Monitor downtime and machine performance
The data collected through machines and operators is useful to develop a KPIs (Key Performance Indicators) set, such as the effectiveness of overall equipment. It stimulates a scrape analysis of root cause along with downtime because of data. Remember, data science becomes useful for a responsive and proactive approach to optimization and machine maintenance.
Undoubtedly, quick response to different issues can directly impact expensive downtime and productivity. A predictive model may prove helpful to monitor downtime and machine performance. It can anticipate the yield gains, external changes, and their impacts, quality, and scrap reduction. Manufacturers find it useful to discover new approaches and methods for cost management and quality improvement.
Benefits for the company
Predictive maintenance often allows the detection of impending failures that could never be detected by human eyes. With predictive maintenance, downtime and repairs are directly tied to likely failure, minimizing cost (e.g. less labor time, less chance of unexpected failure) and maximizing asset life.
Type of expertise/ AI domain
Regression analysis, Ranked scoring, real-time analysis and prediction
Internal data required
Real-time sensor data