  Errors or failures in a high-volume manufacturing environment can have
significant impact that can result in both the loss of time and money.
Identifying such failures early has been a top priority for manufacturing
industries and various rule-based algorithms have been developed over the
years. However, catching these failures is time consuming and such algorithms
cannot adapt well to changes in designs, and sometimes variations in everyday
behavior. More importantly, the number of units to monitor in a high-volume
manufacturing environment is too big for manual monitoring or for a simple
program. Here we develop a novel program that combines both rule-based
decisions and machine learning models that can not only learn and adapt to such
day-to-day variations or long-term design changes, but also can be applied at
scale to the high number of manufacturing units in use today. Using the current
state-of-the-art technologies, we then deploy this program at-scale to handle
the needs of ever-increasing demand from the manufacturing environment.
