With automation and AI towards more sustainability

With Koray Köse, chief industry officer at Everstream Analytic, and Dugan Trevathan, then enterprise account executive at Carbmee, two experts spoke at this year's Factory of the Year Congress about the significance and possibilities of AI in sustainability management. For Köse, the AI era has already begun.

This is evidenced not only by the rising star Chat GPT, which completely skipped the early adopters phase of the traditional technology acceptance curve and shattered the theory of the hype cycle. The 65,000 startups in the field of AI and synthetic data last year and the nearly 200 billion US dollar GenAI market also testified to this by the end of 2023.

The AI evolution is chaos - volatile and unstable

Among the most important drivers of AI are the (still) unregulated environment, which allows for a high degree of experimentation, and sustainability guidelines that create a multitude of application possibilities. However, increased fraud through deepfakes, the (un)intentional disclosure of sensitive data, and higher hurdles in verifying information before use hinder the deployment of AI. A circumstance that artificially generated data can solve.

“With artificially generated data, you expand the ability of AI to positively influence your business,” Köse knows. Their advantage: LLMs can be trained much faster with synthetic data than with natively generated data.

Their use in generative engineering accelerates the innovation of new products as well as the adaptation of specifications to improve efficiency and performance, while in generative design they aim to optimize design functionality for specific purposes.

Tips: Sustainability risk management with AI

However, for Köse, the most important use case for AI is sustainability risk management. To achieve sustainability and competitiveness simultaneously, companies should take advantage of the current situation. How? “Summarize complexity by creating thoughtful simplicity for analyzing complex data to make timely and high-quality decisions in dynamic situations,” is his first tip.

He also advises engaging and investing to influence and advance sustainability risk management as a stakeholder. In data-poor environments, relying on synthetic data to test the effectiveness, efficiency, and resilience of supply ecosystems is as advisable as automating low-value, high-effort tasks to free up resources for creativity.

Identifying environmental risks in the supply chain

“Environmental risk is business risk,” says Dugan Trevathan. He explains this with the impact of the CO2 border adjustment mechanism CBAM. According to this regulation, a seven-billion-euro automotive company would have to expect an annual CO2 tax of 160 million euros in 2034. By reducing CO2 emissions, 55 million euros could be saved annually if 30 percent of the previously undiscovered savings potential were utilized by then.

Especially when it comes to decarbonization, there are vast amounts of data in companies' ERP, SRM, PLM, and CAD systems that the end-to-end carbon management solution carbmee can use to control carbon reduction through the use of automation technology.

Based on this, the AI-powered Environmental Intelligence System (EIS) not only provides full transparency over the CO2 balance of the supply chain at the bill of materials, product, and service level but also AI-based reduction recommendations for materials, manufacturing processes, energy sources, and suppliers. This allows greenhouse gas emissions, financial, and sustainability risks to be reduced with AI along the entire supply chain.