More than 5,000 leaders, including businesses and investors, have said they will slash U.S. carbon emissions by half by 2030, and work toward net-zero by 2050. Manufacturing stands to play a significant role in meeting this ambitious goal. And digital technologies can play a significant part in enabling them to achieve sustainability goals.
By deploying artificial intelligence in manufacturing systems, organizations can unify and analyze massive datasets to generate real-time insights. For example, AI can be used to monitor internet of things (IoT) data and dynamically adjust energy consumption. AI-driven visibility into the manufacturing chain allows businesses to optimize consumption of water, energy and other natural resources, as well as address the biggest emissions sources. And AI can analyze the carbon emissions of various transportation modes and recommend the most sustainable options. That said, this requires the establishment of cross-organizational data-sharing, and consolidation of fragmented data, across suppliers.
Apart from providing detailed insights into manufacturing waste, generative AI can help innovate reusable packaging materials, optimize material layout during cutting and machining, and design products that are biodegradable or recyclable.
By analyzing product lifecycle data using AI models, manufacturers can analyze the total environmental impact of their products, including greenhouse gas emissions, resource usage, pollution and waste creation. This is increasingly becoming possible due to IoT data from smart, connected products. Today, aircraft manufacturers are optimizing the design of components for aerodynamic performance, structural integrity, fuel efficiency and material consumption, as well as for simulating and testing virtual prototypes to conserve physical and financial resources. Finally, generative AI tools can monitor various information sources to keep manufacturers up to date with the latest trends, regulations, consumer expectations and improvement opportunities in the field of sustainability.
To harness the power of AI, organizations must prioritize applications that integrate quality assurance at every stage of the product lifecycle. This approach is crucial to avoiding failures and reputational damage. It ensures that the results produced by AI are both explainable and traceable.
Pioneering Innovation With Digital Twins
Leading automobile manufacturers are building digital twins of their factories — a virtual replica of physical operations — to simulate production assets and processes before implementing them in the real world. They’re examining every process in the automotive lifecycle for energy efficiency, and trying out different materials and designs to optimize resource usage and cut waste.
An example from the high-tech world is Foxconn’s digital twin of a new factory in Guadalajara, Mexico. Foxconn anticipates increasing the manufacturing efficiency of complex servers using the simulated plant, leading to reducing kilowatt-hour usage by over 30% percent annually.
The IoT-Enabled Digital Thread
Manufacturers need to take a holistic approach to sustainability, focusing on products that have longer operational life and can be repaired, reused or recycled. This may require drawing on data insights from IoT-connected devices, and feeding it to a product lifecycle management process to refine circular product designs.
Data from products in service can be analyzed to proactively address product issues, or refurbish or recycle goods at the end of their useful lives. Further, IoT data can identify used product components that are fit for reuse, eventually creating a closed-loop system where materi…
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