• Written by Sonali Subhadarsini, Product Manager

When a shift supervisor checks the maintenance log and finds that routine tasks are being completed more quickly, it seems like a clear win for productivity, but the question remains as to whether this time saved is actually being redeployed in a way that improves quality, and if so, where the evidence of this can be found, particularly in areas such as production where the impact of quality control is most directly felt.

The difference between time saved and time redeployed

The introduction of AI productivity tools in the enterprise is often touted as a way to save time, but the reality is that simply saving time does not necessarily translate to improved quality, as the time saved may not be redeployed in a way that directly impacts quality control, and may instead be absorbed by other tasks or responsibilities, which can make it difficult to measure the actual impact on quality.

For example, a category manager may find that they have more time to compare suppliers, but if this time is not being used to implement changes that directly impact quality, such as revising inspection protocols or updating non-conformance reports, then the actual benefit to quality may be minimal, and may not show up in measurable improvements to first pass yield or defects per unit.

Why self-reported time savings overstate the effect

One of the main problems with relying on self-reported time savings is that they often overstate the actual impact, as individuals may report time savings based on their own perceptions, rather than on actual data, and may not take into account other factors that could be influencing their productivity, such as changes in workload or responsibilities, which can make it difficult to get an accurate picture of the actual time saved.

Additionally, self-reported time savings may not account for the fact that time saved in one area may be offset by increased time spent in another area, such as if a quality control inspector finds that they have more time to review inspection records, but this time is offset by increased time spent on administrative tasks, which can make it difficult to determine the actual net impact on productivity.

The quality data this depends on

The ability to measure the impact of AI productivity tools on quality depends on having access to high-quality data, including inspection records, non-conformance reports, batch genealogy, and customer complaints, which can provide a detailed picture of where quality control efforts are being effective, and where they may be falling short, and can help to identify areas where improvements can be made.

For example, a quality control manager may use batch genealogy data to track the origin of defective products and identify trends or patterns that can inform quality control efforts, or may use customer complaint data to identify areas where quality control efforts may be falling short, and make adjustments accordingly, which can help to improve the overall quality of products and reduce the risk of defects.

What it changes about defects traced late to a batch or supplier and CAPA records

One of the key areas where AI productivity tools can have a direct impact on quality is in the area of defect tracking and corrective action, as these tools can help to identify defects more quickly and accurately, and can facilitate the implementation of corrective actions, such as revisions to inspection protocols or updates to supplier quality control procedures, which can help to prevent future defects and improve overall quality.

For example, a quality control inspector may use AI-powered tools to review inspection records and identify trends or patterns that can inform corrective actions, or may use these tools to facilitate the review and implementation of CAPA records, which can help to ensure that corrective actions are being taken in a timely and effective manner, and can help to improve the overall quality of products and reduce the risk of defects, and can also help to reduce the time to root cause, which is a key metric for measuring the effectiveness of quality control efforts.

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

The impact of AI productivity tools on quality can be measured using a variety of metrics, including first pass yield, defects per unit, and time to root cause, which can provide a detailed picture of where quality control efforts are being effective, and where they may be falling short, and can help to identify areas where improvements can be made, such as revising inspection protocols or updating supplier quality control procedures.

For example, a quality control manager may use first pass yield data to track the percentage of products that are passing inspection on the first try, and may use defects per unit data to track the number of defects per unit of product, which can help to identify areas where quality control efforts may be falling short, and can inform corrective actions, such as revisions to inspection protocols or updates to supplier quality control procedures, and can also help to reduce the time to root cause, which is a key metric for measuring the effectiveness of quality control efforts, and can be a key factor in determining the overall quality of products.

What this does not do

While AI productivity tools can be a powerful tool for improving quality, they are not a silver bullet, and there are certain situations where they may not be the best choice, such as in situations where the quality control process is highly complex or nuanced, and requires a high degree of human judgment or expertise, or in situations where the data required to support quality control efforts is incomplete or inaccurate, in which case it may be more effective to contact us to discuss alternative solutions.