Votorantim Cimentos wanted to better understand its asset performance and implement a condition-based maintenance program by using machine learning to predict asset behavior. The leadership team at Votorantim selected AVEVA Predictive Analytics owing to the user-friendly interface that enabled leadership and operations teams to operate independently, ensuring that Votorantim had full control of its data at every stage of the optimization process.
“Using AVEVA Predictive Analytics we wanted to realize a vision of our next-generation plant operations, using data to shape our decision-making. Because AVEVA’s software is agnostic we were able to click in the systems within days, and see benefits within weeks, driving unparalleled optimizations that spanned our entire operations and our network of plants.”
Fabio Eduardo Scarlassari
Global Maintenance General Manager
Votorantim Cimentos
Goals
- Cut costs for corrective maintenance and maintenance (MRO) inventory
- Increase productivity through predictive maintenance
- Increase operational reliability and reduce downtime
- Achieve autonomous and automated scaling of solutions between plants
Challenges
- Need for rapid implementation and a user-friendly interface
- Shared data built trust across the team, allowing for future scalability without relying on a supplier
- Meaningful return on investment
- Adapting to COVID-19 scenario with 100% remote deployment
Solution
Results
- 10% reduction in recurring maintenance costs compared to previous years
- Savings of R16M per site, equivalent to US$5.5M savings per site and a total of US$88M across sites
- Asset reliability boosted by 6%, achieving target values of 85.9% to 91.2%
- Reduced unplanned maintenance from 52% to 42% for global maintenance costs
- Implemented successful change management strategy, improving collaboration
- Improved asset transparency, enabling comparative analysis between plants and sharing of best practices
- Improved instrumentation quality
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