Manufacturing and ESG: Facing Challenges and Embracing Opportunities
Due to a combination of regulatory pressures, stakeholder expectations, global challenges, and jostling for competitive advantages, ESG has been at the forefront of anxious manufacturing industry internal discussions for the last few years.
On one level, many manufacturers see the clear advantages of investing in ESG strategies, such as being able to differentiate themselves from competitors and attract capital, as well as the opportunity to be part of an industry wide effort to build a more sustainable world, but on another level, connected to the practical pressures of running their business, many have a number of significant worries and reservations
The most prevalent of these is the belief that adopting ESG practices will lead to poorer financial performance. However, the reality is that ESG and business goals can complement one another. By optimizing operations to reduce energy consumption and manufacturing costs, businesses can often improve their financial performance while also attracting investment and talent. With employees increasingly prioritizing sustainability when considering job opportunities, embracing ESG principles can also help manufacturers attract talent.
Another common belief is that ESG tracking is both a laborious and costly challenge. The reason for this is that there is a lack of unified standards and structure for reporting, as well as the complex and evolving regulations that they must adhere to, which can vary across different jurisdictions and require accurate and robust disclosure. Many manufacturers do not have the resources and teams to allocate to this task.
Integrating AI Into ESG Frameworks for Manufacturers
This article puts forward the notion that Artificial Intelligence can be the key to alleviating manufacturers difficulties with ESG, as it can allow them to automate tracking and reporting while at the same time ensuring that they are compliant with the latest relevant restrictions.
Moreover, AI can assist manufacturers in achieving their ESG targets by optimizing energy usage, identifying areas for improvement, and providing insights for reducing environmental impact, as exemplified in the following cases:
Case 1: Energy Management
To meet customer demand, many manufacturers operate on a short-term product schedule of one to two weeks. It's common for Governments to set monthly caps for electricity usage for industry, and when manufacturers go above these thresholds, they are charged at a higher rate. This represents a challenge for many companies,as to meet customer demand, many manufacturers operate on a short-term product schedule of one to two weeks, making it difficult to accurately forecast electricity consumption during that period.
By leveraging AI, manufacturers can analyze their product schedules and predict energy usage for upcoming production runs. If projected consumption exceeds the threshold, the AI can recommend energy optimization strategies, such as redistributing production across different months to lower overall usage.
For example, optoelectronics manufacturer AUO’s smart water grid system called Smart Grid integrates Profet AI’s machine-learning and AI platform to efficiently collect electricity consumption data, resulting in an 8% annual reduction in power consumption. When electricity exceeds certain thresholds, the Smart Grid's AI makes automatic adjustments. Similarly, thanks to the AI of the Smart Grid, the company's Taichung plant successfully reduced water consumption by 23% and carbon emissions by 20% between 2018 and 2020.
Case 2: Tool Parameter Optimization
Manufacturers can leverage AI to optimize their energy and resource efficiency, achieving their goals for sustainable development. By predicting the most efficient tools and equipment to use, AI can inform teams on how to manage cooling systems and set the right parameters for best performance, leading to significant energy savings and meeting ESG targets.
One prominent example of this is TSMC, which implemented an AI-powered chilled water control system, that used machine learning to analyze over 1,000 parameters of the chilled water system. By determining the optimal energy efficiency parameters, TSMC increased energy efficiency by 2%, saving an estimated 30 GWh of electricity per year. This successful implementation not only solved the two major efficiency problems of aging equipment and complex nonlinear systems but also helped TSMC fulfill its commitment to green manufacturing and environmental protection.
Case 3: Maximizing Material Efficiency
In many industries, material costs can make up a significant portion of production costs, and the prices of individual materials are subject to market fluctuations. By implementing AI alongside ESG strategies, rising operating expenses, such as raw material costs, can be effectively combated.
An example of AI's potential impact can be seen in the case of Profet AI's client, a glass manufacturer. The glass melting process requires natural gas, oil, and electricity, all of which contribute to high greenhouse gas emissions. AI has enabled the manufacturer to optimize the use of these energy sources based on market costs and energy savings. As a result, the manufacturer has reduced its environmental footprint and achieved significant cost savings.
Furthermore, by aggregating historical data on materials used in their processes, the manufacturer was able to leverage AI to fine-tune their formula and reduce material waste. For example, when making the base or decoration of a snow globe using resin, temperature and humidity are critical in determining the speed and success of the molding process. By utilizing this data, they were able to reduce material consumption in the base-making process by 10 to 15%.
Conclusion
Manufacturers are increasingly concerned about the costs and difficulty involved with ESG reporting and strategies. However, by leveraging advanced AI capabilities in ESG management, businesses can achieve their sustainability goals with ease while also reducing operating costs and increasing revenues. AI can optimize material and energy use, leading to significant environmental and cost savings. With AI, manufacturers can streamline their ESG efforts, making the process easier and more effective. As sustainability continues to be a critical issue, manufacturers who want to remain competitive must embrace AI to overcome cost concerns and achieve the benefits of ESG strategies.