Alcoa Improves Operational Efficiency And Asset Reliability With Predictive Maintenance
21 Jul, 2020
Executive Summary
With the increasing adoption of asset performance management software in industrial markets during the last five years, recognition of the business potential of predictive maintenance is rising. Alcoa, a global aluminium producer and owner-operator of bauxite mines, has implemented predictive maintenance software to restructure its maintenance programme and practices. The software leverages artificial intelligence and machine learning to anticipate asset failure and send alerts to relevant personnel before failures occur. These advancements have precipitated a 30% increase in Alcoa’s operational efficiency and reduced maintenance costs by 20%.
Asset Failure Prediction Software Helps Alcoa Modernize Its Maintenance Approach
Alcoa Moves From Planned To Predictive Maintenance By Combining AI And Machine Learning
Alcoa Enhances Maintenance Practices And Improves Operational Efficiency By 30% Through Adoption Of Predictive Maintenance Solutions
Alcoa Moves From Planned To Predictive Maintenance By Combining AI And Machine Learning
Alcoa Enhances Maintenance Practices And Improves Operational Efficiency By 30% Through Adoption Of Predictive Maintenance Solutions
About the Authors
Hugo Fuller
Senior Analyst
Hugo is a Senior Analyst in the Verdantix Industrial Transformation practice. His current research agenda explores the technologies within the industrial asset management soft…
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Malavika Tohani
Research Director
Malavika is a Research Director at Verdantix, guiding research that explores how digital technologies and services are reshaping industrial operations to become safer, more ef…
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