• Written by Sonali Subhadarsini, Product Manager

This week, a shift supervisor at a pharmaceutical manufacturing plant must decide whether to accept a batch of raw materials, and they currently base this decision on a manual review of the batch record, which can be a time-consuming process. The supervisor must carefully check the record for any deviations or discrepancies, and if any are found, they must initiate a deviation investigation, which can lead to significant delays in the production process.

Pharmaceuticals' unique challenges

The pharmaceutical industry is heavily regulated, and as such, any changes to processes or systems must be carefully documented and validated, which can create a significant change control overhead. This overhead can make it difficult to implement new technologies, such as AI process automation, as any changes must be thoroughly tested and validated to ensure compliance with regulatory requirements. Additionally, the batch record review process can be laborious, requiring manual checks and verifications, which can lead to delays in the production process.

Deviation investigation time is also a significant challenge in the pharmaceutical industry. When a deviation is detected, an investigation must be initiated to determine the root cause and implement corrective actions. This process can be time-consuming, and if not managed properly, can lead to significant delays in the production process. AI process automation can help streamline this process, but it must be carefully implemented to ensure compliance with regulatory requirements.

Mapping the current process

Before automating any part of the process, it is essential to map the current process in detail. This involves identifying all the steps involved in the process, from raw material receipt to finished goods dispatch, and documenting the responsibilities of each role, such as the shift supervisor, quality control specialist, and production manager. By mapping the current process, organizations can identify areas where automation can add the most value and ensure that any changes are carefully planned and validated.

A category manager, for example, may be responsible for sourcing raw materials and ensuring that they meet the required specifications. By mapping the current process, the category manager can identify areas where automation can help streamline the sourcing process, such as automated supplier comparisons or raw material tracking. This can help reduce the time and effort required to source raw materials and ensure that the production process runs smoothly.

Handoffs and delays

Handoffs between different roles and departments can create significant delays in the production process. For example, when a batch of raw materials is received, it must be inspected and tested by the quality control team before it can be released to production. If there are any issues with the raw materials, the quality control team must notify the shift supervisor, who must then initiate a deviation investigation. This handoff can create a delay, as the shift supervisor must wait for the quality control team to complete their testing and inspection before proceeding.

AI process automation can help streamline these handoffs by providing real-time updates and notifications to relevant roles and departments. For example, when a batch of raw materials is received, the system can automatically notify the quality control team and the shift supervisor, ensuring that everyone is aware of the status of the raw materials and can take action accordingly. This can help reduce delays and ensure that the production process runs smoothly.

Exception handling

Exception handling is a critical aspect of AI process automation in pharmaceuticals. Most of the real work in automating processes involves handling exceptions, such as deviations or discrepancies in the batch record. When an exception occurs, the system must be able to detect it and initiate the necessary corrective actions, such as notifying the shift supervisor or quality control team. This requires careful planning and validation to ensure that the system can handle exceptions correctly and ensure compliance with regulatory requirements.

For example, if a deviation is detected in the batch record, the system can automatically initiate a deviation investigation and notify the relevant roles and departments. The system can also provide real-time updates and notifications, ensuring that everyone is aware of the status of the deviation investigation and can take action accordingly. This can help reduce delays and ensure that the production process runs smoothly.

Limitations and wrong choices

AI process automation is not a solution for every problem, and there are certain situations where it may not be the best choice. For example, if an organization has not carefully mapped its current processes and identified areas for automation, AI process automation may not add significant value. Additionally, if an organization is not willing or able to invest the time and resources required to validate and implement AI process automation, it may not be the best choice.

Organizations should carefully consider their options and review their current processes before implementing AI process automation. They should also consider seeking advice from experts, such as consultants or system integrators, to ensure that they are making the best decision for their organization. By carefully considering their options and seeking advice from experts, organizations can ensure that they are using AI process automation to its full potential and achieving the desired benefits.