• Written by Azhar Aliyar, Director

A production manager checks the quality metrics for the latest batch of products, only to find that the data is inconsistent and unclear, making it difficult to identify the root cause of the issues. This is a common problem in many organisations, where the data foundations for quality are not well established, leading to partial answers and ineffective corrective actions.

Field-level meaning and why column names are not documentation

The first step in establishing a solid data foundation for quality is to ensure that the meaning of each field is clearly defined and understood by all stakeholders. This is not just a matter of having descriptive column names, but also of documenting the underlying business rules and definitions that govern the data. Column names are not documentation, as they can be ambiguous and open to interpretation. For example, a column named "defect_code" may seem straightforward, but what does it actually represent - a specific type of defect, a severity level, or something else entirely?

A clear understanding of field-level meaning is essential for ensuring that the data is accurate, consistent, and reliable. Without it, organisations may struggle to identify and address quality issues effectively, leading to wasted time and resources. By taking the time to document and standardise the meaning of each field, organisations can establish a solid foundation for their quality data and improve their overall ability to analyse and act on it.

The systems that get left out of scope and the partial answers that follow

When implementing a quality data system, it is easy to focus on the most obvious sources of data, such as inspection records and non-conformance reports. However, there are often other systems that are left out of scope, which can lead to partial answers and a lack of visibility into the overall quality process. For example, the production scheduling system may contain valuable information about batch sizes, production runs, and supplier data, but if this system is not integrated with the quality data system, then this information will not be available for analysis.

This can lead to a situation where organisations are only seeing part of the picture, and are unable to identify the root cause of quality issues or take effective corrective action. By including all relevant systems in the scope of the quality data system, organisations can gain a more complete understanding of their quality process and make more informed decisions.

The quality data this depends on: inspection records, non-conformance reports, batch genealogy, customer complaints

Establishing a solid data foundation for quality requires a range of different data sources, including inspection records, non-conformance reports, batch genealogy, and customer complaints. Inspection records provide detailed information about the quality of individual products or batches, while non-conformance reports document any defects or issues that are found. Batch genealogy provides a record of the production history of each batch, including information about suppliers, materials, and production processes. Customer complaints provide valuable feedback about the quality of products in the field.

By combining these different data sources, organisations can gain a comprehensive understanding of their quality process and identify areas for improvement. For example, by analysing inspection records and non-conformance reports, organisations can identify trends and patterns in defect rates and take targeted corrective action. By analysing batch genealogy and customer complaints, organisations can identify potential root causes of quality issues and take proactive steps to prevent them.

What it changes about: defects traced late to a batch or supplier and CAPA records that are written but never read

By establishing a solid data foundation for quality, organisations can make significant changes to their quality process. For example, defects can be traced back to a specific batch or supplier, allowing for targeted corrective action to be taken. This can help to prevent future defects and improve overall quality. Additionally, CAPA (Corrective and Preventive Action) records can be made more effective by ensuring that they are properly documented and acted upon.

Too often, CAPA records are written but never read, which can lead to a lack of follow-through and a failure to address underlying quality issues. By making CAPA records more visible and actionable, organisations can ensure that corrective actions are actually taken and that quality issues are properly addressed. This can help to improve overall quality and reduce the risk of future defects.

Measured with: first pass yield, defects per unit, time to root cause

The effectiveness of a quality data system can be measured in a variety of ways, including first pass yield, defects per unit, and time to root cause. First pass yield measures the percentage of products that are produced correctly on the first attempt, while defects per unit measures the number of defects found per unit of product. Time to root cause measures the time it takes to identify the underlying cause of a quality issue.

By tracking these metrics, organisations can gain a clear understanding of their quality process and identify areas for improvement. For example, a low first pass yield may indicate a problem with the production process, while a high defects per unit rate may indicate a problem with inspection or testing. By using these metrics to guide their quality efforts, organisations can make targeted improvements and achieve better overall quality.

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

While a quality data system can be a powerful tool for improving quality, it is not a solution to every problem. In some cases, the costs and complexity of implementing such a system may outweigh the benefits, particularly for small or simple organisations. In these cases, a more manual or straightforward approach to quality management may be more effective.

Additionally, a quality data system is not a substitute for human judgment and expertise. While the system can provide valuable insights and data, it is still up to quality professionals to interpret the results and make informed decisions. For more information on how to get started with a quality data system, or to discuss your specific needs and challenges, please contact us.