A legacy of siloed functions in pharma and biotech organizations inhibits innovators from developing and commercializing therapies with speed and efficiency. Manufacturing organizations rarely have access to key insights and information from research and development (R&D), quality, supply chain, sourcing, procurement, and logistics functions and systems. This is particularly true for product and process data stored across enterprise applications. R&D professionals may use Digital Knowledge Management (DKM) systems to manage product and process data, including formulation and packaging information, but often this information is not easily accessible to the manufacturing organization that has to make daily production decisions based on available supply and capacity. Demand forecasting, sales and operations (S&OP) and supply planning functions may use purpose-built solutions and enterprise resource planning (ERP) systems to align demand, supply, capacity, sourcing events and purchase orders, but when they run into supply constraints or demand fluctuation, manufacturing cannot react quickly, preventing them from making proactive decisions on how to best meet production demand.
In March 2020, the Pharmaceuticals industry faced one of its most public-facing and time-pressing challenges to date: develop, manufacture, and distribute a vaccine for COVID-19 globally as quickly and safely as possible. While sheer human perseverance played a pivotal role in driving the notable speed to market for the vaccine, this approach is not sustainable nor practical for commercial drug products. Digital methods and tools should be leveraged to achieve similar results in a sustainable, efficient, and profitable way. Pharmaceutical manufacturers now face the need to confront these challenges head-on and embrace connectivity across the value chain, starting with laying the digital foundation for innovation in the form of Digital Knowledge Management and Pharma 4.0.
The pharma industry’s product development processes is complex and lengthy, but digital technologies can help. Here are the top AI use cases.