“First pass yield” (FPY), is defined as the % of units that pass the first time the test of a particular process divided by the total number of units tested during the period. In other words, it measures the quality and efficiency of a manufacturing process. 

 

WATCH DEMO

The higher the FPY, the more consistent and reliable the process. In order to improve production yields and efficiency, it is important to gain full visibility into all stages of the manufacturing process.

 

Using AI analytics tools to determine the proportion of finished units that pass inspection during product testing is the most effective way to accomplish this. Let’s look at the reasons why.

 

Data Collection – Gathering the Right Information

 

QualityLine’s AI manufacturing Analytics and automated data integration maximizes manufacturing efficiency and product quality as it continuously collects the entire manufacturing data into a unified digital twin database. Data sources from multiple global locations are then harmonized and analyzed in real time.

 

By ensuring that you incorporate all of your manufacturing data into the formula, you will be able to achieve a correlation between the data that will provide the most accurate insights for identifying and resolving issues.

Monitor KPIs in real time to improve the detection of faulty units.

 

The first pass yield can be calculated manually, but it is best tracked with real-time production data collected by AI manufacturing analytics. In order to achieve a high and consistent FPY, manufacturers should monitor their output in real time.

 

QualityLine AI analytics delivers ongoing monitoring & optimization of yield and is powered by automatic alerts in case of issues, prediction reports and more. 

QualityLine continuously monitors the yield of a factory by ensuring that all data sources from any factory are combined, integrated and analysed. Based on the statistics of QualityLine customers, First Pass Yield was improved by 30% within the first year after using the AI analytics. 

 

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