The rise of AI and data in pharmaceutical and chemical manufacturing
In our 13th iteration of the Technology Circle (24.10.23), we had a new record of 9 guest speakers! In 10–25 minutes per speaker (or speaker pair), we learned about the impact of digitalization in pharma and chemical production.
In this article, you’ll read about digital twins, AI’s transformative power in manufacturing, the crucial application of data science within regulatory frameworks and the increasing importance of cybersecurity.
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The rise of digital twins in pharma production
Digital twins have emerged as a pivotal technology in pharmaceutical manufacturing.
”A digital twin is a digital representation of an intended or actual real-world physical product, asset, system or process that serves as the effectively indistinguishable digital counterpart of it for different practical purposes.
Alessandro PloznerTechnical Process Manager, F. Hoffmann-La Roche AG
The concept of digital twins in pharmaceutical manufacturing has seen remarkable evolution. They’ve transitioned from basic models to sophisticated systems that mirror complex pharmaceutical processes.
Currently, digital twins are essential in various aspects of production, from streamlining processes to enhancing decision-making with real-time data.
Alessandro showcased several highly interesting use cases for digital twins, such as:
- Guided learning sessions with VR. Learning sessions for equipment operation and maintenance can be done in a virtual reality. This provides immersive, lifelike training environments without the risk of injury to the operator or damage to the equipment.
- Dashboards with maintenance Information. Digital twins provide interactive dashboards with comprehensive maintenance information. These dashboards display real-time data on equipment performance, maintenance schedules and potential issues, enabling proactive maintenance and minimizing downtime.
A more efficient, safe and compliant production processes
Digital twins are revolutionizing pharmaceutical and chemical manufacturing.
You can test dozens of optimizations to your manufacturing process simultaneously in the digital world before implementing the best one in the real world. They provide valuable real-time data and insights that aid decision-making in production management and can predict manufacturing problems before they even happen. In the realm of training and safety, digital twins introduce innovative, immersive virtual environments, leading to safer practices and better knowledge retention.
Even though the case for the digital twin’s positive impact is easy to make, its implementation doesn’t come without challenges.
Creating a thoroughly accurate digital twin is essential for facilitating real-time interaction between the two. Being even 1% off can skew the data to a degree it becomes unusable.
Securing internal support and buy-in from various stakeholders is vital for the success of your digital twin projects. Everyone has to be on board and understand the project.
Additionally, fortifying the digital twin infrastructure against potential cybersecurity threats is a significant concern that must be addressed.
To learn more about digital twins, read our summary of Technology Circle 10:
Harmonize the advanced capabilities of data science with the stringent requirements of GMP
Ensuring data integrity and security in a tightly regulated environment is one of the main challenges. Adapting data science methodologies to fit within the strict confines of GMP regulations is also crucial. Managing the complexity of data integration across various pharmaceutical processes poses an additional hurdle.
To address these challenges, Stefan Kramberg, IT System Engineer at ControlTech Engineering AG, partner in the AI4SME program, suggests:
- Implementing robust data management systems that can securely handle sensitive information and maintain compliance.
- Continuously training staff in data science applications within GMP contexts.
- Collaboration between regulatory experts and data scientists to ensure that data-driven advancements meet the high standards set by GMP.
The future is bright!
The more data we can collect, store and efficiently analyze, the more important data science becomes. The potential for automated data analysis and predictive modeling is particularly promising.
The integration of data science in GMP environments represents a pivotal advancement in pharmaceutical and chemical manufacturing.
As the industry continues to embrace these innovations, the marriage of data science with GMP standards will continue to set the bar higher and higher for efficiency and quality in pharmaceutical production.
Data science in GMP environments
Good Manufacturing Practice (GMP) is crucial in pharmaceutical and chemical manufacturing. It’s a set of systems that ensure products are consistently produced and controlled according to quality standards.
Extracting actionable knowledge from large amounts of data enables more precise control over manufacturing processes, leading to more consistent and higher-quality products.
It also improves process efficiency by allowing for predictive maintenance, process optimization and real-time decision-making based on data insights. This combination ensures that manufacturing processes not only adhere to regulatory standards but also benefit from the efficiency and accuracy that data science offers.
Cybersecurity in pharma
In the digital era, cybersecurity has become a major concern in pharmaceutical and chemical manufacturing.
Production networks can be fragile and, therefore, need robust security measures. Cybersecurity in pharma now extends beyond protecting data. It’s become essential for ensuring the uninterrupted functionality of production systems.
The integration of data and AI in cybersecurity strategies is becoming key in identifying vulnerabilities and protecting your company against potential cyber attacks, as well as in maintaining the integrity and reliability of pharmaceutical manufacturing processes.
Rising Cybersecurity threats
Cybersecurity threats in the pharmaceutical sector are on the rise. The expansion of digital technologies in manufacturing has opened up new vulnerabilities, from data breaches to disruptions in production lines.
These threats are not just external. Internal risks, like accidental data leaks or system misconfigurations, are big problems.
The increasing sophistication of cyber-attacks, including ransomware and phishing, calls for a more robust and proactive approach to cybersecurity.
What should you do for your cybersecurity?
