Case Study: Novelis achieves predictive maintenance and reduced downtime with SymphonyAI

Novelis transforms from preventive to AI predictive maintenance with SymphonyAI

Novelis, a global leader in aluminum manufacturing and recycling with 13,000 employees and 32 plants across nine countries, needed to move from preventive maintenance to predictive maintenance. With a mix of ERP systems, historians, and equipment ranging from modern assets to legacy machinery, Novelis struggled with scheduled downtime, unexpected failures, and rising pressure to improve uptime and meet customer demand. SymphonyAI helped address this challenge with its Predictive Asset Intelligence platform.

SymphonyAI implemented a predictive maintenance solution combining rule-based alerts with AI/ML insights, allowing Novelis to adopt the technology incrementally and build trust in the recommendations. As a result, Novelis reduced unplanned downtime across multiple facilities, improved collaboration across engineering and data teams, and gained better real-time visibility into asset health. With SymphonyAI, Novelis is advancing toward its “plant of the future” vision while improving responsiveness to customers and supporting a culture of innovation and continuous improvement.

Novelis

Chirag Agrawal

Head of AI and Simulation