How to measure AI in quality
Evaluating AI in quality control requires tracking leading measures such as first pass yield and defects per unit, which provide early indications of the AI system's impact on quality, allowing for timely adjustments and improvements to the production process and overall quality control strategy.
When implementing AI in quality control, a common objection is that it is impossible to measure the effectiveness of AI metrics in quality, but this is a misguided concern, as the right measures can indeed be put in place to evaluate the impact of AI on quality. The business consequence of using the wrong metrics is that it can lead to incorrect decisions being made, which can have serious repercussions on the overall quality of the products or services being offered.
Leading measures: first pass yield, defects per unit
Leading measures such as first pass yield and defects per unit are essential in evaluating the effectiveness of AI in quality control. These measures are critical because they provide an early indication of whether the AI system is having a positive impact on quality. For instance, a shift supervisor checks the maintenance log to ensure that the AI system is correctly identifying defects, and a category manager compares two suppliers to determine which one is providing higher quality materials.
A good example of this is when a production line is using AI to inspect products for defects. The first pass yield measure can be used to track the number of products that are produced correctly on the first attempt, without the need for rework or repair. This measure can provide valuable insights into the effectiveness of the AI system in detecting defects and improving overall quality.
Why the headline metric moves for unrelated reasons
Most people get wrong the idea that the headline metric, such as overall quality rating, is a reliable indicator of the effectiveness of AI in quality control. However, this metric can move for unrelated reasons, such as changes in production volume or supplier quality, which can make it difficult to determine the true impact of the AI system. This is why it is essential to use leading measures, such as first pass yield and defects per unit, which are more directly related to the AI system's performance.
For example, a company may experience an increase in overall quality rating due to a change in supplier, but this does not necessarily mean that the AI system is performing well. In fact, the AI system may be missing defects or incorrectly identifying good products as defective, which could lead to serious quality control issues down the line.
Baselining before rollout
Baselining before rollout is critical in evaluating the effectiveness of AI in quality control. This involves collecting data on the current state of quality control before the AI system is implemented, which can then be used as a benchmark to compare the performance of the AI system. By doing so, companies can determine whether the AI system is truly having a positive impact on quality, or if other factors are at play.
For instance, a company can collect data on the number of defects per unit before and after the implementation of the AI system. By comparing this data, the company can determine whether the AI system is reducing the number of defects, and if so, by how much. This information can be used to refine the AI system and improve its performance over time. To learn more about how to implement AI in production, visit our production page.
What to record when nothing was found
When using AI in quality control, it is essential to record data even when nothing is found. This may seem counterintuitive, but it is crucial in evaluating the effectiveness of the AI system. By recording data on the number of products that were inspected and found to be defect-free, companies can determine the AI system's false negative rate, which is critical in ensuring that the system is not missing defects.
This data can also be used to refine the AI system and improve its performance over time. For example, if the AI system is consistently missing defects in certain types of products, this data can be used to retrain the system and improve its accuracy. By recording data even when nothing is found, companies can ensure that their AI system is operating effectively and efficiently.
What this does not do
This approach to measuring AI in quality does not provide a complete solution to all quality control issues. It is essential to remember that AI is just one tool in the quality control toolkit, and it should be used in conjunction with other methods, such as manual inspection and testing. Additionally, this approach does not address issues related to supplier quality or production volume, which can also impact overall quality.
For companies that are looking for a more comprehensive solution to their quality control issues, it may be necessary to consult with a quality control expert or implement a more robust quality control system. To learn more about how AsscherAi can help with your quality control needs, visit our contact us page. By taking a holistic approach to quality control, companies can ensure that their products meet the highest standards of quality and reliability.
Frequently asked questions
How do I measure the effectiveness of AI in quality control?
Track leading measures such as first pass yield and defects per unit to evaluate the AI system's impact on quality.
Why are leading measures important in AI quality control?
Leading measures provide early indications of the AI system's performance, allowing for timely adjustments and improvements.
What is the purpose of baselining before rollout in AI quality control?
Baselining provides a benchmark to compare the AI system's performance, determining its true impact on quality.
Why is it essential to record data even when nothing is found in AI quality control?
Recording data helps determine the AI system's false negative rate, ensuring it is not missing defects and refining its performance over time.