Agenda

Ton Aerdts, Research Director, GBR

• Pillars of Digital Manufacturing – Infrastructure, Data and Applications
• Secure industrial communications and connectivity
• Invest in fit for purpose technologies
• Prepare for the future by embedding advanced and emerging technologies

Sohard Aggarwal, Head of Digital Manufacturing, Reckitt

• The business model that has developed since the mid 1970’s is not fit for purpose
• 70% of transformation programs fail
• The reason is due to a systemic problem with the current global model
• Without change, future programs will also fail
• The solution requires leadership from the C-Suite
• The solution will bring about a global paradigm shift
• The UK manufacturing sector is already changing

John Robinson, Founder and Creator, The Quorum Principle Limited

• Bridging the Physical-Digital Gap & Centralizing Operational Data: transform siloed, raw data into structured entities and intelligent queries
• Real-Time Visibility: provide plant managers with immediate visibility into global operations
• Driving Measurable Outcomes: enhance efficiency through the precise tracking of material usage, order status, and plant-level waste reduction
• Scalable Transformation: Establish a strategic roadmap for industrial enterprises to accelerate digital maturity and operational excellence

Jakob Hall, Global Digital Transformation Director, Velotic

• Integration readiness and data spaces: The importance of data infrastructure
• Applying the right concepts to facilitate data integration
• Are data formats the true integration drivers / limits?
• AI or DIE? The role of data structures in AI projects and the role of AI in data structures

Dr. Martin Beisiegel, BD/PM, Steinhaus Informationssysteme GmbH

Most AI in manufacturing fails due to fragmented operational data across PLCs, MES, historians, and cloud systems, lacking shared data models and validation.

• This session details a practical data maturity path for manufacturers to move from isolated pilots to production-scale AI and agentic operations
• Establish enterprise-wide real-time data accessibility using a Unified Namespace
• Build an AI-ready data layer that contextualises and governs raw telemetry
• Define governance and control frameworks for safe AI and industrial agent deployment

Shashank Sharma, Director of Product Marketing, HiveMQ

• AI-powered manufacturing software built for the shop floor accelerates decisions, autonomy, action, implementation — and ultimately productivity.
• Connected Workforce platforms are uniquely positioned to operationalize agentic AI where it matters most: at the line, in the hands of the people doing the work.
• Redzone is live in 2,000 sites globally, with 550,000 frontline workers using it every day to take action, communicate problems, visualize line performance, run tiered meetings, complete quality/safety checks and TPM, train on the job, and surface root causes.
• Measurable results in 90 days from go-live — and fully implemented in just 8–10 weeks.

John Parsonage , Solutions Director, EMEA , QAD Redzone

• How a structured operational excellence approach builds leadership capability and employee engagement
• Practical strategies for selecting and implementing digital systems to become a smart factory

Salvatore Impellizzeri, Corporate Manufacturing & Capability Director, Procter & Gamble

• Transforming maintenance history and MES data into instant, AI-driven support for frontline teams
• Unlocking the power of proven solutions—making hidden know-how instantly accessible and actionable across shifts
• Building a cloud-based backbone for scaling troubleshooting intelligence seamlessly across global operations
• Standardizing tools and data sources to create a unified layer of operational intelligence for every plant
• Bringing AI straight to the shop floor—turning complexity into clarity and speeding up every decision

Jan Kolarcik, Head of Industrial Engineering & I4.0, Aumovio SE

• AI in industrial robotics: How AI drives efficiency and flexibility (multi-robot planning, autonomous welding, model-free bin packing)
• Humanoid robots: needs vs. reality: Industry expectations, capability gaps, and the dexterity challenge
• AI in Production: Use cases beyond Chat GPT in real production environments

Roman Ungern-Sternberg, Head of BU Operations, Fraunhofer Institute for Manufacturing Engineering and Automation (IPA)

• Most supply chain networks have evolved over time, through circumstances and acquisitions, rather than by intentional design. Consequently, inefficiency is baked into the structures of many complex networks
• Traditional approaches to network design model transport, facility and process costs separately. This doesn’t reflect the interconnectedness of these dimensions in real-world supply chains, meaning most optimisation results are unrealistic and many opportunities for transformation and cost reduction are missed
• A smarter approach to network design and supply chain transformation is needed. By taking a multidimensional approach to optimisation, and optimising economies of scale in production and facility costs alongside transportation, leaders can evaluate opportunities for harmonisation, and understand tradeoffs and strategic tipping points between priority metrics

Beth Walker, Head of Sales and Marketing, SimPath

• Holcim’s intro and strategy
• Plants of tomorrow program
• Examples
• Challenges and success factors

Marjan Milosevic, Head of Smart Operations, Holcim

• A high-volume beverage manufacturer was facing capacity constraints and reliance on outsourced production because planning could not fully reflect what could be blended, stored, delivered, and executed.
• The solution transformed production planning from a static schedule into a live capacity-management capability, helping the business adapt around changing constraints without new CapEx.
• When constraints changed, the system re-optimized around current plant state while protecting work already in progress.
• The impact: more capacity from existing assets, faster disruption response, and less reliance on outsourced production.

