopt innovative methods and approaches, which already have been widely implemented in other industries. But herein lies the problem:
practically all other (non-validated) automated manufacturing sectors are replacing or enhancing statistical methods with machine learning techniques to improve overall efficiency, product yield and cost, and final product quality.
In some of the more competitive automated electronics manufacturing companies, for instance, specifically fine-tuned machine learning procedures are often considered absolutely mission-critical for competitiveness and business survival. Yet, in pharmaceutical and medical device manufacturing, these techniques are only beginning to be considered.
During my session, entitled “Machine Learning Techniques in Manufacturing: Applications & Caveats,” I made the case for how machine learning can offer manufacturers, including pharmaceutical and medical device companies, a much clearer end-to-end understanding of their manufacturing processes. Regardless of how complex or non-linear, as well as how difficult-to-articulate the interactions of process inputs may be, machine learning techniques help stakeholders understand, predict, and ultimately “lock-down” the variability of their process for consistent and robust, low-risk product quality.
For example, machine-generated data can be used to drive advanced pattern recognition methods that anticipate manufacturing problems before they occur.
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