How BMW Group has embraced AI for positive use cases and to improve sustainability | AWS re:Invent | VentureBeat

How BMW Group has embraced AI for positive use cases and to improve sustainability | AWS re:Invent | VentureBeat

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Artificial intelligence (AI) is helping many different industries and is having a particularly strong impact in the automotive industry. Among the most exciting use cases is for fully autonomous vehicles, but that’s not the only area where AI is having an impact. For example, Microsoft and Mercedes-Benz are working together to improve automobile production efficiency. 

At the AWS re:Invent cloud conference this week, BMW Group outlined the impact that AI has had on its organization and detailed emerging use cases where AI will yield future positive business outcomes.

In a session, Marco Görgmaier, GM, data transformation and artificial intelligence, BMW Group, said that his team had built up a library of thousands of data assets across the company that can be reused for analysis and AI. Since 2019, he said his team has been able to deliver more than 800 use cases that have yielded over $1 billion in U.S. dollar value. The use cases span research and development, logistics, sales, quality and supplier network.

“The vision and the mission of our team is to drive and scale business value creation through the usage of AI across our value chain,” Görgmaier said.

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BMW driving toward a sustainable future with some help from AI

An emerging area where BMW is now investing resources is in helping to improve sustainability. 

Görgmaier commented that 60% of the world’s population lives in cities and urban areas and that’s also where 70% of greenhouse gas emissions are generated. What BMW is now trying to do is assist city planners in solving problems to help reduce emissions.

BMW is already helping with machine learning models that are able to predict how traffic regulations can potentially help to reduce both traffic and gasoline emissions. ML models are also used to help identify where there isn’t yet sufficient electric vehicle charging infrastructure. Görgmaier said that a lack of charging infrastructure prevents people from switching to an electric vehicle, which in turn has an impact on sustainability.

There is also a BMW ML effort to help predict the impact of parking space availability and pricing on driving patterns. Those patterns include commuting routes and traffic, which also will have an impact on emissions.

Driving geospatial information with Amazon SageMaker

Görgmaier said that many of the urban sustainability issues that BMW is trying to help solve can benefit from geospatial information. That’s where BMW is starting to make use of new geospatial capabilities in the Amazon SageMaker ML tool suite that were just publicly revealed this week.

One area where BMW is looking to benefit from geospatial ML is for helping to predict when an organization with a fleet of vehicles will be able to transition to electric vehicles.

“We set up the goal to train machine learning models to learn correlations between engine type and driving profiles,” he said. “The rationale behind that was if such a correlation would exist, then the model could learn to predict the affinity of certain drivers for an electric vehicle based on their profiles.”

As BMW was working with fully anonymized data at a fleet level, it had to use GPS traces and geospatial data to make the correlations.

“At the end of the training, the model was capable of predicting how likely it was for specific fleets to convert to EV with an accuracy of more than 80%,” Görgmaier said.

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