Tell us about yourself, your company and your experience in the industry?
I’m David Hurtado, Principal Value Engineer at Celonis.
Celonis is a process intelligence company – we work with over 1,400 global enterprises, including 300+ leading manufacturers, to help them see exactly how their operations actually run and where the value is hiding in the gaps.
On the industry side, I’ve spent the last 16+ years in supply chain and operations. Before Celonis, I was at PwC for six years leading large-scale supply chain transformations for Fortune 500 clients, and before that I was in finance and analytics at Citigroup. So my role today is really about bringing that operator’s lens to how manufacturers use process intelligence to close the gap between how they think their operations run and how they actually run.
How is your organisation responding to digital transformation in the industry?
Celonis is powering the next generation of business performance by helping manufacturers industrialize Enterprise AI. Rather than attempting costly “rip and replace” core system overhauls, we provide a real time, system-agnostic digital twin of enterprise operations, what we call the Celonis Context Model.
This living model connects data across fragmented ERPs, MES, PLMs, and supply chain applications, enriching it with operational context. It acts as the foundational layer that gives both human teams and emerging Agentic AI solutions the exact operational understanding needed to analyze, redesign, and autonomously operate processes.
What outcomes do you hope to achieve in your participation at Digital Manufacturing Strategies Summit this year?
Our goal at the summit is to connect with industry leaders to demonstrate how Process Intelligence bridges the gap between digital vision and measurable P&L impact. We hope to share best practices on how manufacturing executives can:
- Safeguard profitability amid ongoing cost pressures.
- Build resilient, agile supply chains capable of responding to disruptions in real time.
- Successfully scale AI initiatives beyond siloed experimentation.
What excites you about the topics to be explored at the summit?
I am particularly excited about discussions surrounding the practical integration of Agentic AI on the shopfloor and in supply chain planning.
There is immense enthusiasm in the industry around AI, but scaling it requires a shared, objective understanding of how work actually gets done. Exploring how peers are pairing AI with operational context to solve talent shortages and streamline complex processes is a hugely promising topic.
What do you see as the biggest challenges affecting the industry over the next 12-months?
The major headwinds facing manufacturing leaders are escalating material and energy costs, persistent supply chain volatility, and talent shortages. Additionally, there is a growing “AI value gap” where organizations struggle to move AI initiatives beyond siloed experimentation
due to legacy system constraints and lack of real-time operational visibility.
What surprises you most about digital transformation in the industry?
What surprises me most is the persistent gap between how enterprise
processes are designed to run versus how they actually run. Despite massive investments in enterprise software (ERPs, WMS, PLMs), individual departments still operate in functional silos. Procurement might buy in bulk to lower unit costs, while inventory management is pressured to cut stock levels. Without a unified operational digital twin, systems don’t communicate effectively, leaving millions in value trapped across the organization.
How do you see digitalisation and automation improving the sector?
When grounded in process intelligence, digitalisation shifts manufacturing from reactive firefighting to proactive, autonomous execution. It enables automated master data fixes, dynamic order re-promising, and real-time lead-time updates across supply networks, driving productivity without forcing costly IT overhauls.
Can you think of examples within your own projects of how automation /
digitalisation has improved the sector?
Several proven customer outcomes demonstrate this impact:
- Bosch Mobility: Leveraged Celonis Process Intelligence to unlock over €100 million in value, driving operational efficiency and strategic decision-making.
- Smurfit Westrock: Created a single source of truth across 300 plants, preventing teams from buying spare parts already in excess stock and realizing millions of euros in value.
- Stada: Corrected 300+ inaccurate planning lead times, driving recurring six-figure quarterly savings and preventing stockouts.
- Manroland Goss: Used the Celonis Optimization Engine to automate delivery date assignments, shipping orders 3 days faster and achieving a 25% reduction in FTE hiring needs through productivity gains.
Biggest opportunities for manufacturing the coming years?
The single largest opportunity is closing the AI Value Gap by establishing an operational foundation for Enterprise AI. By establishing a real-time digital twin across the end-to-end value chain, manufacturers can deploy composable AI agents to autonomously handle routine tasks—like supplier inquiries, inventory balancing, and master data cleanup—allowing human
talent to focus on innovation and strategic growth.
What are the biggest challenges relating to planning, designing and
implementing your digital and automation strategies?
The core challenges stem from system fragmentation and process complexity:
- Data is scattered across multiple legacy ERP instances, PLM tools, and custom spreadsheets.
- Teams lack a common “vocabulary” or single source of truth across engineering, procurement, and shopfloor operations.
- Organizations often attempt point-solution automations without understanding underlying root causes, which simply speeds up bad processes rather than fixing them.
What did you learn from project experiences that might help industry
colleagues going forward?
The most critical takeaway is to modernize on top of existing infrastructure rather than waiting for multi-year system replacements. Establishing an objective single source of truth across operations builds cross-functional alignment and allows teams to target high-impact bottlenecks—like inaccurate lead times—for immediate P&L returns.
What advice would you give someone who is just entering the industry
and what, would you say, are your biggest career learnings?
My advice to anyone entering manufacturing is to focus on end-to-end processes rather than functional silos, understanding how data and decisions flow continuously from engineering to shopfloor execution. My key career learning is that operational reality rarely matches how leadership assumes a process runs, and the biggest value opportunities live at departmental handoffs.
Why should industry colleagues come to the summit in 2026?
DMSS USA brings together decision-makers with real operational authority in a working forum designed to tackle deployment hurdles head-on. It moves the conversation beyond vendor pitch decks into practical strategies for scaling digital execution, building supply chain resilience,
and turning AI investments into measurable P&L performance.
What is your top tip when attending summits and conferences?
Prioritize interactive think tanks and 1-on-1 peer discussions over passive presentation viewing. Focus your conversations on asking fellow leaders how they drove cross-departmental adoption, navigated shopfloor change, and measured tangible business outcomes.
