• Written by Azhar Aliyar, Director

A production planner at a pharmaceutical manufacturing site must decide this week whether to rerun a batch of medication that failed quality control, and currently bases this decision on a manual review of the batch record and associated deviation investigation reports. The planner must consider the root cause of the failure, the corrective actions taken, and the potential impact on future batches. However, this process can be time-consuming and prone to errors, highlighting the need for more efficient and reliable methods for evaluating batch data.

Pharmaceuticals' unique challenges

The pharmaceutical industry poses unique challenges for implementing AI solutions, particularly in terms of data foundations. The need for strict documentation and validated processes introduces significant overhead in terms of change control, batch record review, and deviation investigation time. For instance, a single batch record can contain hundreds of pages of documentation, making it difficult to quickly identify the root cause of a failure. Furthermore, the time spent on deviation investigations can be substantial, taking up to several weeks or even months to complete.

This overhead can make it difficult to implement AI solutions that rely on real-time data analysis, as the data may not be available in a timely manner. Additionally, the need for strict documentation and validation can limit the ability to make changes to the data foundation, making it harder to adapt to changing business needs. The AsscherAi platform can help alleviate some of these challenges by providing a centralized repository for batch data and associated documentation, making it easier to access and analyze the data.

Field-level meaning and documentation

One of the key challenges in establishing a strong data foundation is ensuring that the data is properly documented and understood at the field level. Column names are not sufficient documentation, as they may not accurately reflect the meaning or context of the data. For example, a column named "temperature" may not indicate whether the temperature is being measured in Celsius or Fahrenheit, or whether it is being recorded at a specific point in the manufacturing process.

To establish a strong data foundation, it is necessary to have a clear understanding of the meaning and context of each field, as well as the relationships between different fields. This requires careful documentation and validation of the data, as well as ongoing monitoring and maintenance to ensure that the data remains accurate and consistent over time. Failure to do so can lead to incorrect or misleading conclusions being drawn from the data, which can have serious consequences in the pharmaceutical industry.

Systems that get left out of scope

In many cases, certain systems or data sources may be left out of scope when implementing AI solutions, either due to technical limitations or resource constraints. For example, a company may have a legacy system that is not compatible with the AI platform, or a data source that is not easily accessible. However, leaving these systems out of scope can result in partial answers or incomplete analysis, which can be misleading or inaccurate.

For instance, a shift supervisor may use a manual log to track equipment maintenance, but this data may not be integrated into the AI platform. As a result, the platform may not have a complete picture of the equipment's maintenance history, which can affect its ability to predict future maintenance needs or identify potential issues. To get the most out of AI solutions, it is essential to consider all relevant systems and data sources, and to work to integrate them into the platform.

Gaps in the data

Gaps in the data, such as stockouts or backfills, can also affect the accuracy and reliability of AI solutions. These gaps can occur due to a variety of reasons, such as equipment failures, supply chain disruptions, or human error. If not properly addressed, these gaps can teach the model the wrong lesson, leading to incorrect or misleading conclusions being drawn from the data.

For example, if a batch of medication is missing data on the temperature readings during the manufacturing process, the model may not be able to accurately predict the quality of the batch. To mitigate this risk, it is essential to have processes in place to detect and address gaps in the data, such as implementing data validation rules or using data imputation techniques. By doing so, companies can ensure that their AI solutions are based on accurate and reliable data, and that they are getting the most out of their investment in these technologies.

Limitations and wrong choices

While AI solutions can be a powerful tool for pharmaceutical companies, they are not a panacea for all data-related challenges. There are certain situations where AI may not be the best choice, such as when the data is highly unstructured or when the business problem is relatively simple. In these cases, other solutions, such as traditional data analysis or business intelligence tools, may be more effective and efficient.

Additionally, AI solutions may not be suitable for companies that do not have a strong data foundation in place. If the data is inaccurate, incomplete, or inconsistent, AI solutions may not be able to provide reliable or accurate results. In these cases, it may be necessary to focus on establishing a strong data foundation before implementing AI solutions. Companies that are considering implementing AI solutions should contact us to discuss their specific needs and challenges, and to determine whether AI is the right choice for their business.