Watch: Mazda's John Rich on AI and data integration in automotive supply chains
John Rich, manager of data analytics & AI programmes at Mazda, discusses AI, machine learning and maximising the end-to-end impact of these technologies on the automotive sector.
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Developments in AI and machine learning in recent years have revolutionised supply chains in the automotive industry, with these technologies expected to play a significant part in supply chain strategy in the near future. Speaking on the Red Sofa at Automotive Logistics and Supply Chain Global, John Rich, manager of data analytics & AI programmes at Mazda, shared his thoughts on the future of AI in automotive supply chains.
Optimising supply chains with AI
"I think most of our OEMs and supply chains – from tier one all the way down to tier three – they've spent many years working with different types of artificial intelligence, so when it comes to machine learning and deep learning, they've been doing these things for years," he said. "So I think building on that and being able to bring in this new age of agentic automation with AI is really where I think you can see the most benefit."
He went on to detail how taking the outputs of machine learning models and then training agents to take autonomous actions can "mistake-proof" processes and optimise supply chain forecasting.
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The future of technology
Rich spoke of his enthusiasm for developments in technology in the years to come, specifically within manufacturing facilities. "Coming from being inside manufacturing facilities, I think we definitely are on track to start seeing more robotic automation in the factories and different applications of humanoid robots," he said.
But there's something perhaps more exciting that has caught Rich's attention. "I think the thing that's most interesting for me is in the supply chain, you always deal with missing data or inaccurate data or data quality issues, and something that's really come along just in the last six months is something called joint embedding predictive analytics or predictive architecture," he shared.
"So when you combine that with your traditional LLMs, there's a whole new possibility of AI and incorporating that with agentic and agentic mesh solutions, where you're programming different agents to do very specific things, you can look at mass quantities of data and really see the trends, even if you have data quality issues." Rich elaborated.
Collaboration is key
According to Rich, integration and interoperability will be crucial to the next phase of supply chain optimisation. "There are so many silos that we have in supply chain, whether it be from the OEM to the supplier or even within a company itself, where you have this act of data sharing, has always been difficult," he said.
"You're copying data, you're sharing it either via email or through cloud storage or even on prim locations, and I think adopting new standards that are coming to life with things like open sharing protocols..., new open table formats, things like clean rooms... I think that's really the key because the more people that have access to data – good, clean, governed data – the better success you're going to have with any agentic or or artificial intelligence implementations," he concluded.
