Packaging is the very last step of the production process before shipping out the product to the supply chain and, ultimately, to patients. That means that scrapping units at this stage is extra costly and should be avoided as much as possible. Even though products at this stage have already gone through quality control, scrap rates in packaging can be as high as 2%, due to incorrect labeling, failed assembly of products that go into devices, faulty configuration of such devices and other issues.

Some pharmaceutical manufacturers have installed cameras and employed traditional rules-based computer vision algorithms to help mitigate some of these issues. However, such systems often introduce even more risk into the packaging process because of their low accuracy, low reliability, and high maintenance needs.

AI models significantly outperform traditional computer vision solutions in terms of accuracy, reliability, and maintainability. By making use of state-of-the-art convolutional neural networks, both false rejection rates and need for maintenance can be kept close to zero, which boosts Overall Equipment Effectiveness rates and helps maintain healthy gross margins.

With Criterion AI you can

  • Train AI models to improve efficiency in packaging
  • Improve all three components in the calculation of OEE:
    • Availability due to less maintenance
    • Performance due to potential speed increases
    • Quality due to reduced false rejection rates
  • Streamline packaging operations across multiple sites
  • Simplify manual processes and SOPs in packaging

Contact us today to start the dialog on how Criterion AI can help you improve your packaging operation with the power of AI:

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