• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, the shift supervisor in a pharmaceutical production team is likely to be waiting for a report on the previous week's output, which will inform their decisions on production planning, maintenance scheduling, and quality control. This report is often delayed, and when it finally arrives, it may not provide the level of detail needed to address specific issues or opportunities for improvement.

The production questions that currently wait on a report

The production team has a range of questions that they need answers to in order to do their job effectively, such as what was the actual output against plan for the previous week, and what were the main reasons for any deviations from the plan. They may also want to know what the current cycle times are for each production line, and whether there have been any changes in scrap rates or quality records. Currently, these questions often have to wait for a report to be generated, which can take several days or even weeks, by which time the information may be out of date.

For example, the category manager may want to compare the performance of different suppliers, or the maintenance manager may want to review the maintenance history for a particular piece of equipment. These questions are critical to the smooth operation of the production team, but they are often frustrated by the lack of timely and detailed data.

The data behind them: output against plan, cycle times, scrap and quality records, maintenance history

The data that the production team needs to answer these questions is typically stored in a variety of systems, including production planning software, quality control databases, and maintenance management systems. This data includes output against plan, cycle times, scrap rates, quality records, and maintenance history. By bringing this data together and making it accessible in real time, the production team can get the answers they need quickly and easily, without having to wait for a report.

The data can be used to identify trends and patterns, such as changes in cycle times or scrap rates over time, and to drill down into specific details, such as the performance of individual production lines or the maintenance history of particular equipment. This level of detail and analysis can help the production team to identify opportunities for improvement and to make data-driven decisions.

Sector constraints: change control overhead and batch record review

The pharmaceutical sector is heavily regulated, and production teams must operate within strict documentation rules and change control procedures. This can make it difficult to implement new systems or processes, as any changes must be thoroughly documented and approved. Additionally, batch record review is a critical process that requires careful attention to detail and a thorough understanding of the production process.

These constraints can make it challenging to implement AI solutions, as they must be carefully designed and validated to ensure compliance with regulatory requirements. However, by working with a solution that is specifically designed for the pharmaceutical sector, such as AsscherAi, production teams can ensure that they are meeting all necessary regulatory requirements while still benefiting from the insights and analysis provided by AI.

What improvement looks like: OEE, unplanned downtime hours, scrap rate

Improvement in the pharmaceutical production team can be measured in a variety of ways, including overall equipment effectiveness (OEE), unplanned downtime hours, and scrap rate. By reducing unplanned downtime and scrap rate, and increasing OEE, the production team can improve efficiency, reduce costs, and increase productivity. AI can help to identify opportunities for improvement in these areas, and provide the data and analysis needed to make informed decisions.

For example, the production team may use AI to identify the root causes of unplanned downtime, and to develop strategies for reducing it. They may also use AI to analyze scrap rates and identify opportunities for improvement, such as changes to production processes or equipment maintenance schedules. By using AI in this way, the production team can drive continuous improvement and optimize their operations.

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

While AI can be a powerful tool for pharmaceutical production teams, it is not a solution that can be applied to every problem or challenge. For example, AI is not a replacement for human judgment and expertise, and it should not be used to make decisions that require a deep understanding of the production process or regulatory requirements. Additionally, AI may not be the best choice for situations where the data is incomplete or inaccurate, or where the production process is highly variable or unpredictable.

In these situations, it may be better to use other approaches, such as lean manufacturing or six sigma, which can provide a more structured and systematic approach to improvement. Alternatively, production teams may want to contact us to discuss their specific needs and challenges, and to determine whether AI is the right solution for their particular situation.