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Pharma Tech Outlook | Thursday, November 18, 2021
Manufacturing cost reduction opportunities exist at every level of the product lifecycle. Advanced analytics can expose these opportunities, allowing pharmaceutical businesses to make educated decisions on saving money.
FREMONT, CA: Artificial intelligence (AI) advancements are beginning to substantially impact automation technologies utilized across the industry, particularly machine vision and analytics. And the pharmaceutical industry is seeing some of the most significant AI applications.
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Given that single batch values for some pharmaceuticals can surpass 3 million dollars, it is not surprising that the pharmaceutical industry is attempting to optimize production with AI. However, research shows that this industry lags behind many others when it comes to employing analytics to boost output.
Even though other industries have used analytics and predictive capabilities to improve performance and respond quickly to changes in demand, 87 percent of pharmaceutical executives agree their companies have a bad digital culture.
COVID-19 prompted a rush to produce a vaccine and had a significant impact on the demand for medications currently on the market. The market's overall velocity has increased, but data shows that the pharmaceutical industry has a long way to go before it catches up.
Asset Management, Predictive Maintenance, and Analytics
Two areas of AI application that pharmaceutical companies are focusing on include:
Manufacturing cost reduction opportunities exist at every level of the product lifecycle. Advanced analytics can expose these opportunities, allowing pharmaceutical businesses to make educated decisions on saving money. These tools give pharmaceutical companies a competitive advantage, whether they are employing multivariate analytics to identify process degradation and its impact on the quality or anticipating final product quality to cut lab testing lag times.
Multivariate analytics software can be used to assess and continuously monitor how differences in material qualities, variations in methods, and process abnormalities such as sensor drift and changing ambient conditions affect the final product in pharmaceutical production plants. These tools can aid in the identification and resolution of process and product quality concerns and the enhancement of yields and reduction of off-spec output.
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