Multinational automaker Nissan manufactures vehicles in 20 countries worldwide, with production volume exceeding 5.6 million vehicles. And while its production assets were generating an abundance of operational and production data, the company lacked sufficient skilled resources to perform analysis on all of that data adequately. However, through an engagement with its vendor, Senseye, and using artificial intelligence (AI), Nissan was able to analyze that data and generate predictions on when its production machines would need maintenance. Before, the automaker used static metrics (such as the number of process cycles or the number of hours in service) to determine when a machine needs to be taken out of service for maintenance. Nissan used machine learning (ML) algorithms to monitor and spot patterns in the operational data of more than 9,000 connected assets and more than 30 different machine types, including robots, conveyors, drop lifters, pumps, motors, and press/stamping machines.