Nearly two years to the day that Jabil’s predictive analytics solution was featured by Microsoft at Hannover Messe in 2016, Jabil will once again be in the spotlight for taking its application of artificial intelligence (AI) and predictive analytics to new heights on the manufacturing floor.
Part of Jabil’s goal in the use of analytics is to extract real-time information from the assembly line to provide prescriptive and preemptive information back to the business to enable Jabil to manufacture with more speed, to proactively anticipate issues and to react in advance to potential disruptions.
In pursuit of that goal, Jabil has collaborated with Microsoft to build a predictive model using Azure Machine Learning Workbench.
Jabil is using this predictive model to create efficiencies in its manufacturing and quality control processes. The most notable and promising application of the technology involves automated optical inspection (AOI).
When Jabil manufactures certain components, it uses a traditional computer-based system to perform AOI of those components to ensure quality.
Those AOI systems require a skilled engineer to build and hard-code algorithms to help the system identify a good component versus a defective one. These systems, however, do not have the ability to learn or adapt, thereby resulting in a high percentage of false positives – situations where a component is flagged as defective but is not.
When a component is flagged as defective, an operator is required to manually inspect it to confirm the diagnosis. In a situation where one station produces 2,000 components daily and the AOI has a false positive rate of 30%, it can result in operators spending up to 200 minutes daily doing manual inspections.
Recent competitions, however, have shown that deep neural networks are more accurate than humans at image classification tasks and can process them exponentially faster (40 images per second).
Enter Project Brainwave.
Project Brainwave is a system that uses field programmable gate array (FGPA) to efficiently do calculations rapidly (with little lag time) and economically, with the potential to improve the image processing rate to 550 images per second.
By using FGPAs, Jabil hopes to refine its AOI process, using the data the system collects to hone its ability to spot defects and ensure that only truly defective components require human operator inspection. Leveraging AI would allow operators to focus on more value-added tasks that machines cannot complete, and the feedback from operators would also be fed back into the system to allow it to learn and further perfect its accuracy.
Another attractive aspect of the solution is that it is easily scalable; one AI engine can collect data from multiple operator stations, so there is no need to have one engine per station. The investment is also relatively small, its main components being edge computing hardware and data storage to allow the system to train and retrain itself.
In Jabil’s early tests, this solution accurately predicts non-defective components with 75% accuracy and correctly identified 92% of defective components. Ryan Litvak, the IT leader driving the project, is impressed with early results and long-term potential.
“This solution represents a great opportunity to create efficiencies in how fast and how well we deliver for our customers,” said Litvak. “The AI compute capability will make a significant difference in our process from both a speed, cost and productivity standpoint, and this will, in turn, give our customers an edge in the marketplace.”