AI and digital twins: Use cases driving sustainable manufacturing
* Register (or log in) to the AI4G Neural Network to add this session to your agenda or watch the replay
AI has the potential to revolutionize the way we produce goods and reduce our impact on the environment. By leveraging the power of machine learning and predictive analytics, we can optimize production processes, reduce waste, and increase efficiency.
Using the concept of a digital twin, i.e., a virtual representation of a physical system such as a manufacturing plant or a supply chain, this talk discusses use cases how AI/ML can optimize production processes, reduce waste, and increase efficiency: (a) AI for energy consumption optimization: changes to the production schedule that align with periods of lower energy demand, or recommending the use of more energy-efficient equipment, leads to lower energy consumption; (b) AI for supply chain optimization: the analysis of data on transportation routes helps to identify opportunities for reducing emissions to recommend optimal modes of transportation that have a lower carbon footprint, or to change routes that reduce the distance goods must be shipped; (c) AI for resource recovery and reuse: we can identify opportunities for circular manufacturing, in which resources are recovered and reused rather than discarded as waste. For example, AI might suggest new products that can be made using recycled materials, or recommend ways to repurpose waste streams as raw materials for other processes.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.