No longer simply confined to autonomous vehicles (AVs), AI is showing its prowess throughout the value chain, as GlobalData’s new report ‘Artificial Intelligence in Automotive’ demonstrates.
The report shows the ways that AI will allow new insights and predictive capabilities, heralding a new operational way of working for the automotive sector.
According to the GlobalData report, all segments of the value chain can benefit from AI implementation. Upstream (tier one, two, and three suppliers and automakers) can benefit, for example, from the use of computer vision in vehicle defect detection during the manufacturing process, or the use of smart transport robots in the factory to streamline production. Downstream (sales and the increasingly important aftermarket) profits from conversational platforms and context-aware systems alongside data science and ML.
Gopal Kambo, Thematic Analyst at GlobalData, comments: “AI has numerous use cases beyond autonomous vehicles that will be essential for automakers to apply as they face the robust headwinds of sustainability, the pivot to electrification, overcapacity, and the prospect of decreasing vehicle demand due to the challenge of shared mobility.”
Kambo adds: “Data is the bedrock of AI, and its volume will only continue to grow as autonomous, software-defined, and connected vehicle functions increase in number and scope. AI implementation is therefore crucial for automakers and suppliers to avoid outsourcing value-add opportunities to the big technology players. This is even more useful for high-end automakers, which can justify the spend on AI by tying the technology’s use to the increased premium of their brands.
“AI also plays a crucial role in closing the feedback loop between upstream and downstream by incorporating sale and post-sale vehicle data into predictive modelling, regulating production more closely to demand. Automakers can thus operate in a more agile relationship with real-world events.”
The report contains a case study detailing the way that Porsche is working with Fraunhofer IPA to use ML for demand forecasting and planning, which has made its supply chain less expensive, more agile, and adaptive to real-time demand. Its customers have also gained additional flexibility in modifying vehicles orders closer to delivery dates.
Kambo concludes: “It is unlikely that AI will be the silver bullet to anticipate Black Swan events that can have a disruptive effect on the automotive sector. For example, the current automotive chip shortage has been due to human oversight and just-in-time manufacturing principles, coupled with the external largely unpredictable shock of the pandemic. However, what AI will certainly do is give automakers and tier-1 suppliers the important insights needed to maintain profitability in the face of several existential threats.”
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