January 11, 2019

Making the Conversation Real – What To Expect From The AI Manufacturing Team At Xavier University

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Artificial Intelligence is no longer an entirely foreign concept in our industry and as companies gradually begin experimenting with these technologies (AI, ML, and big data) in their manufacturing operations, a wave of questions start to surface. How much investment is needed and how do we determine the ROI post-investment?  What will be the regulatory compliance hurdles and how can they be addressed? Where do people fit into all of this? In an attempt to help the pharmaceutical and medical device community adapt to this digital transformation, Xavier Health at Xavier University started an Artificial Intelligence Initiative.  The AI Initiative is a small community focused on integrating the implementation of AI in pharmaceutical and medical device manufacturing operations.  This initiative is the framework where different actors are aligned to explore and empower AI implementation in the regulated environment under the life sciences foundation.  There are four working teams: AI Core Team, CPQA Team, CLS Working Team, and the AI In Manufacturing Team.  Each team is made up of FDA and industry professionals, working collaboratively to address various obstacles from increasing the assurance of product quality through the power of AI to setting a vision and strategy for the AI Intelligence Initiative Core Team.

As a company that understands first hand the journey companies go through when implementing these new technologies, it was important for us at Bigfinite to join the organization and contribute to the dialogue.  Our co-founder and R&D Director Toni Manzano is a team lead for Xavier University AI in Manufacturing Team where, with the help of FDA officials and industry professionals, they address how the power of AI can be used to optimize manufacturing operations.  The goal is to define requirements and explore the knowledge we have of AI and apply it to the ‘real world’. They aim to share existing use cases of AI in manufacturing and also show how to implement these technologies in manufacturing operations.  They are working to define AI in manufacturing, publish examples of use cases on pharma and medical device manufacturing using AI, and develop a white paper to capture the learnings developed from the early AI solutions being deployed in manufacturing.

The capabilities operations can achieve with the use of AI is exponential (reduce costs, improve time to market, and better quality).  Companies are exploding with data and rather than letting it sit, stored away in cabinets and data silos, we see a tremendous amount of power in all of that unused data.  This initiative will elevate all of our understanding of how AI works and advance the life sciences industry.

If you are interested in joining the AI in Manufacturing Team, you can sign up here: https://www.xavierhealth.org/ai-initiative-working-teams