David Coxon, Solutions Director, Cogna

Ton Aerdts, Research Director, GBR

• Industrial AI is not a model problem—it is a context problem. Moving from disconnected data to trusted, reusable context is what enables AI to deliver value beyond isolated pilots.
• Scaling AI is an iterative journey, not a big-bang transformation. Manufacturers should start with high-value use cases, establish trusted foundations, and progressively evolve from visibility to intelligence and autonomy.
• Technology alone is insufficient. Sustainable Industrial AI requires organizational readiness, governance, and cross-functional ownership to create trust and enable AI to scale safely.

Yasir Tuncer, Founder & CEO, MaestroHub

• Why classic Lean reaches its limits in high variant, volatile manufacturing environments
• What the New Lean study reveals about doubling productivity – and what it does not
• End to End Operations as the missing link between strategy, shopfloor, and IT
• How core components, flexible factories, and AI must be designed together
• Typical pitfalls of digital and AI initiatives in variant manufacturing – and how to avoid them

Roman Ungern-Sternberg, Head of BU Operations, Fraunhofer Institute for Manufacturing Engineering and Automation (IPA)

• A roadmap for agile production, from foundational data to autonomous operations.
• No-code ML: frontline teams deploying in days, not years.
• A three-step framework: prioritize, integrate, operationalize.
• Documented customer value: USD 1M to 5M in annual savings.

Remi Goget, SVP Technical Sales & Customer Success, Intelecy

This talk shares practical examples of how manufacturers de‑risk expansion, automation and supply‑chain decisions

• Manufacturing leaders are making multi‑million‑pound decisions using static models in highly dynamic systems
• Variability, interaction effects and uncertainty are the real reasons plans don’t survive contact with reality
• Simulation and digital twins allow leaders to explore “what happens if?” before committing time, capital or reputation
• Leading organisations now use digital twins as decision-support systems, not visual dashboards
• When combined with optimisation, these models don’t just explain performance – they identify better choices

Duncan Brown, Senior Business Development Manager, Haskoning

Most industrial AI projects stall on adoption, not technology. Pilots rarely deliver measurable impact, because operators do not act on output they cannot reproduce, trace, or explain. This session covers the architectural and organizational choices that move industrial AI to trusted, scaled production and competitive advantage.

• How combining deterministic analytics with generative AI earns trust through reproducible, traceable and explainable output instead of black-box answers
• Enable the Shopfloor for natural-language root cause analysis, monitoring and reporting by connecting Agentic AI to your Data Foundation, MES and ERP through TrendMiner
• Understand how to avoid hidden costs

Aline Hüser, Solutions Engineer, TrendMiner

• The shift from cost-focused sourcing to resilient, value-driven supplier ecosystems in smart manufacturing.
• The evolving role of SCM leaders: balancing cost efficiency, sustainability, agility, and supply security.
• Building supply chain resilience through strategic partnerships, risk-sharing with suppliers.
• Best practices for developing collaborative supplier relationships that enable faster decision-making and operational excellence.

Tülay Önder, Vice President SCM, Meyle

• Why most industrial AI fails: data gaps, ownership issues, and lack of integration into real operations
• Designing AI around decisions: linking forecasting, optimization, and pricing directly to business impact
• Hybrid intelligence in practice: combining domain knowledge with machine learning for robust industrial systems
• Scaling from POC to production: reliability, explainability, and monitoring as core design principles
• Building high-performing data science teams: embedding DS in value chains with end-to-end ownership
• Lessons from energy systems at Eneco and how they apply to smart manufacturing

Kaustav Basu, Data Science Manager, Eneco

• Current Digital Challenges and Opportunities
  Group to share their current digital challenges and the opportunities they see in the digital space, such as impact of digital transformation on their respective areas, any obstacles they are encountering and the potential for digital technologies to drive innovation/improvements within the organisation
• Best Practices and Lessons Learnt
  Group to discuss best practices and lessons learnt from their already delivered digital initiatives. Sharing success stories as well as what didn’t work and the valuable lessons gained from those experiences
• Future Trends and Innovation
  Forward looking discussion about current trends and technologies in digital technologies, such as impact of AI, Machine Learning, Blockchain, or other cutting-edge technologies on their respective areas, how they envision these technologies are shaping the future of industry