Pressure to cut development costs and lower regulatory barriers while assuring product quality has stimulated the pharmaceutical industry to apply Quality by Design (QbD) to manage risk and gain process and product understanding. As a result, QbD is being widely promoted by regulatory authorities such as the Food and Drug Administration, and the International Conference on Harmonization.
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Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.
In manufacturing and other industries that have complex processes, knowing which variables have the most impact on quality and at what point, or knowing which combination of variables to change in order to improve your process, can have a huge impact on the overall quality or profitability of your manufacturing process. But without making expensive and time-consuming changes in the physical processes in order to test all possible scenarios, how can you identify and predict the variables that have the most significant impact on your outputs?
Using advanced data analytics models in real time opens up a whole new world of possibilities for improving your production processes. Not only does real-time process monitoring provide a level of confidence in your process performance, it can also help improve the overall quality of your production output.