As the manufacturing sector evolves, the convergence of artificial intelligence (AI) and sustainability is emerging as a powerful force for change.
Traditionally seen as heavy consumers of resources and significant contributors to environmental impact with two-thirds of the world’s emissions coming from heavy industry, sectors like steel and chemicals are now leveraging AI to transform their operations, achieving both profitability and sustainability.
The 2024 Fero Labs Industrial Survey highlights a telling insight: 64% of senior managers within a plant identified increasing profits as their top business target for the year. In contrast, only 41% placed significant importance on reducing emissions. This disparity underscores a common belief in the industry that profitability and sustainability are mutually exclusive objectives. However, with AI software, manufacturers can meet their profit targets while also reducing their environmental footprint.
AI’s ability to analyze and optimize complex processes is at the heart of this transformation. In one instance, a major steel manufacturer used AI to optimize its blast furnace operations, resulting in a 3% reduction in fuel consumption and a significant decrease in CO2 emissions. Similarly, a chemical company adopted AI to refine its production processes, achieving a 15% reduction in waste and improved energy efficiency.
When global steel producer Gerdau incorporated AI to streamline their usage of ferroalloys, they reduced their alloy costs by $3 per ton, whilst improving their Scope 2 and 3 footprint by reducing the overall volume of alloy additives they needed to purchase and have delivered each year. These examples demonstrate that AI is already driving both economic and environmental benefits for early adopters.
Despite these successes, the adoption of AI in manufacturing has not been without challenges. The industrial survey revealed that plant managers within a factory often act as technological gatekeepers, hesitant to adopt new technologies. This resistance is understandable given the industry's history of relying on established methods, and the economic and safety risk of making errors. However, the potential benefits of AI far outweigh the initial hurdles in an industry where optimization and instructions are often still recorded by hand.
Imagine a scenario where achieving your profit targets also means operating sustainably. With industrial AI, this is not just a possibility but a tangible reality. AI-driven predictive maintenance can prevent unexpected downtimes and reduce waste, while process optimization can enhance resource efficiency and minimize energy consumption. Energy management systems powered by AI can monitor and control usage in real-time, reducing the carbon footprint of manufacturing operations.
If you knew you could achieve your profit targets sustainably, why wouldn't you? There wouldn’t need to be any trade-off between economic or environmental objectives. This question is central to the future of manufacturing. Embracing AI not only drives profitability but also aligns with the growing regulatory and consumer demands for sustainable practices. That’s good for the plant and the planet.
[Originally published: AIBusiness.com]
