In early 2024, Martur Fompack International (MFI), which makes seating and interior systems for some of the world’s largest automakers, unveiled an artificial intelligence-driven system that enables customers to not only visualise how specific materials would look inside a vehicle, but also to quantify the carbon footprint reductions that would result from choosing materials that the company sustainably manufactures from plastic waste pulled from the world’s oceans.
Among other things, the system shows customers, in real time, the carbon footprint associated with their choices. Since launching the system, the company says it has seen a 34% drop in the rate of carbon emissions per automotive cockpit it manufacturers.
It’s one example of how AI is helping OEMs and their suppliers meet sustainability goals. The Porsche, Audi and Volkswagen brands have been using AI for several years to identify such risks as environmental pollution and human rights abuses with business partners and across their supply chain. AI is also integral to the sustainability-focused activities of the collaborative automotive data ecosystem Catena-X. But which AI use cases are particularly promising for driving sustainability in the automotive industry?
Sustainability goals and responsibilities: Not only can intelligent capabilities give customers visibility into the carbon impact of their product choices, it also can digest a huge amount of data sourced from inside and outside an organisation, then suggest logistics pathways that minimise the carbon footprint associated with specific products.
See, calculate and report on sustainability activities with a high degree of certainty: Intelligent track-and-trace, data collection and analytics capabilities are essential to an organisation’s ability to meet its own sustainability targets, and to comply with the incoming wave of more than 100 sustainability regulations that are expected to impact the automotive industry worldwide. Many of these new regulations apply to entire value chains, meaning one company’s choices can impact the overall value chain’s sustainability profile.
Generative AI can quickly parse huge amounts of content and data across a vast range of sources to keep companies apprised of regulatory and policy developments in markets where they’re active. GenAI also can assist with the potentially daunting task of identifying, collecting, managing and reporting data from various sources across the value chain (such as Scope 3 emission data) in order to comply with varying requirements across multiple jurisdictions. Among them is the EU’s new regulation for electric vehicle batteries, which takes hold in 2027. GenAI can play an important role in the EU programme, helping companies meet requirements to develop a “digital battery passport” for each battery product that includes information related to circular design, composition, recycled/renewable content, etc. These kinds of capabilities also can help the automotive end-user, enabling them to use a QR code, for example to easily access information about their battery’s condition.
Giving sustainability appropriate weight in company-wide decision-making: To comply with carbon-reduction regulations and fulfil sustainability goals, it’s imperative that automotive companies be able to embed, track and act upon sustainability-related KPIs across their entire operation, whether they have multiple manufacturing facilities or just one. As seen with the MFI use case, intelligent modelling tools are yielding insights about how specific decisions across the business, from product design and manufacturing operations to logistics, impact sustainability-related KPIs and a company’s ability to meet its compliance responsibilities, ultimately steering its own operations, its customers and the world toward a sustainable mobility future.
The opinions expressed here are those of the author and do not necessarily reflect the positions of Automotive World Ltd.
Nadine Kanja is SAP’s solution head for Catena-X and SAP Industry Network for Automotive
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