AI IN SUSTAINABILITY
failures, thereby minimising downtime, extending the lifespan of machinery and improving the safety of workers. This not only conserves resources but also reduces waste, contributing to more sustainable industrial operations.
GUNTHER ROTHERMEL: AI is revolutionising the way we approach predictive analysis for sustainability by enabling more accurate and timely insights. There are a couple of interesting use cases.
AI-powered algorithms can analyse data from sensors and IoT devices to predict equipment failures before they occur. This not only reduces downtime and maintenance costs, but also minimises the environmental impact of unexpected breakdowns. If we take it a step further, this can also help transition to a circular economy by predicting the entire lifecycle of products and materials.
If we look at supply chains, AI-driven predictive analysis can help companies to forecast demand, identify potential disruptions and recommend alternative routes for suppliers. This leads to more efficient resource utilisation.
For our customer ZF Friedrichshafen, a multinational automotive supplier that develops chassis and drive trains for vehicles, and industrial technology, correct planning and forecasting are critical parts of their business. Using AI, they drastically improved forecast accuracy, using nine times more planning combinations and running 92 % faster. sustainabilitymag. com 167