ALSC DS Europe editor's blog: Toyota's JC Deville on using JIT to reveal problems

27 November

Enhancing supply chain resiliency, compliance and automation: Interactive workshops 

Closing out ALSC Digital Strategies Europe 2025, Neu-Ulm, University of Applied Sciences’ Prof. Dr. Tobias Engel and Gökhan Cenk, Karlsruhe University of Applied Sciences’ Dr. Monica Schmickler, and Forvia Interiors’ Zvonimir Zaja lead three workshops with the delegates on building resilience with AI, compliance and governance, and automation. 

Building resilience with AI for demand prediction

• In the workshop led by Dr. Engel and Cenk, discussions focus on advancing from descriptive analytics forecasting to predictive and prescriptive analytics. The current status of AI in supply chain forecasting is research in progress. They explain that a simulation game provides a robust option for managing real supply chains. Real people play the game, with the OEM role simulated to control orders and observe outcomes. Players are provided with various tools to improve processes and create efficiency.
• Future outlook for the bullwhip effect: Algorithms combined with AI agents will likely mitigate the effect, but well-documented business processes are essential.
• Further research ideas include: Using reinforcement learning or adding rewards and punishments to AI agents to evaluate performance, and investigating collaboration among Tier 1, Tier 2, and Tier 3 suppliers and its impact.

Compliance and governance

• This workshop, led by Schmickler, focuses on the interdependence between regulatory frameworks and technological solutions.
• Identified challenges in the current landscape include significant fragmentation in the data landscape, an abundance of IT solutions, but a lack of standardised approaches. There is a need for IT tools integrated with regulations to enable speedier and more agile processes.
• Positive impacts and opportunities arising from regulation: Insights into the impact of climate on the supply chain, increased IT resilience against cyber attacks, and regulatory requirements, while sometimes perceived as a burden, present opportunities that require closer examination.
• Red tape and slow processes persist due to inherent pitfalls.
• Transparency of partners in the ecosystem is a prerequisite for further deep dives into compliance.
• Compliance is a major driver for AI applications within OEMs, particularly for customs, parts of origin, and other requirements.

Automation

• Zaja’s discussions centre on distinguishing tasks suitable for automation versus those requiring human involvement.
• Tasks identified for automation include: Repetitive tasks, administrative tasks, system detection and analytics, and data cleaning and sorting.
• Tasks requiring human involvement include: Final decision-making power, innovation and creativity, negotiation and customer experience and personal contact.

  • Human involvement is connected to emotion, while automation handles non-emotion tasks.

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27 November

Forvia's next steps for ‘intelligent automation’ and demystifying AI

Zvonimir Zaja, head of global supply chain and logistics, Forvia Interiors discusses the company’s end-to-end supply chain framework, pillars of digital transformation, and their implemented use cases.

• End-to-end supply chain framework: Strategy defines business objectives and company direction. Core processes encompasses planning, warehouse operations, and material flow. Performance management assesses and ensures alignment with performance goals. Digital transformation technology serves as the backbone, focusing on technologies and transformation. People and organisation considered the lifeblood, essential for operational success.

• Pillars of digital transformation technology: Data focuses on data quality, availability, and structuring, utilising a data lake with Palantir. Transparency aims to enhance visibility within the 60 plants and connect suppliers and customers. Self-control advances automation to self-controlled, rule-based systems, minimising human intervention. Predictability involves the use of models, simulations, and scenarios to anticipate outcomes. 

• Implemented use cases and lighthouse plant initiatives:

  • Automation: Self-controlled AGVs are implemented in manufacturing plants to transport materials between lines. Automated injection moulding machines use AI capabilities for continuous sequencing and planning updates, replacing manual processes.
  • Transportation Management: The load builder is extended with MRP functionality to optimise truckloads for full capacity, enhancing cost efficiency and transparency.
  • Lighthouse Plant in Spain (Carazona): Produces door panels and instrument panels for a major OEM. Digitalises the injection process across 30 machines, recording parameters and transmitting data to operator supervisors via cameras and dashboards. Features auto-packaging, where parts are directly placed into shelves, and end-to-end automation with AGVs moving parts from injection moulding to assembly lines. Utilises a "Flex line" assembly for various products and variants with advanced robotics. Implements end-of-line quality control using data measurement. Tests forklift-free operations and optimises truck utilisation with load building.  Incorporates solar panels for CO₂ neutrality.
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27 November

