Christopher Ludwig on building supply chain resilience from the inside out

Whether confronting Covid-era shutdowns, chip shortages, canal blockages or climate-induced volatility, automakers are shifting from reacting to crises to systematically preparing for them. But the question that Christopher Ludwig, chief content officer at Automotive Logistics, poses to the industry is: What have we learned from all this?

At this year’s Automotive Logistics & Supply Chain Digital Strategies North America event, Ludwig flipped the script from his usual role as host to interviewee, taking a seat on the Red Sofa to share his insights on how automakers can reshape their digital backbone.

Offering a grounded yet urgent view of supply chain priorities, he cautions against focusing on speculative AI hype around digitalisation. Instead, he urges the industry to drill into operational realities – such as building internal capabilities, from robust data foundations to collaborative cultures – that enable lasting resilience.

Much of the conversation echo’s themes that Mazda’s John Rich articulates in his session on hyperautomation: that the conversation around AI is often misunderstood or overstated. “AI is not a sentient being… What AI is today and what it can be mostly used for is intelligent automation,” Rich states.

Rather than speculative, one-size-fits-all AI solutions, the focus is shifting toward Intelligent Automation – practical, layered technologies like machine learning, robotic process automation, and agent-based systems applied to real operational problems.

Reinforcing this point, Ludwig comments: “AI in supply chain operations isn’t as simple as just plugging something in… You need a semantic layer to enable data integration and understanding across systems.”

He points to companies like Nissan, who is already showing what’s possible with integrated, cross-functional teams that unite supply chain, IT and procurement to push digital capabilities forward from the inside out.

Throughout the conversation, Ludwig emphasises that while generative AI and agentic systems may eventually support strategic decision-making across complex, multi-tier supplier networks – as in the case of Nissan – they can only operate effectively on well-curated, secured data streams. That work, he notes, is already underway – but needs to accelerate.

And it’s not just technical readiness that matters. Trust – in people, in systems and in shared value – is what will determine the effectiveness of next-generation supply chain networks.

In the conversation that follows, Ludwig unpacks where digital transformation is gaining real traction, what internal barriers still remain and why the road to digital maturity begins with culture as much as with code:

What are the key issues you’re seeing emerge across the automotive supply chain right now? First of all, I have to say, it feels a bit strange to be on this side of the Red Sofa – answering questions instead of asking them. I have a lot more sympathy now for the guests I’ve interviewed here in the past.

There are a few key issues that we can break down. On one hand, we kicked off the event with a time-lapse of the many crises we’ve faced over the last five or six years – Covid, the semiconductor shortage, the Suez Canal blockage, extreme weather, and now tariffs. There’s a sense of “here we go again,” but also a critical question: What have we actually learned?

Because going through these disruptions without gaining insights would be the worst outcome. Most people coming into 2025 understand that we need to be more prepared – we need better visibility, stronger connectivity and the ability to map out our tiered supply chains and logistics so we can respond more quickly. That could mean avoiding tariffs, shifting suppliers or moving products ahead of time – and ultimately saving significant costs.

Interestingly, there’s a convergence happening. Some of the tools and data we’ve been working toward for compliance – such as demonstrating that materials didn’t come from regions using forced labour – can now also help us optimise operations. So, yes, it’s a bit of a cliché to say “never waste a crisis,” but in this case, it’s absolutely true.

Now, in terms of digital transformation – a key theme at this event and in our work at Automotive Logistics – this is a process that must be approached in phases. Even though many of us use generative AI or chatbots and can see their power, implementing AI in supply chain operations isn’t as simple as just plugging something in. Especially when using real operational data, you need the right infrastructure first.

That means setting up a data lake, implementing a semantic layer to enable data integration and understanding across systems, and connecting legacy mainframe systems (in the case of traditional OEMs) to cloud-based services and tools. These are foundational steps. Once that’s in place, you can start to apply machine learning, analytics and AI.

As John Rich from Mazda mentioned, even companies that haven’t yet used AI in a significant way often have the necessary foundation – and even the people internally who can apply these tools. That’s the positive message: we’re not starting from zero, and many organisations are actively accelerating these efforts – driven, in part, by the need to respond better to crises.

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Digital transformation requires collaboration across teams and organisations, from OEMs down to tier suppliers. What are your thoughts on collaboration and trust across the supply chain? I think we can split this into internal and external collaboration.

Internally, manufacturers need strong cross-functional collaboration. That means aligning data teams, logistics, business operations and IT. The logistics team might say, “I need visibility of this shipment or access to freight payment data,” and they need to work closely with IT to build solutions that are fit for purpose.

What doesn’t work anymore is the old model – logging a request with a centralised IT team, waiting weeks for a response, and finally getting a solution six months later that’s already outdated. We need agile, iterative approaches.

We’ve seen great examples of this. For instance, Nissan’s supply chain management innovation team – led by Will Leak and including Grecia Luna, one of our speakers – has become increasingly cross-functional. It already worked closely with IT and data, but now it’s being integrated with purchasing and manufacturing to drive shared processes, systems and skills. In fact, supply chain management may be further along in this transformation because of the sheer volume and complexity of the data it handles.

Externally, it’s a mix. Some elements of supplier monitoring don’t necessarily require active collaboration – tools can be used to check for cyber, weather or financial risks. But the most powerful solutions will come from true information exchange – where both parties are committed to a shared outcome.

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That includes logistics providers or tier-one suppliers understanding that sharing data securely isn’t just about ticking a compliance box. It’s about building a stronger, more optimised supply chain. That may raise tough questions, like, “If this optimises routes, does it remove some of the work I do?” These are complex discussions – but the potential benefits of transparency and data-sharing are clear.

Of course, trust and fear are factors too – internally, some people worry tools will replace their jobs. Externally, there’s concern about how customers might use shared data. That’s why it’s so important to have both personal relationships and robust systems and agreements that ensure security and trust are baked in.

That’s a good segue into Catena-X, and some of the data exchange use cases we’ve seen on stage today such as Ford and Flex Automotive. Looking ahead, where do you see digital transformation going? Catena-X is a great example – and it’s exciting to showcase it here in North America after a few years of focus in Europe. While it’s not the only data-sharing solution, it illustrates what’s possible. Some important use cases are emerging – around sustainability, compliance, traceability and even integrating logistics providers into the network.

Two areas I’d highlight for the future:

1. AI: I’m more of an AI optimist than a doomsayer, but in the meantime, the sheer complexity of supply chains and the volume of data is the perfect domain for AI. Humans will still make the most strategic decisions, but identifying risks at the tier-four level or optimising a supply chain across all tiers – AI is well-suited for that. For example, when an engineering change occurs, it usually triggers costly disruptions and obsolescence down the supply chain. But with simulation tools, you could model the impact ahead of time and make better decisions – maybe even avoid the change or approach it differently.

2. Digital twins and control towers: Sometimes people dismiss control towers as old technology, but the integration with modern systems is evolving. When you connect operational systems with new tech, you get much better real-time visibility. Even if we’re not fully predictive yet, companies can react faster and more intelligently – and in more cases, mitigate the problem.

Eventually, could we see immersive digital twins in a metaverse-style environment? Maybe. But for now, the steps we’re taking toward using strategic data in real-time ways are already making a difference – and that will only accelerate in the months and years ahead.

Christopher Ludwig unscritped on the Red Sofa