Manufacturing is a data-intensive process that utilizes analytics to evaluate equipment health while optimizing costs and efficiencies. Balancing costs requires manufacturers to keep equipment downtime to a minimum while maintaining productivity.
Evaluating machinery health enables you to identify equipment preventative maintenance and other upkeep requirements while tracking the mean time between failures and the mean time to repair. Using data to track equipment health will also show the optimal times and performance thresholds that lead to preventative maintenance requirements. Oracle Analytics Cloud allows you to utilize no-code machine learning that can track historic data and make predictions about the conditions that necessitate preventative maintenance downtime.
Much like manufacturing process equipment, athletes also have planned downtime. In sports, they are referred to as maintenance days. These maintenance days are built into athlete training programs to protect against overuse injuries.
In either manufacturing or in sports, unexpected downtime increases expenses and can damage results and reputation. In sports, the cost of an athlete becoming unavailable due to injury is often seen on the scoreboard. Of course the unforeseeable happens occasionally, but using analytics to prepare for preventative maintenance will allow you to meet your customer needs while protecting your assets.
Watch the attached video to learn more about integrating AutoML into your Oracle Analytics instance. The quickly established machine learning models will generate outputs that enable you to make data-supported decisions which can save costs and improve efficiencies.