DRL MCQ 1.pptx

IR 4.0 Industry 4.0—also called the Fourth Industrial Revolution or 4IR—is the next phase in the digitization of the manufacturing sector, driven by disruptive trends including the rise of data and connectivity, analytics, humanmachine interaction, and improvements in robotics. Pharma 4.0 is a fra...

IR 4.0 Industry 4.0—also called the Fourth Industrial Revolution or 4IR—is the next phase in the digitization of the manufacturing sector, driven by disruptive trends including the rise of data and connectivity, analytics, humanmachine interaction, and improvements in robotics. Pharma 4.0 is a framework for adapting digital strategies to the unique context of pharmaceutical manufacturing. It introduces more connectivity, increased productivity, simplified compliance, and the ability to leverage production information to respond to problems as they emerge1. The emerging technologies that characterize Industry 4.0— from connectivity to advanced analytics, robotics and automation—have the potential to revolutionize every element of pharma- 4 IR Connectivity, data, and computational power: cloud technology, the Internet, blockchain, sensors Human–machine interaction: Analytics and intelligence: advance d analytics, machine learning, artificial intelligence Advanced engineering: additive manufact 4IR and Pharma 4.0 • Implementing new Industry 4.0-based manufacturing concepts in Pharma 4.0™ requires alignment of expectations, definitions, and interpretation, with global pharmaceutical regulations. • While Industry 4.0 has been called a new industrial revolution, Pharma 4.0™ implementation will more likely resemble an evolution in which digitalization and automation meet very complex product portfolios and cycles • Digitalization, an important component of Pharma 4.0™, will connect everything, creating new levels of transparency and adaptivity for a “smart” plant floor. This will enable faster decision-making, and provide in-line and on-time control over business, operations, quality, and regulatory compliance. Notably, this new connectedness will require higher levels of security, since linked systems heighten vulnerability. Key Success Factors for the introduction of Data Science in the Pharma Industry “AI isn’t just a new set of tools. It’s the new world” From automation to augmentation, generative AI and beyond, AI is changing everything. $15.7 trillion— that’s the global economic growth that AI will provide by 2030, according to PwC research. Challenges in Pharma Manufacturing 1 Lack of visibility 2 Too much data Without real-time Pharma manufacturing visibility, production creates an overwhelming teams struggle to make amount of data. Without data-driven decisions in proper organization and a timely manner. visualization, it can be difficult to identify trends or patterns. 3 Competitive pressure Innovative companies constantly look for ways to cut costs while increasing production quality. • TRUST AI-ML Driven PSC or Digital PSC • AI-ML provides the wherewithal for building Trust, Visibility & Control • VISIBILITY CONTROL 9 Digital transformation and digitalization are on the agenda for all organizations in the Pharmaceutical industry. But what are the main enablers of intelligent manufacturing? We hypothesize that data science–derived manufacturing process and product understanding is the main driver of digitalization in the bioprocessing industry for biologics manufacturing. Companies that use advanced analytics to improve operations have the potential to transform the Pharmaceutical manufacturing industry Pharma supply chains in a post-Covid-19 world will consist of resilient systems and processes infused with reliability, transparency and intelligence, at every stage. To achieve operational excellence, Pharma manufacturers must develop capabilities in advanced analytics. McKinsey & Pharma and Healthcare Logistics - Services | Maersk AI-ML-DS : Pre-requisites & Use cases From “Molecule to Market” there is no spectrum of the Pharma value chain that AI-ML-DS cannot impact including Manufacturing. However, KSF’s include : Data Driven Culture Search | Pharma Manufacturing https://youtu.be/xJSapAmgMRU https://youtu.be/wcnXPnPV64g https://youtu.be/EoYvUx1v8vY AI in Pharma and Life Sciences | Deloitte US https://youtu.be/WRUliRQ2MHA Team work Appropriate Skills Design Thinking AI-ML Infrastructu re AI-ML in Pharma Use Cases across the Value Spectrum in general and Manufacturing in particular AI Value Creation for Pharma