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AI and Continuous Manufacturing

Olena Gligorich

Hello Regulatory Science Group,

Artificial Intelligence implementation and application have been proliferating across many companies and industries (financial, healthcare and so much more). I would love to hear from the Continuous Manufacturing Community on how AI is transforming this field. While there are still many questions need to be asked and answered, here are a few to highlight and kick off this discussion:

  • Should we anticipate regulatory obstacles and considerations?
  • What are our learnings and reflections so far regarding the AI impact and transformative value on the CM field?
  • Have you read about leaders in the industry who currently pioneer AI adoption?
  • Where do we expect to see the immediate AI impact on the CM field and process?

Please share specific examples, articles, presentations, webinars, or other materials to further advance our collective knowledge and learn together about this exhilarating and transformative topic!

  • Hi Olena,

    This article, "The Rise of Continuous Manufacturing in Pharma," has an interesting section on AI. It notes the capability to analyze large amounts of data from sensors, and how that can help with scheduling, material flow, and resource management:

    "AI-powered systems can optimize production schedules by considering factors such as machine availability, worker skill sets, order priorities, and production constraints. This leads to improved efficiency, reduced downtime, and better resource allocation. Additionally, AI can optimize material flow within the continuous process by identifying bottlenecks and inefficiencies, ultimately reducing inventory levels and improving throughput.

    Furthermore, AI can analyse energy consumption patterns, predict equipment failures, and optimize maintenance schedules, resulting in significant cost savings and increased equipment uptime."

    Eager to hear from the rest of the group about what interests you most AI & CM!

  • in reply to Kaitlin Baumer

    Thank you Katie so much for sharing the article and section on AI! My highlights are the following:

    • It will be fascinating to observe how the benefits will be realized in some of these areas (resource management, scheduling, gaining equipment efficiencies) that have a reputation of being “difficult to optimize” with current tools and best practices
    • Another observation, should we anticipate changes to the industry standards in the future? For example, overall equipment effectiveness

    I am also eager to hear more from others on their key highlights and aha moments!

  • in reply to Olena Gligorich

    Hello Katie and Olena, 

    Data analytics and AI applications have recently gained much attention in pharma industry as they can analyze vast amounts of process and manufacturing data in a shorter period of time. This is helping manufacturers in identifying patterns, predicting potential issues, and optimizing processes to meet quality standards. Digital twin for e.g is a virtual replica of your manufacturing process, that can gather real time data and where you will be able to simulate the process to predict the possible performance outcomes and evaluate what if scenarios without actually doing a physical process. And with advanced manufacturing technologies evolving everyday, pharma companies can leverage AI and automation to have better control over their process, produce drugs with consistent quality and increase overall equipment efficiencies.

  • in reply to Rohit Lokhande

    Thank you Rohit for providing this excellent summary and definitions, compelling points and case for the data analytics tools, AI and digital twin. Were you attending the first CM webinar in December of last year? The digital twin tool came up during this discussion, and it resonated with me your points regarding the faster to market path and quality. 

  • Hello Regulatory Group,

    I came across this article “Digital twins: The key to unlocking end-to-end supply chain growth” with a robust discussion and a compelling case for the Digital Twin software through the lens of supply chain. Also, Article highlights a tremendous future growth: “Market analysis indicates the global market for digital twins will grow about 30 to 40 percent annually in the next few years, reaching $125 billion to $150 billion by 2032”. I found many valuable points and insights outlined in this article. I will share my top 3 insights:

    1. Enables quick and effective response to internal and external demands and pressures
    2. Rapid, wholistic modeling capabilities and analytics
    3. Integration with already existing systems

    Did you find it valuable, relevant, applicable? Does it help you to shape your processes, planning and forecasting? I look forward to hearing from all of you.

  • in reply to Olena Gligorich

    Here is another engaging article on the “Digital twins: The next frontier of factory optimization”. Many valuable highlights on the digital twin technology being a leading solution for scaling, resilience and efficiency in the rapidly changing industry. Article is describing it as “The “factory of the future” is here and unlocking value today.” Article provides a deeper dive into how technology works and delivers value. Also, Exhibit 2 illustrates how to build a modular, scalable digital twin outlining the key building blocks. Interestingly, article concludes with a paragraph discussing “How to get started” mentioning certain challenges (capabilities knowledge, in-house talent, etc) and providing the following approaches and considerations to ensure success:

    • Agility
    • Cross-Functional Collaboration
    • Proof of Concept
    • Minimal Viable Product
    • Machine Learning Optimization

    What is your level of readiness to implement the digital twins technology? Are these approaches comprehensive and resonate with subject matter experts to ensure success in the continuous manufacturing? I look forward to your list of how tos to unlock the full potential of digital twins and create value.