Andreas Eschbach, eschbach
In 2020, a Deloitte survey found that 93% of manufacturers across all industry sectors believed AI would be critical to driving innovation and growth. However, the same survey found that up to 91% of AI projects had yet to meet expectations. Over the past four years, AI capabilities, products and applications have exploded, and a growing number of manufacturers in the pharmaceutical sector and beyond have begun to integrate AI into various aspects of their operations.
What are the best uses of AI in pharmaceutical manufacturing right now? And are manufacturers finally starting to see the ROI of their investment in AI?
From digitalization to the age of AI
AI builds on the foundations of digitalization laid over the last decade. Digital data from sensors and equipment connected to the Industrial Internet of Things (IIoT) is a rich source of historical information about processes and equipment. Many manufacturers have also digitized work notes, inspection and maintenance logs, and other human-generated data sources. When these data sources are centralized and integrated, they provide a holistic view of facility operations that can be used for advanced analytics, process automation and other technology initiatives, including AI.
AI is an umbrella term that covers a wide range of tools and applications, from general-purpose large language models (LLMs), like ChatGPT, which has captured the public imagination, to AI models. Highly specialized AI used to optimize production processes, predict equipment. maintenance needs or detection of quality deviations using machine vision. AI tools such as machine learning (ML), robotic process automation (RPA), and natural language processing (NLP) are playing an increasingly important role in manufacturing operations.
Many AI applications used in pharmaceutical manufacturing rely on ML, which gives AI models the ability to learn and make data-driven decisions. Training an AI model for a particular task requires a lot of data, which must be both accurate and relevant to the task at hand. The large volumes of data generated by IIoT devices and digitized notes and logs provide the basis for AI models tailored to a particular industry or even a particular company. AI tools use this data to detect patterns and correlations that can enable process automation or support human decision-making and insights. AI is able to sort through large amounts of structured and unstructured data in seconds, bringing hidden information to the surface.
The impact of AI in manufacturing
AI is already making inroads in the manufacturing sector, particularly in the pharmaceutical sector. A 2024 study by Researchscape found that 70% of manufacturers have implemented some form of AI into their operations and 82% plan to increase their AI budgets over the next year.
How are manufacturers using AI? Across industries, AI tools are infiltrating nearly every aspect of manufacturing, from customer service to supply chain management. According to a May 2024 report from the National Association of Manufacturers (NAM), the top uses of AI in manufacturing include manufacturing and production operations (39%), inventory management (33%), quality operations (24%), R&D (24%), information and operational technologies (21%) and equipment maintenance (17%). These numbers will almost certainly increase significantly over the next two to three years as more AI tools come to market and achieve higher penetration. The same report reveals that among manufacturers who have adopted AI:
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72% reported cost reduction and improved operational efficiency;
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51% reported improved operational visibility and responsiveness; And
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41% reported improved process optimization and control.
Top Uses of AI in Pharmaceutical Manufacturing
In the pharmaceutical industry, companies use AI throughout the product lifecycle. Pharmaceutical companies are using sophisticated AI models to accelerate drug discovery and development through faster identification of potential drug candidates, advanced molecular design and protein folding, and clinical trial optimization . In fact, a 2024 McKinsey report estimates that AI could generate between $60 billion and $110 billion in value per year for the pharmaceutical industry.
For pharmaceutical manufacturing in particular, key uses of AI include:
Quality control: AI systems maintain quality by continuously monitoring production processes in real time, using machine learning algorithms to detect any deviations or anomalies that may indicate a quality issue. Advanced AI models can analyze data from spectroscopy and other chemical analysis methods to verify that a drug’s active and inactive ingredients are present in the correct proportions. Machine learning algorithms and computer vision can also be used in quality control to detect defects and inconsistencies with much greater accuracy and speed than human inspectors.
Process control and optimization: AI is used to continuously analyze data from manufacturing processes and dynamically adjust parameters to maintain optimal production conditions, improve yield and reduce waste. In batch manufacturing, AI algorithms can optimize the sequence and timing of batch processes to maximize throughput and minimize downtime.
Predictive maintenance: AI-based predictive maintenance uses machine learning models to analyze data from equipment sensors and maintenance logs to predict when machines are likely to fail. By predicting potential problems before they occur, manufacturers can schedule maintenance at the most opportune times, reducing downtime, extending equipment life, and avoiding unplanned and costly production interruptions.
Pharmaceutical companies are using sophisticated AI models to accelerate drug discovery and development.
Inventory management: AI helps optimize inventory levels by predicting demand and efficiently managing supply chain logistics. AI algorithms can analyze trends, monitor inventory in real time, and automate replenishment processes. This helps ensure that pharmaceutical manufacturers have the right amount of materials and products, reducing the risk of having excess inventory or stock-outs.
Regulatory Compliance: AI systems can automate documentation and reporting processes to ensure compliance, verifying that all operations meet strict regulations governing pharmaceutical production. This reduces the risk of non-compliance, simplifies audits and improves overall operational transparency.
As AI becomes more integrated into operations, more advanced uses are emerging, including process simulation and the use of digital twins, which are virtual models of physical manufacturing processes. These models simulate various scenarios to predict the impact of changes in the process on the final product. This allows manufacturers to experiment with process adjustments without disrupting actual production, leading to optimized processes and reduced trial and error in the physical environment.
Many pharmaceutical manufacturers are already seeing returns on their AI investments. For example, a McKinsey report on intelligent quality control systems estimates that pharmaceutical companies can increase the productivity of quality activities by 50 to 100 percent and reduce quality control laboratory turnaround times by 60 to 70 percent by integrating AI tools. The report also reveals that digitalization and automation have led to a reduction of more than 65% in overall spreads, as well as 90% faster times to close for some businesses.
Solve production problems with AI
AI is transforming pharmaceutical manufacturing by providing powerful tools for problem solving and continuous improvement. Some of the most effective uses of AI include identifying production bottlenecks and recurring issues, conducting root cause analysis, and proposing potential solutions for faster problem resolution. These capabilities improve the efficiency and effectiveness of troubleshooting efforts on the production floor.
AI excels at sorting structured and unstructured data to identify patterns and correlations in historical records such as shift logs, maintenance and inspection logs, equipment data and data from historical, factory production management (PPM) and manufacturing execution system (MES). software. Unlike humans, AI can process large amounts of data quickly and efficiently, revealing hidden insights critical to process optimization and problem solving.
For example, intelligent AI-based search can provide solutions using a combination of machine learning and natural language processing to aid in problem solving. These capabilities allow staff to ask questions in simple language (for example, “Why is the viscosity low in this lot of product A?”) and receive potential causes and solutions based on historical data from the product. historian, work notes, maintenance and equipment logs. , and other sources. This feature saves hours of manual analysis, delivering the most relevant results in seconds and significantly speeding up root cause analysis and problem resolution.
AI tools enable teams to improve efficiency, increase productivity, reduce quality issues, and improve compliance. It is important to note that these AI tools are not intended to replace humans on the production floor, but rather to augment and improve human capabilities and decision-making. AI is an essential part of Industry 5.0, which emphasizes collaboration between humans and technology. By leveraging the strengths of humans and AI, pharmaceutical manufacturers can achieve significant operational improvements and maximize the return on their AI investments.