The biopharmaceutical industry is experiencing a surge in demand for large molecules, driven by advances in biotechnology and the need for innovative treatments for complex diseases. With an annual growth rate of around 6 percent, the sector’s role in global healthcare is more critical than ever. However, existing production capacities are struggling to keep up, even though the pharmaceutical industry is spending around $57 billion every year on new facilities. Slow ramp-ups are part of the problem, with technology transfer and workforce upskilling extending the lead time before new plants achieve steady-state production.
This environment is putting more pressure on the industry’s existing production sites, which are being asked to find ways to boost output without big investments in new capital equipment. AI and advanced analytics could be a shot in the arm for the sector, helping sites optimize existing resources and enhancing throughput to meet the rising demand for large molecules.
Key challenges in biopharma production
Biopharma manufacturing is a sophisticated, variable, and fragile process. Biological raw materials, including cell banks, culture media, and serum components, may have characteristics that differ significantly between product types or individual lots. Manufacturing sequences involve multiple steps with complex interactions and interdependencies across end-to-end processes. Successful production depends on the tight control of hundreds of critical parameters. Biopharma facilities aiming to increase throughput typically face significant challenges in three areas: low utilization of equipment and personnel, high process variability, and slow, expert-dependent decision making.
Poor visibility of equipment and personnel utilization
Redundant equipment, multiproduct lines, and intricate setups make it difficult for production managers to optimize processes, due to a lack of visibility into equipment utilization. Identifying bottlenecks and understanding time-critical processes is challenging, often leading to perceived capacity shortages. Detailed analyses frequently reveal underutilized equipment, with bottleneck assets spending too much time idle and producing fewer batches than they could. According to McKinsey’s proprietary Pharmaceutical Benchmarking and Opportunity Sizing (POBOS) database for biologics drug substances, the top-quartile bioreactor throughput in the industry is 17 drug substance batches per year, while median sites produced only 14 batches per reactor. Closing that gap would increase output at those median sites by almost 25 percent (Exhibit 1).
Variable yields
Biomanufacturing processes involving live cells are inherently variable, presenting challenges in standardizing and optimizing production. Numerous parameters—often exceeding 1,000 for a given product–process combination—affect yield distribution. Efforts to increase titer or yield typically involve optimizing a subset of these parameters, yet variability remains a significant hurdle. Increasing yield reduces the number of batches needed to meet the annual target in kilograms, thereby freeing up capacity for additional batches. It saves money too: Analysis of a year’s production data at multiple sites revealed the potential to reduce the cost of goods manufactured (COGM) nearly 10 percent by closing the gap between average and best achievable yields.
Expert-dependent decision making
In the biopharmaceutical industry, decision making related to operations, scheduling, and yield optimization predominantly relies on experience and expertise. This dependence on human judgment introduces variability and delays the implementation of operational changes. The industry’s cautious nature and traditional process-driven mindset further impede the swift adjustments necessary for industrial optimization. In a POBOS survey, respondents at 20 biologics drug substance plants said their facilities gather process data at a high quality and have data collection infrastructure. However, only 2 respondents (translating to 10 percent) indicated they use advanced analytics tools for regular improvement actions or real-time optimization (Exhibit 3).
How AI can unleash capacity and productivity in biopharma
Addressing the challenges in biomanufacturing requires innovative solutions that capture the complexity of processes and human–machine interactions. The biopharma industry generates vast amounts of data, yet this data is often underutilized. AI and advanced analytics can harness this data to optimize production processes dynamically.
Following are four examples of approaches that have helped biopharma players improve the productivity and utilization of their assets and people, thereby reducing their cost of goods sold (COGS).
