Automotive Logistics — 2025-05-14
Automotive Industry
The use of digital strategies in automotive logistics is becoming ever more necessary to mitigate risks in an increasingly uncertain global market. With natural disasters, shaky economies, the global pandemic and economic trade on uneven ground due to tariffs and trade wars, the need to plan, predict and prevent risk is more pressing than ever to ensure a resilient supply network.
While the automotive industry has excelled at technology adoption in the past, particularly in the form of physical innovations within plants and production lines, it has been slower to catch up in software solutions, data quality and sharing, and integration of pilots into the supply chain. But some OEMs, logistics providers and suppliers are leading the pack and have been piloting data analytics and AI solutions that could be scaled up and shared across the automotive logistics landscape.
While some are still in their infancy, these real-world applications of AI are being tested now to iron out flaws in data quality and management, spot potential gaps in performance, and most importantly, to train people in the supply chain on their benefits.
Real-time supply chain risk management with AI
AI is moving beyond theory and into practical applications and use cases – one of the most exciting being risk management. There has been a shift from reactive problem-solving to proactive real-time logistics orchestration, with the goal of faster decisions and greater decision coverage, ensuring AI tools can handle a high percentage of issues autonomously and freeing up planners in teams for more strategic tasks.
At our recent Automotive Logistics & Supply Chain Europe 2025 conference, Dr Gisela Linge, vice-president of global logistics at Autoliv explained how what was once thought of as a dream future is now possible using AI. She said that Autoliv is using machine learning (ML) for demand forecasting, predictive maintenance and quality control. Autoliv has built a control tower digital twin which it is enhancing daily with more use cases and growing data, and has an AI chatbot which summarises standards across functions and the company to answer questions, help to build training materials, or process descriptions. Similarly, Fabian Pobantz, vice-president of operation digitalisation & IT, supply chain and purchasing at Schaeffler, said AI has been a part of Schaeffler’s forecasting algorithm for many years, and the company is now searching for how this forecasting accuracy with AI can be shared with OEMs.
Pobantz explained that the use cases for AI in forecasting accuracy are growing more important, whereas previously emphasis may have been on things like inventory optimisation.
“This year, it’s about execution and taking it to the broader scale,” Pobantz said. “There is a focus on data quality, and forecasting accuracy which is a common pain point, because that drives the entire supply chain.”
Giving advice to the industry based on Schaeffler’s experience, he said: “Make [your use case] more granular and adapt to your business condition. Small successes with iterations will give you good results, and engagement of the leadership, but also make you aware of the upcoming challenges in regards of processes, standardised systems, connectivity, data quality, accuracy or data acquisition. If these are challenges, work on those first.”
Linge said that Autoliv has been using its control tower and AI tools to help deal with recent supply chain disruptions, such as the tariffs enforced in recent months.
“When we talk about these control towers and digital twins it’s a lot about risk mitigation, getting transparency and being able to react fast, for example we used it when the whole tariff topic gained momentum,” she said. “It’s really good if, within a short time frame of a couple of hours, you can really tell your CEO, ‘This could be the annual impact,’ because then you get more clarity and what we all hate is uncertainty.”
Giving a recent example of real-world AI application at Autoliv, Linge said: “We recently dealt with an incident at a key supplier and the digital twin helped us to immediately see which customers are affected. We could see where, if we didn’t get the inventories out from the different regions, there could be a potential production line stop. It’s a great foundation then for the task force team to work on and get the details.”
“When we look into the planning systems it’s more about improving our decision making, being data enhanced, and for us in logistics, is it a way in demand forecasting to reduce our inventories, our obsoletes, so that’s also a big goal and then in the end making the whole process more efficient.”
We have also seen OEMs begin to make use of AI tools for risk mitigation use cases. Since 2023, JLR has been using Everstream’s AI to monitor for risks such as natural disasters, strikes, data breaches and export issues that could delay shipments. The technology uses AI, predictive analytics and machine learning to help avoid disruption, in combination with “human intuition”, as part of a wider strategy at JLR to build end-to-end visibility throughout its supply chain.
Similarly, BMW has been deploying AI technology across its plants, including at its largest global plant, Plant Spartanburg, where AI is already used daily in its body and assembly shops for quality checks. BMW has also integrated AI-driven systems in its San Luis Potosí plant in Mexico, using historical data to make predictions on the supply chain.
And GM has been using machine learning to give the OEM parameters of the highest risk suppliers in its network, allowing GM to engage with them proactively to put mitigating actions in place.