Dr. Tim Senn from Narrowin GmbH offered practical advice for pharmaceutical and chemical manufacturing companies. Your cyber security strategy should include:
- Implementing layered security measures
- Adopting a Zero Trust‘ approach
- Ensuring regular updates and patches to security systems
- Training your employees in cybersecurity awareness
- Using technologies like AI and machine learning for real-time threat detection and response
Dr. Tim Senn
Cofounder, Narrowin GmbH
For example, AI can be used for continuous monitoring and analysis of network traffic, helping to identify and respond to anomalies in real time. Data analytics aids in understanding patterns of cyber threats to enable predictive cybersecurity measures.
The integration of AI and data analytics in cybersecurity strategies not only strengthens defense mechanisms but also provides deeper insights into potential vulnerabilities, ensuring a more resilient and secure pharmaceutical manufacturing environment.
To learn more about cybersecurity, read our summary of Technology Circle 9:
The future of AI in pharmaceutical and chemical manufacturing
AI’s role in enhancing production efficiency and equipment effectiveness will continue to evolve.
AI will become more integrated into pharmaceutical and chemical manufacturing processes, focusing on real-time optimization and predictive maintenance. This will lead to smarter, more efficient production lines that can adapt and respond to operational needs automatically and in real time.
Felix Georg Müller and his company Plus10, winner of the i4Challenge 2022, bring a bit of this future into the present. The company offers tools that fully automate the optimization of production lines and machines to enhance efficiency and reduce downtime.
For example, Shannon® is a knowledge management tool that focuses on streamlining handovers and ramp-ups in automated medical device production lines, thereby enhancing the efficiency and reliability of the manufacturing process.
It continuously analyzes data from production processes and machinery and uses AI algorithms to identify patterns, predict potential issues and recommend optimizations.
With real-time data, Shannon® can make immediate adjustments to improve efficiency and reduce the likelihood of equipment failure. Its AI-driven analysis also facilitates better decision-making, ensuring that production lines operate smoothly and efficiently.
How AI and machine learning transform manufacturing
Just like digital twins, AI has been a hot topic in the pharmaceutical industry for a while.
Charles Ezan and Noémie Prin from Axom, winner of the i4Challenge 2022, showed us, by their own example, how AI can enhance processes and improve production control.
Axom uses AI to enhance processes such as counting stations for inventory management, narcotics reconciliation for regulatory compliance and production control for operational efficiency.
Their approach to AI involves using advanced algorithms to automate and optimize these processes, significantly improving accuracy and efficiency.
To learn more about AI, read our summary of Technology Circle 11:
Lessons learned
Implementing an AI solution in a strictly controlled and regulated environment is difficult. Charles and Noémi shared some lessons they’ve learned along the way.
- Understanding the capabilities of AI in its current state was key to overcoming misconceptions. With all the hype around AI, it’s almost impossible to live up to the idea in people’s minds.
- Project management has to involve different teams, people across the internal hierarchy and even external stakeholders. Everyone has to be on board.
- It’s important to adapt to different product types and manufacturing environments to make your AI solution applicable to a broad range of use cases.
Colomba Link GmbH: Revolutionizing pharma with IoT
Finalist of the i4Challenge 2023
Normand Overney
CEO and Co-Founder, Colomba Link GmbH
Colomba Link GmbH is pioneering the integration of IoT in pharmaceutical manufacturing. Their innovative solution, combining Hardware-as-a-Service (HaaS) and Software-as-a-Service (SaaS), offers a secure, scalable and intelligent system for real-time monitoring of manufacturing processes.
This approach not only enhances operational efficiency but also ensures data security, a key challenge in the pharma industry.
Reasonance GmbH: Empowering pharma with on-premise machine learning
Finalist of the i4Challenge 2023
Todor Kostov
CEO Reasonance GmbH
Reasonance GmbH is at the forefront of bringing machine learning solutions to the pharma manufacturing floor. They focus on enabling those with deep domain knowledge but no data science knowledge to utilize data science tools and methodologies.
With end-to-end machine learning capabilities, Reasonance GmbH is transforming how pharmaceutical companies leverage data science for operational excellence.
Never stop innovating, never stop collaborating!
Innovation and collaboration go hand in hand. It’s only when we merge ideas and expertise that true advancement occurs. In the dynamic field of pharmaceutical and chemical production, this idea is most important.
The Basel Area Industrial Transformation initiative offers a powerful ecosystem for tackling the transition of the manufacturing industry towards smart technologies and sustainable production methods. The initiative offers a collaborative platform with events and catalyst projects within the broader Basel Area (Upper Rhine including France and Germany) and is the host of the i4Challenge program, the accelerator that develops startups, SMEs and new ideas in the field of Industry 4.0.
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Sébastien Meunier Director Industrial Transformation.
Sébastien Meunier joined Basel Area Business & Innovation in 2016. Prior to this, he was Cleantech Manager at i-net innovation networks switzerland.
”Are you interested in innovation and networking? In our Trinational Industry 4.0 Technology Circles we bring together researchers, developers, entrepreneurs and other stakeholders for cross-disciplinary exchange.
Sébastien MeunierDirector Industrial Transformation