Autoliv's end-to-end command centre for smarter decision making

Dr. Gisela Linge, vice-president of global logistics at safety systems supplier Autoliv says that smarter decision making can be achieved by addressing challenges including low visibility due to disruptions and data silos. The goal, she says, is to achieve clarity and enable end-to-end decision-making beyond SCM, integrating sales and risk management. By mapping the supply chain from supplier to customer, including information and financial flows, in a digital twin, Autoliv can tailor different dashboards for varying users, such as operations buyers, crisis management and inventory. 

The system is used for:

• Global allocations during supply shortages.
• Risk management.
• Inventory optimisation.
• Assessing the impact of tariffs, enabling sales teams to seek reimbursement and supply chain teams to focus on critical components.
• Supporting sustainability colleagues in tracking conflict minerals.
• Assisting supplier quality teams in identifying affected part numbers and customers during supplier issues.

Impact and future outlook:

• The command centre provides better visibility, replacing fragmented Power BI reports and enabling both local and global views.
• It facilitates faster decision-making by providing immediate access to data, allowing teams to focus on solving problems rather than gathering information.
• Examples of impact include the rapid assessment of tariff impacts on sales and purchasing, enabling global "inventory flea markets" to reallocate materials across plants (e.g., from China to Thailand), and improving forecast accuracy discussions with customers.

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27 November

ALSC Digital Strategies Europe Day 1 wrap up

Want to catch up on the key insights from day one of the conference? Watch Christopher Ludwig, chief content officer, Automotive Logistics' wrap up below! 

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27 November

Combining digital, data and logistics teams

Kai Albus, senior vice-president of global supply chain and logistics for transportation, Mann+Hummel and Johan Lindahl, head of digitalisation and AI, Scania Logistics, Scania are speaking about AI assistants, organisational learning, building trust in AI adoption and improving data quality in a fast fail culture.

Fast fail culture at Scania:

• Ensured by an agile way of working with shorter cycles for testing and improvement.
• Continuous improvement is embedded in the organisational DNA.
• Emphasises starting in fail-safe environments to control and assess output, as failing builds stronger AI assets.

Improving data quality at Mann+Hummel:

• Defining clear rules for data setup.
• Cleaning existing data and validating it with customers, suppliers, and internal teams.
• Integrating production machinery to provide digital data on status, speed, and work processes.
• Utilising experts and AI to analyse data, identify gaps, and implement fixes.

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27 November

How Scania is embedding a culture of digitalisation across its logistics

Johan Lindahl, head of digitalisation and AI, Scania Logistics, Scania says the truck manufacturer believes digitalisation is a shared responsibility, not solely an AI function. He says the concept of a "software-defined supply chain" is crucial for legacy companies, and that Scania aims to build agility, innovation, resilience, and sustainable competitiveness without locking into specific industry trends or technologies.

This shared responsibility approach empowers delivery streams with trust, competence, ownership and autonomy. The organisation is structured into capability teams (e.g., Material Planning team), which include all necessary resources to deliver within that stream. Through a shared digital platform, there is autonomy for capability teams through services like AI as a service, knowledge graphs as a service, advanced cloud analytics, and process mining. He emphasises that people are the most valuable asset, a lever that is accelerating with the addition of general purpose technology.

AI for leadership and research: 

• He speaks about a tool for the democratisation of knowledge and research, providing "deep research" insights. This synthesises information from academic research, industry reports (McKinsey, Gartner), and competitor case studies and acts as a "Scania consultant" with governance training. It identified a Toyota North America case study on improving end-to-end visibility for reliable delivery dates, which was later highlighted by McKinsey.
• It saves time and money by providing insights that might otherwise require external consultants, he says. This includes modules for scenario simulation (e.g., port closures) and strategy workshops. 

  • The strategy workshop module creates personas (e.g., operational, IT architect, Tesla perspective based on research) to facilitate dialogue and decision-making. This allows selection of standard workshop methods and models outcomes into board-ready presentations.

