How can the metals industry accelerate adoption of digitalization to reduce carbon footprint and improve productivity?

By Venkatesh Muthusamy, Lead Data Scientist at Canvass AI

The metals industry constitutes the backbone of economic growth and development. It plays a critical role in the industrial value chain by supporting thousands of jobs and supplying essential raw materials to key manufacturing and construction industries. While the COVID-19 pandemic further accelerated production and demand irregularities, the industry recovered fast in 2021, and this trend is likely to continue in 2022 as well. According to the World Steel Association, the global steel demand is likely to increase 2.2% year-over-year in 2022 from 1,855.4 Mt to 1,896.4 Mt.

Concurrently, being one of the largest producers of GHG emissions, the metals industry also needs to reduce its carbon footprint and undertake decarbonization measures to combat the threat of climate change. The World Steel Association estimates that for every ton of steel produced in 2018, 1.85 tons of carbon dioxide was emitted, culminating to approximately 8% of global carbon dioxide emissions. Government regulations targeting climate change and increasing public interest in sustainability leaves the metals industry with little choice but to explore technologically and economically viable ways to decrease carbon footprint and adopt more sustainable solutions.

What does this mean for the sector in 2022? With demand likely to skyrocket and equal pressure to reduce their carbon footprint, 2022 must be the year that industry accelerates the adoption of digitization technologies to meet these opportunities and challenges head on, says Venkatesh Muthusamy, Lead Data Scientist at Canvass AI.

Where the metals industry can get the most value from AI

Industry 4.0 is spearheading how the metal industry is transforming its operations. Powered by new technologies, including the Internet of Things (IoT), cloud computing, and AI/ML, Industry 4.0 allows manufacturers to immediately make data-driven decision to leverage predictive maintenance, control their processes, optimize their assets, minimize downtime, and improve product quality. However, while the industry has been collecting data for some time now, it falls behind in leveraging its full potential to gain insights that can help drive greater efficiencies.

AI and ML are key enablers to accelerating the benefits of Industry 4.0. Together they are critical to extracting value from the massive amount of data in real-time to improve operational performance. Today, AI is helping the metals industry primarily in three crucial areas: optimizing assets, optimizing processes, and optimizing energy. For a metals company, the immediate benefit of AI is to aid operations engineers to make data-driven decisions based on their actual process and asset performance. Instead of operating to past performance, operators using AI can control their operations to actual performance. Further, as more and more metal companies commit to net-zero targets, the AI-powered digital transformation is helping them build sustainability into their operations.

And this is happening today. Across the world, leading metals companies are using Canvass AI to improve quality, lower costs, reduce waste and bring down carbon emissions. Every day, the users use AI to unlock intel from the data to forecast future production throughput, control production quality, lower energy consumption, and cut waste across all stages of steel production, including raw material processing, steel making and casting, and hot rolling.

You may be asking why these metal companies have been successful when Gartner estimates that 85% of AI projects fail. I can bring this down to three commonalities:

  • Utilized an Industrial AI platform: Not all AI platforms are suited for all environments. And this is particularly true for industrial environments. Industrial data is noisier and exponentially more complex because of its volume, velocity and variability. AI systems designed to be used in financial or retail settings simply do not meet the demands of an industrial environment. Metals companies who extracted meaningful value with AI have instead utilized Industrial AI platforms, that have been designed from the ground up specifically for industrial challenges.
  • Empowered the operations team: Some companies prefer to build up their AI capability by amassing a team of data scientists. However, this can take years to develop, and it also puts the operational SMEs – i.e. the industrial engineers and operators – on the side lines. Companies that have been successful in extracting value from their data have empowered their operation steam with AI so that they add their expertise to contextualize the data.
  • Focused their efforts on high ROI: Metal companies that have been successful at extracting value from AI have focused their efforts on processes that consume the most energy and have higher raw material costs/operational costs. By doing so, they have validated their investments with increased line-speed, improved quality, less scrap, and reduced emissions.

AI-powered Future

The metal industry stands at a critical juncture. On the one hand it needs to produce quality production while keeping costs under control and on the other hand, the industry must decarbonize and fast-track its net-zero targets. The metal industry stands to gain immensely by focusing on AI-powered digital transformation. The World Economic Forum estimates that digitization can unlock $400 billion in the metals industry in the next decade. Further, it makes business sense to use AI to reach its net-zero target. McKinsey estimates 14% of the global steel companies’ potential value is at risk if they do not decrease their environmental impact. As these pressures continue to mount, the time is now for the metals sector to transform the future of their industry with the power of AI.

Venkatesh Muthusamy Canvass Ai, Industry Today
Venkatesh Muthusamy
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