• Written by Sonali Subhadarsini, Product Manager

A typical Monday morning for a shift supervisor in a pharmaceutical manufacturing plant begins with a routine check of the maintenance log to identify any issues that may have arisen over the weekend, and a review of the production schedule to ensure that all necessary materials and equipment are available for the day's operations. However, when it comes to answering questions about the current state of operations, such as the status of ongoing projects or the performance of suppliers, the shift supervisor often has to wait for a report to be generated by the operations team, which can take several hours or even days. This delay can have a significant impact on the ability of the operations team to respond quickly to changes or issues that may arise during the manufacturing process.

Operations questions that currently wait on a report

The operations team in a pharmaceutical manufacturing plant is responsible for ensuring that all aspects of the manufacturing process are running smoothly and efficiently. This includes managing projects, tracking budget consumption, and monitoring the performance of suppliers. However, when it comes to answering questions about the current state of operations, the team often has to rely on reports that are generated on a periodic basis, such as daily, weekly, or monthly. These reports can provide valuable insights into the operations of the plant, but they can also be limited in their ability to provide real-time information. For example, a category manager may need to know the current status of a project in order to make informed decisions about resource allocation, but if the report is only generated on a weekly basis, they may have to wait several days for the latest information.

A shift supervisor may also need to know the details of any exceptions that have occurred during the manufacturing process, such as equipment failures or raw material shortages, in order to take corrective action and prevent future occurrences. However, if the report is not generated in real-time, the shift supervisor may not have access to this information until it is too late to take action. By using AI for operations in pharmaceuticals, the operations team can ask questions of their own data and get answers in real-time, without having to wait for a report to be generated.

The data behind them: process and exception records, project status, budget consumption, supplier performance

The data that is used to answer operations questions in a pharmaceutical manufacturing plant comes from a variety of sources, including process and exception records, project status updates, budget consumption reports, and supplier performance metrics. This data is typically stored in a variety of systems, such as enterprise resource planning (ERP) systems, manufacturing execution systems (MES), and quality management systems (QMS). By using AI for operations in pharmaceuticals, the operations team can bring all of this data together and ask questions of it in real-time, without having to manually extract and analyze the data from each individual system.

For example, a category manager can use AI to compare the performance of different suppliers and identify areas where improvements can be made. This can be done by analyzing data on supplier performance metrics, such as on-time delivery rates and quality ratings, and using this information to inform procurement decisions. By using AI to analyze this data, the category manager can make more informed decisions and drive improvements in the supply chain.

Sector constraints: change control overhead and batch record review

The pharmaceutical industry is heavily regulated, and as such, there are a number of sector constraints that can impact the use of AI for operations. One of the main constraints is the overhead associated with change control, which requires that all changes to processes or systems be thoroughly documented and approved before they can be implemented. This can make it difficult to implement new technologies, such as AI, without incurring significant costs and delays. Additionally, the review of batch records is a critical step in the pharmaceutical manufacturing process, and any errors or discrepancies in these records can have serious consequences. By using AI to analyze batch records and identify potential issues, the operations team can reduce the risk of errors and improve the overall quality of the manufacturing process.

Another constraint is the need to ensure that all systems and processes are validated and compliant with regulatory requirements. This can be a time-consuming and costly process, but it is essential to ensure that the use of AI for operations does not compromise the integrity of the manufacturing process. By working with experienced operations teams, pharmaceutical manufacturers can ensure that their use of AI is compliant with all relevant regulations and standards.

What improvement looks like: on-time delivery, exception rate, expediting cost

When it comes to measuring the improvement that can be achieved through the use of AI for operations in pharmaceuticals, there are a number of key metrics that can be used. One of the most important metrics is on-time delivery, which measures the percentage of products that are delivered to customers on or before the scheduled delivery date. By using AI to analyze data on the manufacturing process and identify potential bottlenecks or issues, the operations team can take corrective action to improve on-time delivery rates. Another key metric is the exception rate, which measures the number of exceptions that occur during the manufacturing process, such as equipment failures or raw material shortages. By using AI to analyze data on exceptions and identify root causes, the operations team can take action to reduce the exception rate and improve overall efficiency.

Finally, the expediting cost is another key metric that can be used to measure the improvement that can be achieved through the use of AI for operations. This metric measures the cost of expediting shipments or production to meet customer demand, and can be a significant drain on resources if not managed effectively. By using AI to analyze data on the manufacturing process and identify potential issues, the operations team can take corrective action to reduce the expediting cost and improve overall profitability. For more information on how AI can be used to improve operations in pharmaceuticals, please contact us.

What this does not do, or when it is the wrong choice

While AI for operations in pharmaceuticals can be a powerful tool for improving efficiency and reducing costs, it is not a solution that is suitable for all situations. For example, AI is not a replacement for human judgment and expertise, and should not be relied upon to make critical decisions without proper oversight and review. Additionally, AI is not a solution that can be implemented overnight, and requires significant planning, testing, and validation before it can be used in a production environment. In some cases, the use of AI may not be the best solution, such as when the data is incomplete or inaccurate, or when the processes are highly variable or complex. In these situations, other solutions, such as process improvement initiatives or training programs, may be more effective.

Most people get wrong the idea that AI can be used to replace human workers, or that it can be used to make decisions without proper oversight and review. This is wrong, as AI is a tool that is designed to support and augment human decision-making, not replace it. By understanding the limitations and potential pitfalls of AI, pharmaceutical manufacturers can ensure that they are using this technology in a way that is safe, effective, and compliant with all relevant regulations and standards.