• Written by Sonali Subhadarsini, Product Manager

When a shift supervisor in a pharmaceutical manufacturing facility notices a recurring issue with a piece of equipment, they typically have to wait for a report to be generated before they can start investigating the cause, which can lead to delays in resolving the issue and impact production schedules, as they often have to coordinate with the production team to ensure that any maintenance work does not disrupt the production workflow, which can be managed more efficiently using tools like those described in our production planning article.

The maintenance questions that currently wait on a report

The maintenance team in a pharmaceutical facility often has to rely on reports to answer basic questions about equipment performance, such as how often a particular machine has broken down, or what the most common causes of failure are, and while these reports can provide valuable insights, they can also be time-consuming to generate and may not always be up-to-date, which can make it difficult for the maintenance team to make informed decisions about where to focus their efforts.

For example, a maintenance manager might want to know which pieces of equipment are most likely to require maintenance in the next quarter, or which spare parts are most frequently used, but without access to real-time data, they may have to rely on historical trends or intuition, rather than hard data, to make these decisions, and this can lead to inefficiencies and wasted resources.

The data behind them: work orders, asset history, spare parts inventory, condition data where it exists

The data that underlies these questions is typically stored in a variety of systems, including work order management systems, asset management systems, and inventory management systems, and while these systems can provide valuable insights, they are often not integrated, making it difficult to get a comprehensive view of equipment performance and maintenance needs, and even when they are integrated, the data may not be easily accessible to the maintenance team, or may require specialized training to interpret.

Condition data, such as vibration analysis or temperature monitoring, can be particularly valuable in predicting equipment failures, but this data is not always available, and even when it is, it may require specialized expertise to interpret, and the maintenance team may not have the resources or training to take full advantage of this data, which can limit its usefulness in preventing equipment failures.

Sector constraints: change control overhead and batch record review

The pharmaceutical industry is heavily regulated, and as such, maintenance activities must be carefully documented and controlled to ensure compliance with regulatory requirements, which can add complexity and overhead to the maintenance process, and the maintenance team must carefully balance the need to keep equipment running with the need to follow strict documentation and change control procedures, which can slow down the maintenance process and make it more difficult to implement changes.

Batch record review, in particular, can be a time-consuming and labor-intensive process, as it requires careful review and verification of all maintenance activities to ensure that they are properly documented and compliant with regulatory requirements, and this can divert resources away from other important maintenance activities, such as preventive maintenance and equipment inspection, which can help to prevent equipment failures and reduce downtime.

What improvement looks like: planned versus reactive ratio, mean time between failures, spares availability

Improvement in maintenance can be measured in a variety of ways, including the planned versus reactive ratio, which measures the proportion of maintenance activities that are planned and scheduled, rather than reactive and unplanned, and the mean time between failures, which measures the average time between equipment failures, and spares availability, which measures the availability of spare parts and materials, and by tracking these metrics, the maintenance team can identify areas for improvement and make data-driven decisions about where to focus their efforts.

For example, if the planned versus reactive ratio is low, it may indicate that the maintenance team is spending too much time responding to emergencies, rather than performing preventive maintenance, and this can help to identify opportunities to improve the maintenance process and reduce downtime, and by using tools like AI for maintenance, the maintenance team can gain insights into equipment performance and maintenance needs, and make more informed decisions about where to focus their efforts, and if you would like to learn more about how AsscherAi can help your organisation, please contact us.

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

While AI for maintenance can be a powerful tool for improving equipment performance and reducing downtime, it is not a replacement for human judgment and expertise, and it should be used in conjunction with, rather than instead of, the knowledge and experience of the maintenance team, and it is also important to note that AI for maintenance is not a solution for underlying problems with equipment design or maintenance processes, and it should not be relied upon as a quick fix for deeper issues, but rather as a tool to support and enhance the maintenance process.

In some cases, AI for maintenance may not be the best choice, such as when the maintenance team is not familiar with the technology, or when the data is not accurate or complete, and in these cases, it may be better to focus on developing the skills and knowledge of the maintenance team, or on improving the quality and accuracy of the data, rather than relying on AI for maintenance, and by carefully considering these factors, organisations can make informed decisions about when and how to use AI for maintenance, and can get the most out of this powerful tool.