Historical and current data to forecast the condition of a piece of equipment. Essentially, manufacturing parts can monitor themselves and alert a human when a part may be faulty or break down. Smart factories are using predictive maintenance because machines can detect errors that humans cannot see.
Big data allows manufacturers to
Move away from preventative maintenance, which country email list solves the problem after a malfunction occurs, to predictive maintenance. The data captured enables the implementation of predictive analytics. This means less downtime and a safer workplace.
Example of predictive analytics: food manufacturing
Halts in the production line are frustrating for google maps for local searches became increasingly i any manufacturer. However, halts in a food production line can be dangerous. If a part breaks, pieces of the equipment could get into the food. Also, if the production is down for too long, food can spoil. There have been many recalls around the world because of spoiled food originating from manufacturing errors.
Predictive analytics keeps the manufacturing process safer
If a part seems likely to break, it will send zn business directory an alert to a human that can replace it. This means less downtime and fewer chances of food spoiling. Predictive analytics also helps the environment because it means less food waste and less energy use, as machines do not have to complete the same job twice. Big data and predictive analytics are just a part of smart factories. Another innovative technology moving this revolution along is the Internet of Things (IoT).