Digital batch schedule optimization
AI-driven optimization of batch schedules can maximize equipment utilization and reduce the duration of critical paths, especially in complex production environments where different products compete for the same assets and production teams. By analyzing real-time data, AI can identify bottlenecks and optimize the sequence of operations, leading to enhanced overall productivity. By integrating data from equipment sensors, production logs, and enterprise resource planning (ERP) systems, AI-driven analytics offer a holistic view of the manufacturing landscape. This visibility enables managers to identify underutilized assets and optimize production schedules, ultimately enhancing overall efficiency, and meet demand more effectively. This optimization also reduces the risk of delays and ensures that production schedules are aligned with market demand, enhancing overall responsiveness.
Dynamic operator allocation
Real-time AI tools can dynamically allocate operators based on real-time process status, ensuring optimal deployment aligned with skills and process needs. These tools analyze current operational conditions and adjust operator assignments to match the required expertise and availability, improving efficiency and reducing downtime.
Dynamic operator allocation enhances workforce flexibility and responsiveness. By continuously monitoring operational conditions and adjusting assignments in real time, companies can adapt to changing production requirements and minimize the impact of unexpected disruptions.
Yield improvement with advanced analytics
AI-driven analytics can continuously monitor and adjust processes in real time to maximize yield and mitigate variability. Using machine-learning algorithms, these systems can predict yield outcomes based on various parameters and make real-time adjustments to optimize performance. By analyzing historical data and identifying patterns, AI-driven systems can predict potential deviations and implement corrective actions before they affect yield. This proactive approach ensures that processes are always operating at their highest potential, reducing variability and improving overall consistency.
Higher yields don’t just help biopharma players increase their output. They also reduce the quantities of raw materials, energy, and other inputs per unit of production, which cuts costs and improves the cost and sustainability of manufacturing operations.
Agile workforce development
Targeted training to bridge skill gaps can foster flexibility and responsiveness in the workforce. AI tools can identify areas where employees need additional training and provide customized learning programs to address these gaps. This approach accelerates the time to qualification and supports the execution of critical and rare operations, ensuring that the workforce is always prepared for new challenges.
In the biopharma industry, the complexity of production processes requires a highly skilled workforce. Traditional training methods may not always align with the rapidly changing demands of the industry. AI-driven workforce development tools can analyze data on employee performance, identify skill gaps, and provide targeted training programs to address these gaps. This ensures that employees are continuously developing their skills and staying up-to-date with the latest industry trends.
Agile workforce development also enhances overall flexibility and responsiveness. By continuously monitoring employee performance and providing real-time feedback, companies can ensure that their workforce is always prepared to handle new challenges and adapt to changing production requirements.
From promising pilots to transformative potential
Leading pharmaceutical and biopharma players have already applied these approaches individually or in combination to achieve significant throughput improvements and manufacturing cost reductions. A global pharma sterile player used AI-based in-flight optimization to increase production yields by 15 percent. A North American biopharma contract manufacturer used AI to optimize equipment utilization and workforce deployment, helping it raise upstream throughput by 15 percent and downstream throughput by 30 to 60 percent across different sites. A midsize European biopharma player applied a comprehensive suite of AI and analytics approaches to increase its throughput by 29 percent in upstream operations. Across the sector, other leading companies are rolling out novel generative AI use cases to support critical tasks in production, supply chain, maintenance, and technical development.
If the whole industry were to follow the example of biopharma leaders by increasing productivity, throughput, and yield to top-quartile levels, the biopharma sector could achieve savings of between $30 billion and $40 billion per year, as well as significant avoided capital expenditure.
The integration of AI and advanced analytics into manufacturing processes has transitioned from an opportunity to an imperative for maintaining competitiveness in a rapidly evolving industry. Strategic investments in these technologies and their comprehensive utilization can catalyze unparalleled productivity and efficiency gains. For the biopharmaceutical sector, this means being better positioned to meet the escalating global demand for innovative treatments while simultaneously reducing costs and enhancing sustainability. The future of biopharmaceutical manufacturing is upon us, and the pioneers who embrace these advancements now will be the ones to define the industry and lead it forward.
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