Alcoa is an American industrial firm and leading producer of aluminum. The business increased its focus on digital transformation in 2017 and 2018 to improve the tools and processes used in machine asset management.
- Alcoa’s plants are running around the clock, so stability is crucial to efficiency, productivity, and the bottom line
- Improving asset uptime is also essential for meeting customer expectations and ensuring downtime doesn’t affect deliveries
- Finally, Alcoa operates out of large, complex plants with interconnected machinery. Asset failures have the potential to impact large parts of its operations
Alcoa sought to improve this area of operations through condition monitoring and improved operational management. It has a long history of traditional preventative and predictive maintenance, and data collection was integral to operations for a long time.
The firm had struggled to tap the data for insights as manually analyzing the data from thousands of assets was never viable. Alcoa turned to Senseye to begin utilizing the data it was collecting at scale.
Alcoa’s main aim was to drive down costs by improving productivity in maintenance and eliminating non-value-added tasks on select assets. It achieved a 20% reduction in downtime during its 2019 proof of concept and a 10% reduction in the time spent on maintenance.
Automated analysis of condition monitoring data has allowed Alcoa to interrogate increasingly large and varied data points for insights into asset health. By understanding any machines' problems early, the business can resolve them quicker, in optimum operational windows. Doing so at scale helps Alcoa make continuous improvements to the processes plant-wide.
By bringing Senseye into its maintenance plan, Alcoa gained a greater understanding of the existing infrastructure to inform its work at other sites and make future implementation even more straightforward.
Senseye: Connecting assets at scale
With smart PdM reaching mainstream adoption, many metals and mining organizations are introducing these advanced technologies at scale. Senseye’s analysis shows that more than half (58%) of firms that introduced pilot PdM projects have already deployed this capability to other business areas.
The critical factor in successful scalability is ensuring that any solution is generic enough to be scaled across different types of machines and smart enough to then learn each monitored asset's unique characteristics. Automating the analytical tasks that engineers perform daily allows organizations to monitor many more assets and focus on the proactive maintenance of equipment.
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