AI application in Pharma


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.

Use cases

Production planning
A model can identify hidden patterns in historical sales data - such as those in seasonal demand and subsidiary behavior or new product launches
In-process controls – errors in Pharma
Many product errors or machine malfunctions are caught late in the manufacturing process, due to manual in-process controls
Predictive maintenance in Pharma
An algorithm predicts the risk of equipment failure and need for maintenance
Inventory prediction in Pharma
The sale of goods gets predicted and based on that algorithm gives recommendations for store inventory management, this allows reducing of inventory of trash
Environment monitoring
Incubation time for environmental samples counts in days and weeks with the risk of production being non-compliant
Document search in Pharma
Searching and classifying quality documents can save time and help identify areas for improvement
Increase output – modelling patterns
Modelling patterns between input and output of the batches and analyzing performance parameters can increase output substantially
Sorting and scrap reduction in Pharma
Scrap of good products must be minimized and scrap of poor products must be hundred percent.

Meet the industry leader

Kim Tosti

Kim Tosti

Pharma & Biotech, 2021.AI

Kim Tosti is the Head of Pharma and Biotech at 2021.AI. His goal is to bring AI & ML onto the agenda of the Pharma industry. Kim’s core competencies include corporate governance, supply chain excellence, and risk management. Prior to 2021.AI, Kim spent the majority of his career at Novo Nordisk as a Senior VP. in

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