Accelerating problem resolution in pharma processing with AI assistance
In the vast ocean of process manufacturing historical records — shift notes, inspection logs, plant management systems — lies a treasure trove of solutions to the myriad problems operators face daily. Artificial Intelligence (AI) can unearth this hidden knowledge, enabling workers to resolve issues swiftly and keep operations running smoothly. By equipping human workers with AI assistance, pharmaceutical processors can expedite problem-solving and preserve invaluable historical knowledge.
Digital technologies such as AI, Machine Learning (ML), Internet of Things (IoT) and blockchain are revolutionizing the way pharmaceutical processors operate. These technologies enable real-time monitoring and control of manufacturing processes, predictive maintenance of equipment, traceability of products and personalized medicine.
The challenge of hidden knowledge in pharmaceutical manufacturing
Every day, operators and technicians in a pharmaceutical processing facility confront numerous problems that demand resolution: Out-of-specification (OOS) products, process deviations, batch variability, unexpected equipment malfunctions, calibration issues, contamination in raw materials or final products, and more.
Most of these problems are not without historical precedent. Seasoned workers can often recall a similar issue from the past and the steps taken to resolve it. (“Viscosity is higher than normal. We should raise the temperature of the transfer line.”) This experience-based knowledge, known as tacit knowledge, is priceless in intricate industries such as pharmaceutical processing.
However, it is becoming increasingly elusive. As experienced workers retire and younger workers frequently switch jobs, companies face a growing tacit knowledge gap. Even when experienced workers are on hand, human memory can be unreliable, leading problem-solvers astray.
The loss of tacit knowledge, either through talent attrition or the frailty of human memory, presents a significant barrier to problem recognition and resolution. Pharmaceutical manufacturing processes are often highly complex, with many steps where something can go wrong. It can be extremely difficult to pinpoint exactly where a process went off the rails to produce a specific undesirable result. Without a starting place based on prior knowledge and historical data, operators and technicians must reinvent the wheel each time a problem is encountered, resulting in lost time, lower productivity and increased costs. Slow problem resolution can also lead to delays in production, compromised product quality and potential regulatory non-compliance, ultimately impacting the company’s bottom line and its ability to deliver safe and effective products to patients.
AI to the rescue
This is where AI steps in. According to a survey by the National Association of Manufacturers (NAM), 72% of surveyed manufacturers reported reduced costs and improved operational efficiency after deploying AI technology. AI tools such as ML and natural language processing (NLP) are designed to tackle this kind of knowledge management challenge. The right AI tools can not only help workers find the information they need but can also suggest potential solutions based on historical knowledge, significantly accelerating the problem-solving process.
How it works
A Plant Process Management (PPM) system with AI capabilities aggregates data from multiple sources, such as the historian, MES and PI systems, along with human-generated data from shift, inspection and maintenance notes. When a user encounters a specific problem, they simply enter the problem in plain language. The AI then sifts through all the available data, both structured and unstructured, to find similar historical incidents. It uses this historical information to suggest potential solutions based on past successes or recommend additional tests to resolve problems.
Using ML, operators at manufacturing facilities can diagnose and predict failure while helping to shift to a more proactive operation that will reduce downtimes, decrease deviations and increase production rates. The AI captures these activities as part of an overall digitalization solution that helps future workers with issues.
These suggestions are not meant to replace human expertise and knowledge, but to augment human abilities and provide a starting place to speed up problem resolution. Operators, technicians, engineers and QC managers can quickly find historical precedents for current problems, as well as what actions were taken and what results were achieved. Based on this information and the smart AI suggestions, they can decide the best course of action. In this way, the AI program acts as an experienced mentor and guide, surfacing tacit knowledge amassed by large numbers of employees in different operational roles over many years.
To make this type of AI system work, it is important to tune the AI to the specific language and processes in use at a company or production site. The language model is not a general AI, such as ChatGPT, but rather a model specifically trained on industry and company data. This allows it to understand what is being asked and interpret both structured and unstructured data to find relevant information in the historical record. In turn, this allows it to generate appropriate solution suggestions for specific problems.
In an increasingly complex manufacturing environment, knowledge management is a critical competitive advantage. A smart solution program makes hidden knowledge in historical records accessible and usable for everyone, from front-line operators on the floor to process engineers, quality control and maintenance departments. Augmenting human skills and knowledge with AI will facilitate faster problem-solving and empower people to make better operational decisions.
For pharmaceutical processors, the knowledge obtained by both machines and humans becomes a necessity when creating a Continuous Improvement Process plan. This critical information captured reduces risks of production disruptions and helps product quality to remain consistent. It also allows for improvements to be made more quickly and efficiently.
Digitalization is a strategic imperative for pharmaceutical processors. It is a key enabler of operational excellence, innovation and competitiveness in the global market. When people have the information and guidance they need to do their jobs, manufacturers experience fewer product quality issues, less downtime and enhanced operational performance.