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27 November

Collaborate or fail: The essential partnerships for supply chain digitalisation

There is a need to foster willingness to rely on data over anecdotal information if digitalisation is to be successful, says Kai Albus, senior vice-president of global supply chain and logistics for transportation at Mann+Hummel.

Albus says that the company’s vision for the next two to three years is to be primarily data-focused when making decisions, providing a strong foundation for human judgement. 

Digitalisation has become a key driver in the company, shifting the company's thinking from manual to digital processes over the last 18-24 months. The firm implemented four digital hubs in North America, Europe, Singapore, and Shanghai to serve as incubators for cross-functional teams working on solutions in short sprints, with a focus on the Internet of Things (IoT) and other digital solutions, prioritising customer needs. 

In 2024, Mann+Hummel established a dedicated supply chain organisation, which was previously managed as a part of operations or sales, and is now planning to implement a data consolidation platform alongside the existing S4HANA ERP system to feed a data lake for supply chain utilisation, as well as a standardised tool for Sales and Operations Planning (S&OP). The company is also collaborating with Celonis to build solutions. 

While cash is king, he says that data is queen, and he aims for high transparency and organisational ownership of data, expecting partners to share data in return for shared data. Together, they aim to create the "supply chain of tomorrow" using process mining and continuous improvement, aligning with the company's mission to "separate the useful from the harmful" in data. 

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27 November

Supply chains fit for the future: Tools and skills to enhance visibility, resiliency and efficiency

Toyota's Deville is joined by Agnieszka Kubiak, senior vice-president of logistics at Brose Group,  Prof. Dr. Tobias Engel, professor of supply chain management at HNU - Hochschule Neu-Ulm, University of Applied Sciences, and Fearghal Kearney, senior sales director EMEA at Loftware. The panel is discussing standardisation, interoperability, and how to ensure that digitalisation has a foundation of solid data.

• Data standardisation and interoperability: Legacy systems (e.g., different SAP versions) impede data sharing between OEMs and Tier N suppliers. There is a need for interoperable, agnostic systems with standardised protocols for data exchange. Initiatives like Catena-X promote governance around data exchange standardisation in Europe.

• AI and intelligent automation: AI is often used to address problems caused by missing data. Processes must be under control before automation and AI can be effectively applied.

• Forecasting and demand Sensing: Long-term forecasting for Tier 1 suppliers is challenging due to reliance on OEM data rather than direct market view. Focus should be on demand sensing for shorter periods (e.g., 0-4 or 0-6 weeks) using analytics to understand data variations. Internal behaviours, such as sales incentives leading to end-of-month/quarter data manipulation, create artificial bullwhip effects.

• Digital supply chain twins: Digital twins can act as advanced tracking and tracing systems, allowing firms, freight forwarders, OEMs, and suppliers to cooperate. Current versions allow visualisation of material flows on a world map. Future versions (expected by mid-next year) will enable collaborative virtual war rooms using tools like Apple Vision Pro, facilitating real-time decision-making, simulations, planning, steering, and control. Successful implementation requires alignment between business, IT, and computer science teams.

 •  Internal vs. external visibility: Internal visibility and interoperability within large companies are as crucial as external supply chain visibility.

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27 November

Just In Time - WTF?

Opening day two of ALSC Digital Strategies Europe, Jean-Christophe Deville, vice-president of supply chain, Toyota Motor Europe talks about the OEM’s Just in Time (JIT) philosophy and application in combination with digital tools. 

He says that while JIT is expensive, it offers many merits such as faster Kaizen, customer satisfaction and efficiency. However, he says that one of the most valuable benefits that JIT offers is the opportunity to uncover problems lying beneath the surface. 

“JIT is a way for us to reveal problems. It’s not a toolkit, which it’s often understood to be. It’s more a mindset,” he says. 

“JIT is like high lining without a safety net. And that's what we want. We don't want a safety net in our supply chains. We want to be exposed to problems, more and more problems together,” he adds. “If you’re high lining, anytime you lose your balance, you have no choice but to rebalance and get focused, to be back on track. You cannot relax. And that's what we want, for the organisation to be under the positive stress of finding problems, to fix the problems and therefore elevate our skills level. So the spirit here is no hiding and the problem must be fixed immediately.”

