Case studies

Predictive analytics power world-class manufacturing

An inside look at Novelis and the Plant of the Future

A global leader in aluminum recycling and rolling, Novelis recycles nearly 2.3 million tons of aluminum a year. But with growing demand, Novelis needed to unlock capacity across their network. See how Deloitte helped the manufacturer deliver the Plant of the Future with predictive analytics and maintenance, machine learning, computer vision and AI.

Novelis and the Plant of the Future

Building a shared vision

Sustainability is core to business operations at Novelis, being one of the world’s top aluminum recyclers. In recent years, sustainability trends have driven growth in aluminum demand, requiring the manufacturer to unlock capacity in dozens of plants across the globe. While Novelis had use cases around predictive analytics, they didn’t have the strategy to scale across their manufacturing facilities.

Novelis teamed up with Deloitte to help realize the value of their manufacturing investments. Stakeholders across the global organization aligned with Deloitte’s industry specialists to build a roadmap and transformation strategy to drive value over three years. The Plant of the Future program focuses on eight areas and 25 improvement targets inspired by Deloitte’s smart manufacturing use cases.

Plant of the Future use cases

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Improving workplace safety

At manufacturing facilities in Ohio and Brazil, an AI model uses video footage to spot safety risks. The collision avoidance solution gives an extra layer of awareness to mobile equipment operators and pedestrians, alerting them to the surroundings and helping prevent incidents in real time.

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Improving cycle time

In Kentucky, operators had to manually adjust the mill speed to counteract possible mill vibration. The solution allows Novelis to predict vibration—an indication of impending failure—and allows workers to run the mill more aggressively when they know it’s operating smoothly.

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Reducing unplanned downtime

At plants in New York and Brazil, sensors monitor conditions such as vibration and temperature. Machine learning models use that information, combined with process and product data, to contextualize and detect potential failures before they occur, then automatically creates work orders to fix them.

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Contact us

Interested in learning more about our smart manufacturing capabilities? Reach out.

Kelsey Carvell

Principal, Supply Chain and Operations

Deloitte Consulting LLP

kcarvell@deloitte.com

Rohini Prasad

Digital Supply Chain Leader, Supply Chain and Operations

Deloitte Consulting LLP

rohprasad@deloitte.com

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