Controls may include, for example, testing the chemical composition of a product, and the technical parameters at every machine in the process.Īn effective process to control strategy is particularly important when the product being made does not have a well-characterized chemical structure or its quality attributes are difficult to measure due to limitations in testing or detectability. Process modeling informs the controls strategy that defines what, when, and where to test to ensure that the process performs as it should and maintains the quality of the product. The more adept manufacturers are at process modeling, the better able they are to monitor and control the CPPs that determine a product’s critical quality attributes. In pharma, nearly all CQAs-including, to name just two, concentrations of active principles and excipients-are highly regulated. In the case of shampoo, for example, pH balance is a CQA. Guidance for industry: Q8(R2) pharmaceutical development, revision 2, US Food and Drug Administration, November 2009. CQAs are the physical, chemical, biological, or microbiological properties or characteristics that should be within an appropriate limit, range, or distribution to ensure the desired product quality. The more adept manufacturers are at process modeling, the better able they are to monitor and control the CPPs that determine a product’s critical quality attributes (CQAs). Process modeling is a manufacturing discipline for understanding the effects of process inputs (raw materials) and CPPs on final products. This article describes advanced process modeling, introduces three use cases for it, and offers guidelines to boost the likelihood of success with its use.
What industries use batch ph adjustment system upgrade#
In addition, the quality and R&D functions can upgrade product and process parameters during the next R&D cycle. With these insights, plant operators can adjust critical process parameters (CPPs) such as temperature or pH and improve decision making during production. In addition to these tangible benefits, advanced process modeling deepens manufacturers’ understanding of their production processes. Leading companies that use this tech-enabled approach to process modeling have seen a reduction in deviations (for example, products outside specifications) of more than 30 percent and have cut the overall cost of quality by 10 to 15 percent. This article is a collaborative effort by Álvaro Carpintero, Evgeniya Makarova, Miguel Ángel Morán, Matthias Spiegl, and Vanya Telpis, representing views from McKinsey’s Life Sciences and Operations Practices.Ĭompanies can leverage these models to improve performance and quality-even beyond what regulators may require-and save time and resources.
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