AI IN PHARMA & BIOTECH
The value of AI in pharma and biotech manufacturing lies in reducing product defects, automating quality controls, increasing capacity and streamlining maintenance. Pharma and biotech manufacturers are ideal candidates for sophisticated analytics as they are already well-instrumented with sensors, controllers and storage of historical data.
According to a global consulting firm, manufacturing industries have captured only about 20%-30% of the potential value of data and analytics to date – and most of that has occurred at a handful of industry-leading companies. This is despite that advanced manufacturers have been working with automation and lean ways of working for decades. By embarking on an AI journey, far more complicated and bigger amounts of information can be processed across manufacturing operations and across the supply chain – detecting inefficiencies, relationships and predicting potential issues even before they emerge.
Many applications are using different regression and classification models, and even natural language processing.
Areas of AI application in Pharma
- Quality control can be enhanced through vision systems by adding image recognition models based on convolutional neural networks.
- Downtime of equipment can be minimized by analyzing data for each manufacturing step and/or for a chain of linked operations e.g. moldings importance on assembly output.
- Models can be implemented so that they continuously learn from data accounting for changing conditions and the current state of a specific machine.
- Analysis of content in documents like batch records or failure investigations can be time-consuming and complicated. Here AI can help find relevant textual information, words, strings, numbers, and even paragraphs.
- Cameras can monitor organisms growth in e.g. Petri dishes and be trained to identify different types of organisms and count number of organisms.
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