Root Cause Analysis enables to identify the sources of productivity degradation, but only after the impacts have already occurred. Industrial Internet of Things helps to identify those root issues as they happen and address them right away, long before their negative affects propagate across the industrial environment. Predictive Analytics is a way of identifying potential impediments to optimal production capacity and product quality before they erupt and fester, and adapting prescriptive techniques to trigger actions to foster continuous machine uptime. This Early-Warning System relies on continuously streaming device data to a centralized server.
This server runs applications using analytical models to identify collective machine and device behavior patterns common to known productivity or quality issues, as well as ways that environmental characteristics affect the efficiency of machine components. Issues includes like unexpected stoppages, increased time to change over a production line, unplanned downtime, part failure, or product quality variations. Predictive Analytics supports two different facets of the process: discovery of risk patterns, followed by instituting controls to detect imminent issues. Once these analytics applications have access to time-series data from a multitude of intelligent machines, they evaluate historical event streams occurring prior to undesired circumstances to identify sentinel events and sequences that are indicative of potential issues. These patterns are then integrated with real-time stream analysis to monitor the data communicated across the intelligent factory. When sentinel event or sequence is seen, an alert can be generated to notify an individual to take some preventative measures, thereby allowing quicker problem identification and resolution.