For this to work, he says the OEM needs to use AI and shift its use from diagnostic into predictive and eventually prescriptive, and the must be done in partnership with the supply chain. He says that JIT with AI requires stronger partners as it reveals problems to everyone. He believes in giving the bad news first, thereby building mutual trust and long-term vision, moving from suppliers to partners. 

“If we promote JIT, we lower the level of the water to reveal the problems and face those problems together,” he adds. 

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26 November

How people will add value over AI and automation

Fatima Ezahra Ben Mellouk, logistics, supply chain and customer operations manager at Pirelli and Saba Azizi, head of service network and business development at CATL are discussing human intelligence and AI in logistics. 

They say that human intelligence is crucial for aspects AI cannot fully address, including:

• Creating real-world trade-offs: AI optimises cost, time, and efficiency, but humans balance customer relationships, brand reputation, and long-term partnerships.
• Handling disruptions: AI excels with seamless patterns, but human experience and problem-solving are essential for unexpected events like regulation changes or extreme weather.
• Building trust and alignment: The supply chain is a "family" that requires trust and alignment, which AI cannot create.

  • Humans should focus on converting data into decisions, decisions into trust, and trust into long-term partnerships.

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26 November

Digital transformation in action: Case studies of successful implementation of logistics and supply chain technology

Dr. Volker Dörrsam, senior director for digitalisation and supply chain excellence, from semiconductor producer Infineon Technologies lays out the company’s digitalisation vision and strategy for successful implementation of digital tools.

He says that Infineon's leadership team established a digital council for the automotive division, with the goal of digitalisation bridging across functions, with supply chain being a critical area due to its extensive connections.

The council aimed to create an open mindset to encourage new ideas and embrace failure as a learning opportunity, generate business value by transforming data and bringing functions together, and utilise data analysts to work on bilateral and trilateral use cases.

• Use case 1 - Master data accessibility: Master data is a substantial component crucial for advanced planning systems and visualisation. The goal was to provide the easiest possible access to master data, reducing barriers for users. A system was developed using natural language processing to allow users to "talk to the data" with predefined queries. The benefits included no need for specialised skills, as users could interact naturally in any language, a single source of truth, and 24/7 accessibility. 

• Use Case 2 - Contract analysis: Language models were utilised to extract supply chain-relevant elements from contracts using a chatbot for specific inquiries and standardised categories for consistent data extraction. This provided both interpretation and the "ground truth”, saving time and reducing mistakes, and enabling tracking contract evolution, comparing agreements with different partners. 

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26 November

Using genAI and agentic AI to save time, optimise and speed up automotive supply chains

BMW's Oliver Ganser and Markus Kronen are joined by Peter Budweiser, general manager, supply chain at Celonis and Fabian Pobantz, vice-president of operation digitalisation and IT, supply chain and purchasing at Schaeffler to discuss how to put agentic AI to use in the supply chain. They highlight that the automotive sector benefits from a strong history of ecosystem collaboration, such as Catena X and existing supplier partnerships, which can accelerate agent-enabled collaboration.

A strategic jump to the next level involves scaling agentic AI across the industry and integrating it with partners. The vision for the next two to four years includes more agents in the workforce, personalised agents supporting purchasers and logistics teams, and an ecosystem of agents that can manage end-to-end value chain processes such as supply chain due diligence and risk from tariffs and geopolitical barriers. Agents will focus on data flow rather than traditional organisational structures.

One of the biggest challenges lies in employee acceptance:

• Leaders must assure employees of their continued relevance and value, provide a stable work environment, and offer perspective on future roles.
• Bridging the natural interaction gap by using avatars and adapting cultural/linguistic elements can make agents feel more like colleagues.
• New hires expect advanced technological environments, making agent integration crucial for attracting talent.

Other challenges include:

• Data Confidentiality and hallucinations. The solutions include implementing a proper architecture with roles and rights to ensure agents only access authorised data, and utilising moderation layers tailored to specific use cases, and implementing guardrails.
• Return on Investment (ROI)concerns can be addressed through more informed decisions by agents in purchasing, bidding, and supply chain resiliency, leading to significant P&L influence. Agents can perform tasks around the clock, effectively creating "second and third shifts" to accelerate processes like bidding and product development (e.g., achieving 1-month bidding and 12-month SOP). 

