Beyond visibility: How automotive supply chains are building predictive and autonomous resilience

For years, supply chain visibility has been the defining objective of digital transformation in automotive logistics. Companies have invested heavily in control towers, transportation management systems, supplier portals and tracking platforms in pursuit of gaining a more accurate understanding of where parts, vehicles and inventory are at any given moment.

At ALSC Digital Strategies North America 2026, executives from Nissan, Ford, Bridgestone, Gestamp, IAC Group, OPmobility and Glovis America describe how the industry's digital ambitions are evolving beyond visibility toward something more predictive and eventually to autonomous resilience.

Across OEMs, suppliers and logistics providers, a common roadmap is emerging. It begins with trusted data, expands through connected ecosystems, develops into predictive intelligence and ultimately leads toward increasingly autonomous operations.

Building the foundation: Trusted data before AI

The message repeated most consistently throughout the conference was that artificial intelligence is only as effective as the data underneath it.

The industry's most important work towards AI reliability lies in standardising data, harmonising processes and creating trusted information across organisations.

Patrick Bauer, vice-president of supply chain at IAC Group, describes data quality as a long-term journey rather than a destination. The company has spent years improving ERP discipline, compliance and inventory accuracy to establish confidence in its data.

“Data accuracy and quality is a journey, and I’m not sure if we’ll ever get to a cutoff point where we trust everything we have,” Bauer says. He adds that the goal is to herd the company’s plants and supply chain to a point where they trust their data and can use it to make beneficial business decisions.

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He explains that simple data points being inaccurate, like suppliers sending advanced shipping notices (ASNs) that are incorrectly labelled or without the correct serial number, can lead to a lot of time and energy being spent on correcting this information.

“We don't want our plants doing anything manual,” he adds. “We want it to be automatic to receive, and in any plant the first error point is going to be around the receipt. If you don't receive it properly, you're going to have errors throughout the facility. We're really focusing with our plants on that and really there's no excuse for the supply base.”

Sean Moses, head of vehicle logistics planning at Glovis America, highlights an even more basic challenge in standardising data fields and date formats across organisations.

“It'll be as just standardising dates and data fields,” Moses says. “It sounds silly, but it's totally true that standardising these, how we talk to each other and the APIs that we share in EDI, is so critical because if we don't have clean data, the whole getting towards agentic AI is all for naught.”

Jordan Pavel, regional project director and chief of staff for North America at OPmobility, argues that organisations must resist the temptation to connect everything at once. Instead, companies need a disciplined approach to deciding which data should be connected, when it should be connected and how quality can be maintained as systems expand.

“One thing that we did is we really focused on establishing a clean point,” she explains. “So, any data that's going into our TMS, that's new data, and we make sure that we have minimum 98% data accuracy going into our system. And I think it really helps our people prioritise. If you know that everything that is going in is clean, then we can really focus our efforts on what's already there and how do we get it up to that standard.”

That philosophy is echoed by Gestamp. Muhammad Anas, digital transformation specialist at Gestamp North America, explains that the Spanish steel supplier has been delivering standardised architectures for common data foundations through its Gestamp Smart Factory vision.

The programme, launched in 2016, has already delivered more than 400 Industry 4.0 projects across 70 plants globally. Rather than pursuing AI first, Gestamp focused on creating standardised architectures, traceability systems, IoT connectivity and common data foundations.

Only after those foundations were established did the company begin scaling AI applications. Today, more than 10,000 AI rules are running in production across its operations.

As Anas explains, the journey follows a deliberate sequence of connected systems and automated production, connected factories and real-time intelligence, and finally, AI-driven operations and autonomous operations.

"You can't skip the steps," he says. “Standardised data first, then intelligence and then autonomy.”

Anas adds that this materialises as traceability across the entire value chain. He says: “We use three enabling technologies together. Blockchain gives us tamper proof records shared across suppliers. AI detects patterns and flags anomalies in real time. Machine learning predicts quality and traces material origin. Put them together and we get component level, even raw material traceability, transparent trusted data across our supplier base.”

He says this directly supports both efficiency and decarbonisation goals. “You cannot reduce a carbon footprint that you don't measure,” he says. “The result is a smarter, more resilient and more sustainable supply chain.”

Extending visibility beyond company boundaries

While data quality remains a challenge within organisations, the next frontier is extending visibility across entire supply chain ecosystems.

At Nissan Americas, Gerardo de la Torre Garcia, regional senior director of supply chain management, has been leading efforts to improve visibility deeper into the tier-N supply base, alongside the company’s supply chain management team. Beyond helping manage risk, the initiative supports compliance requirements, tariff exposure analysis and broader supply chain resilience planning.

