What is AI analytics and how does it work?
AI analytics utilizes the capabilities of today’s artificial intelligence and machine learning technologies to automate the data integration and unification process.
An AI analytics process refers to the process of interpreting data by using software that exhibits behaviors usually attributed to humans, including learning and reasoning.
Using insights and patterns, AI can predict what drives outcomes. It can even improve the accuracy of its predictions over time.
Analyzing data for businesses is about gaining a deeper understanding of their data so they can react accordingly.
How does AI analytics benefit businesses in manufacturing?
Two main business benefits can be gained from AI analytics ability to analyze data autonomously. Among them are:
QualityLine uses AI technology in two stages of our solution:
1. Automated data mapping: During the integration with the data process.
a) The standard status of data in any factory.
b) As part of the integration process we ask each factory to provide us with samples of each data structure they have (automated testing processes data, manual testing data, repair data of defective units).
c) We run a scanning process on each data structure sample and automatically identify the location of the data fields that are relevant for the analytics. We actually map each data structure.
The scanning process is done automatically and it is based on AI pattern recognition technology that enables us to automatically map each structure.
So, the pattern recognition is a way for us to automatically do this location finding of each required data field.
d) Once mapping is done, we automatically create the data capture tool that memorizes the mopping of each data structure. This capture software tool will be installed in the local servers in the factory (or in the cloud – wherever the company keeps the data which is accumulated during the manufacturing process).
e) then this data capture tool continuously captures the required data from each data structure and reorganizes it all in a unified global database .
2. AI for the data analysis: 2nd usage of AI is during the analytics process where our algorithms automatically analyze the data which is continuously accumulated during the manufacturing process.
We then use AI technology for 2 main capabilities and features:
a) automated anomaly detection of quality and yield problems of each product and each process. The findings of this anomaly detection are shown in a methodological way in the dashboards to enable a quick and accurate root cause analysis.
b) We run correlation between each tested pattern of each product to find cross influence of each parameter with other tested parameters For example, we may find a strong correlation between a power test to a current test. This is a significant capability that enables the engineering and R&D teams of the company to improve the level for manufacturability of their products.