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26 November

Agentic AI in action at BMW Group

General-purpose AI is insufficient for BMW Group's transformation, according to Markus Kronen, head of generative AI, procurement and supplier network at the OEM. BMW-specific AI is crucial for understanding suppliers, processes, compliance rules, and procurement and purchasing.

AIconic is the central platform for BMW's generative AI journey. It integrates approximately 15 agents currently, with the potential for 200 or more in the future. Agents possess knowledge of BMW processes, suppliers, and internal DNA, and can provide precise, BMW-specific answers for various cases, from quality inquiries to supplier volumes and deep web research. Multi-agent orchestration is a core capability, and human-in-the-loop scenarios are integrated for exceptions, such as no supplier response or an unexpected response, both of which lead to an escalation to a person.

Kronen says the current automation efforts at the carmaker are a starting point for broader transformation, with the goal to relieve employees and enable a real transformation.

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26 November

Strategic imperatives for BMW Group’s next-gen AI transformation

Oliver Ganser, vice-president of digitalisation of the purchasing and supplier network at BMW breaks down the most important strategies for utilising AI in the supply chain.

• BMW targets a leapfrog moment in processes equivalent to its multibillion-euro investment in the Neue Klasse programme, moving beyond incremental gains to generational change driven by generative AI.

• Competitive pressure is rising as new entrants like Xiaomi show far faster supply chain response times.

• Transformation rests on three pillars: an AI-orchestrated operating model; trusted interfaces that enable secure, rapid data exchange across the value chain; and leadership teams augmented by generative agents.

• BMW follows a three-step approach: current experimentation in Operating Model 1.0; mandatory integration of generative AI into all core processes through 2027 with agent-driven autonomy; and, from 2028, organisational redesign built around agent-centric workflows to optimise fixed costs and resource allocation.

• Data foundations such as Catena X are essential for shared standards, trust, and sovereignty across partners, ensuring accuracy in logistics, quality, and supply processes.

• Implementing AI-driven workflows and leadership models: AI-driven “subway line” workflows define role-specific journeys with clear human interaction points. BMW is inverting its organisational pyramid, shifting to an AI-first model where leaders set outcomes and assign work to agents before humans, reserving human judgment for validation. 

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26 November

Supply chain mapping: From compliance to strategic visualisation and risk management

Marion Gillich, head of supply chain resilience management, Daimler Truck and Dr. Monica Schmickler, senior advisor and lecturer on supply chain, sustainability and digital business models, Karlsruhe University of Applied Sciences are discussing the current state and strategic importance of supply chain mapping, its effectiveness, barriers to use, and how to build trust on data sharing.

On compliance issues, Gillich says that regulations are becoming more and more demanding, which is increasing the need for supply chain mapping.  EU battery regulations demand that supply chains be mapped, while sustainability regulations including the EUDR deforestation regulation means supply chain mapping would help to track material flow, and the UFLPA regulations in the US requires validated mapping for preventive and reactive approaches.

She says that within Daimler Truck, the group has a risk-based cluster on human rights, semiconductors and rare earth elements. "We build clusters and then based on the clusters, we’re trying to do the mapping," she says. "Building supply chains that are for ‘just in case’ is the new thing you have to do."

­On business continuity management (BCM) and anti-fragile supply chains, Gillich and Schmickler say:

• BCM is crucial for reducing negative impacts and firefighting by using mapping data for scenario planning and anticipation.

• Achieving business continuity requires building anti-fragile, flexible supply chains, moving from "just-in-time" to "just-in-case" strategies. This involves supply chain visibility, ready task forces, and other measures beyond dual sourcing.

• Daimler Truck measures supplier resilience by clustering suppliers based on operational and financial resilience against dependency, aiming to move suppliers out of "danger zones" (high dependency, low resilience).

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26 November

Building an AI roadmap: Aligning supply chain data and technology with business needs

Fabian Pobantz, vice-president of operation digitalisation and IT, supply chain and purchasing at Schaeffler, and Rafael Sanchez Aviles, manager, customer driven supply chain, SEAT, discuss the key steps in starting an AI roadmap,  build on data strategy and connectivity, and how to explore potential applications for AI in logistics.

