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 Historical data Operating history Maintenance reports Technician notes

One Response

  1. One of the most fundamental data science use cases is prediction. Predictive analytics has taken under its control the analysis of vast amounts of data providing the capability to forecast. The ability to track real-time data and change it into meaningful insights for prediction has become a game-changing solution for the construction industry. Multiple scenarios based on the insights are then applied to make estimations and avoid failures in the future.

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