The objective is increasingly to create connected ecosystems rather than isolated company systems. “We all can be more integrated in the way that if there is a fluctuation that is not good for you or not good for your suppliers, then in a proactive mindset we can altogether readjust,” says de la Torre Garcia. “I really value when we have calls back from suppliers saying, ‘Look, what you are doing may put us on risk, can you adjust?’ It's both sides of the coin. Keeping the flexibility to respond to the demand, but also the integration and the capabilities to react together with entire TRN chain.”

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OPmobility's Pavel says the company is exploring opportunities to connect transportation management systems and control towers with customers and logistics partners. Similarly, Glovis America is expanding vehicle tracking capabilities to provide more accurate delivery commitments to dealers and customers, recognising that ETA accuracy is becoming increasingly critical as direct-to-consumer sales models grow.

Meanwhile, IAC Group is working with logistics providers to attach ASN information directly to transportation tenders, creating end-to-end visibility of inbound material and reducing manual intervention by planners.

And of course, there is the possibility for even deeper industry collaboration through freight sharing and common transportation networks. The concept remains complex, requiring agreement on lead times, inventory assumptions and data standards, but the panellists agree that competitive advantage may increasingly come from collaboration rather than isolation.

Turning visibility into prediction

Ford's transformation journey provides a clear example of how to move from visibility to resilience through predictive intelligence.

In late 2022, the company faced severe supply chain disruption driven by shortages, unstable production schedules, premium freight costs and limited visibility beyond tier-1 suppliers, with fragmented information at the heart of the problem. Critical decisions depended on manual processes such as spreadsheets, emails, SharePoint files and disconnected systems, slowing response times and amplifying disruption.

Ford's response has been to build a unified data platform organised around a common foundation capable of supporting collaboration, simulation and eventually autonomous decision-making. The journey involved updating legacy systems, sorting master data strategies, and addressing cybersecurity risks. External factors such as geopolitical disruptions still present challenges, especially when combined with late detection and siloed human interfaces, leading to slow reactions, increased premium freight, schedule churn and high costs, but the goal for the OEM in the future is to have risk-informed scenario planning, predictive analytics, simulation capabilities and decision intelligence that help teams evaluate alternative actions before problems escalate.

Subhasish Roychoudhury, executive director of supply chain technology, describes the goal as moving from reactive management toward orchestrated and predictive operations.

“We needed to build the foundation so that we can do multiple scenario plannings,” Roychoudhury says. “Data governance is super important because without that, we cannot really get into the predictive and prescriptive analytics and monitoring our entire ecosystem. The supply chain is a connected system, so we need to make sure that the tools and solutions we have actually connect our suppliers, our logistics providers, and other trading partners, which is why the focus on collaboration and engaging our extended set of partners is super critical. I think we need to kind of continue to be mindful of that.”

Bridgestone has also evolved from basic shipment visibility toward strategic use of logistics data. Following a cyber incident in 2022, the company accelerated investment in visibility platforms that now cover approximately 90% of ocean shipments across the Americas.

The focus has expanded from tracking the location of shipments to understanding lead-time variability, port performance, inventory requirements and geopolitical risk exposure.

Data is increasingly being used not only to monitor operations but to shape sourcing strategies, inventory policies and network design decisions. 

As Allison Fowler, chief product officer at Transvoyant, notes, the most advanced organisations are contextualising and operationalising data, rather than just collecting more of it.

Resilience through traceability

Traceability is increasingly becoming a resilience tool, instead of merely a compliance requirement. Battery passports, forced labour regulations, product carbon footprint reporting and Scope 3 emissions requirements are forcing companies to understand their supply chains at unprecedented levels of detail.

Ford is developing capabilities to support battery passport requirements and improve visibility into rare-earth materials and semiconductor dependencies, while Bridgestone is working to trace rubber and raw materials deeper into its supplier network. Gestamp is combining blockchain, machine learning and AI to create trusted traceability records that extend across organisational boundaries.

Companies increasingly need to understand where materials originated, how they moved through the supply chain and what risks are embedded within those flows.

The same data supporting sustainability compliance is increasingly supporting risk management, supplier resilience and sourcing decisions.

The challenge of building trust in the automotive supply chain

Much like organisations previously needed to convince employees to move from spreadsheets to enterprise systems, they must now convince them to trust AI-generated recommendations. 

Automotive Logistics & Supply Chain Digital Strategies North America

8-9 June 2026 | Nashville, TN

That process is already underway, with Ford applying AI to supplier risk sensing and exception management, and Bridgestone piloting AI-driven transportation planning, while Nissan's data innovation teams are exploring agentic AI applications built on years of data centralisation work.

Yet most organisations still envision humans remaining in the decision loop for the foreseeable future. Near-term AI deployments are focused on recommendation, prioritisation and scenario generation rather than fully autonomous execution. The goal is to reduce operational noise and allow people to focus on the exceptions that truly require judgment.

Rather that purely being built through additional inventory, contingency plans or reactive firefighting, resilience is increasingly being designed directly into the digital architecture of the supply chain itself.