Starting an AI journey:

• Begin with small, manageable steps, using existing tools like copilot or "bring your own data" folders as initial entry points.
• Prioritise training end-users, leveraging up-to-date training documents to onboard new hires.
• Focus on people to ensure they view AI as an opportunity and not a threat, and establish a clear data strategy with a modern data architecture platform.

Physical AI and automation:

• Humanoids are expected to become prevalent soon, with conservative estimates projecting 500,000 humanoids by 2030. The US and China are leading in humanoid development, with fewer initiatives in Europe.

Agentic AI for inbound planning:

• Handling unexpected disruptions requires agents to have robust training for common use cases, utilising historical data and a strong semantic layer. Implement a clear escalation process for high-risk situations, with humans providing final control and checks. Establish a strong feedback loop to enable agents to learn from real-world scenarios.
• Governance is critical to define boundaries for responsible AI deployment and prevent agents from acting "rogue."

Project selection and implementation:

• Take an agile approach for project selection - small organisations can decide quickly on projects. Full implementation can take a minimum of 16 months.
• Focus on clear business cases and ROI.
• Select use cases that align with people, process, technology, and data criteria, and give teams the freedom to experiment in the innovation path.

Training approaches:

• Schaeffler's approach uses AI master classes, with mandatory three-day training for leaders and team leaders, focusing on use cases and problem-solving relevant to their functions, as well as non-mandatory trainings with a strong push to prioritise AI and digitalisation as first-choice options.

• SEAT's approach includes awareness sessions which are mandatory for all administrative staff, Digital Acceleration Teams (DAT) which spread information across departments, and data stewards for more digitally interested individuals to become ambassadors for AI within the company (currently 150, aiming for 400 out of 13,000 employees in one year).

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26 November

SEAT’s roadmap to agentic AI and data-driven logistics

SEAT has been accelerating its digital path and AI roadmap, aiming for an efficient, productive, and customer-centric organisation, according to the company's manager of customer driven supply chain, Rafael Sanchez Aviles. 

The company's digital journey is progressing through distinct phases: Three years ago, the firm identified needs and initiated IT planning with a 2023-2025 strategy. it then implemented IT democratisation, enabling the use of no-code systems and applications for automated processes. Following that, SEAT adopted hyper-automation by training people to use low-code platforms to increase automatisation and productivity. And this year, the company launched AI initiatives with initial proof of concepts. 

SEAT has also been working on its HAI (human artificial intelligence) programme, which focuses on empowering people first, new technologies, data and AI governance, personal productivity and business processes.

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26 November

Charting the digital and AI roadmap for Schaeffler’s supply chain

Kicking off ALSC DS Europe 2025 we have Fabian Pobantz, vice-president of operation digitalisation and IT, supply chain and purchasing at Schaeffler, who is discussing Schaeffler's AI roadmap for the supply chain. 

He says that the pace for industrial revolutions is changing, from what was once century-long cycles to half-century cycles to decades or less. Industry 6.0 is already being discussed, indicating rapid technological advancement.

Because of this, the industry is trying to catch up as technology gaps have started to disappear, but there is still a need for a human in the loop, and commitment to training and upskilling, particularly in the logistics and supply chain sector.  

At Schaeffler, experimentation with new technologies is encouraged, but with a commitment to discontinue failing solutions. It's crucial to assess the maturity of technology for each specific use case, he says. For each case use, it's necessary to evaluate whether the people and culture are ready to invest in new technology, whether the data quality is sufficient, whether the infrastructure is available to use and scale the tools, and whether the process is stable and harmonised.

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25 November

What to expect at ALSC Digital Strategies Europe 2025!

We're back in the SV Tower, Munich for the third edition of ALSC Digital Strategies Europe, and we're gearing up to hear how supply chain and logistics leaders from OEMs, suppliers and tech partners are exploring how to map supply chains for real-time visibility, create and adapt AI roadmaps, understand ROI for digital technologies, plan and make organisational changes, and manage internal and external pressures. 

Automotive Logistics & Supply Chain Digital Strategies Europe

26-27 November | Munich, Germany

From use cases for AI technologies to how organisations and teams are changing ways of working with the support of autonomous agents, ALSC Digital Strategies Europe 2025 will explore how the automotive logistics industry can stay ahead of uncertainty, become more resilient to external risks, and build smarter, faster and more flexible operations across the Europe and beyond.

There's still time to register here!