Siemens > Case Studies > Digital Twin in Pharmaceutical Manufacturing

Digital Twin in Pharmaceutical Manufacturing

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 Digital Twin in Pharmaceutical Manufacturing - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Digital Twin / Simulation
Applicable Industries
  • Pharmaceuticals
Applicable Functions
  • Process Manufacturing
Use Cases
  • Digital Twin
The Challenge

GSK wanted to improve their vaccine manufacturing processes and see how they could effectively optimize the development process.

The Customer
About The Customer

GlaxoSmithKline (GSK) is one of the world's largest research-based pharmaceutical companies that that discovers, develops, manufactures and markets human health products. It is an innovative company that produces branded products only, which it has developed itself.

The Solution

GSK partnered with Siemens and Atos to create a digital twin of their factory process for creating a vaccine adjuvant, a molecule that helps spark a robust immune response.

The twin allows GSK to tweak, innovate, and simulate the entire process, unlocking hidden gains and fixing problems before they happen.

GSK plans to gradually implement digital twins into more and more processes, and eventually incorporate them into vaccine R&D, as well, where reduced need for real experimentation may make the process quicker, more cost effective, and sustainable.

Operational Impact
  • [Efficiency Improvement - Production]

    “This digital ‘factory’ allows us to simulate, monitor closely, anticipate failures, and optimise quality and self-learning,” GSK said. “The performance data obtained from real run is fed back into the development process and helps optimise products and processes at an early stage.”

  • [Data Management - Data Simulation]

    If you can understand the causes of variabilities in the manufacturing process, you have the potential to make it right the first time. Modeling inconsistencies in the manufacturing process may help minimize risk and help facilities transfer capabilities, as well.

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