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DiManD · Project

Smart Factory Software That Lets Machines Monitor, Optimize, and Fix Production Automatically

manufacturingTestedTRL 5
dimanditn.eu

Imagine your factory machines could talk to each other, spot problems before they happen, and adjust production on their own — like a self-driving car, but for manufacturing. DiManD built the digital tools and trained the people needed to make that happen. They created software services that watch production lines in real time, crunch massive amounts of shop-floor data, and help factories run leaner without constant human babysitting. The project brought together 27 organizations across 8 countries to crack this, with nearly half being industry players who tested the results in real production settings.

By the numbers
27
consortium partners across Europe and beyond
8
countries represented in the network
13
industry partners involved in development and testing
9
SMEs in the consortium
48%
industry ratio in the consortium
30
total deliverables completed
The business problem

What needed solving

Most mid-size manufacturers know they need to digitize their production lines but face a maze of incompatible technologies, data security concerns, and a shortage of people who understand both manufacturing and IT. Factory data sits in silos, machines from different vendors don't talk to each other, and when something goes wrong, operators are still reacting instead of predicting.

The solution

What was built

DiManD delivered 30 deliverables including self-contained, combinable services for production performance analysis, monitoring, and optimization. They also built and tested a pilot that integrates distributed manufacturing techniques, cyber-physical systems, big data analytics, and autonomous adaptation into a working demonstration platform.

Audience

Who needs this

Automotive and aerospace parts manufacturers digitizing their production linesContract manufacturers needing secure data sharing across multi-site operationsMachine tool builders adding smart monitoring features to their equipmentManufacturing SMEs looking for modular Industry 4.0 solutions without full-system overhaulsIndustrial consultancies helping factories plan digital transformation
Business applications

Who can put this to work

Automotive & Discrete Manufacturing
mid-size
Target: Mid-size automotive parts manufacturers running multiple production lines

If you are a car parts manufacturer dealing with unplanned downtime and inconsistent quality across shifts — this project developed self-contained monitoring and optimization services that analyze production performance in real time. With 13 industry partners involved in testing, the tools were designed for real factory floors, not just labs. The pilot integrated techniques from distributed manufacturing and big data analytics into a working demonstration.

Electronics & Precision Manufacturing
SME
Target: Contract electronics manufacturers managing complex supply chains

If you are a contract manufacturer struggling to coordinate production across multiple sites and share data securely with clients — DiManD developed context-aware autonomous systems for distributed manufacturing. Their cyber-physical production resources let different organizations in a manufacturing chain share data with built-in security and privacy. The consortium included 9 SMEs who helped shape these tools for smaller-scale operations.

Industrial Machinery & Equipment
any
Target: Machine builders and OEMs adding smart features to their equipment

If you are a machine builder looking to offer predictive maintenance or remote monitoring as a service — DiManD created combinable services for production performance analysis and advanced diagnostics. These plug into cyber-physical systems using standardized architectures, meaning your machines can be upgraded with smart capabilities without rebuilding from scratch. The project ran across 8 countries with partners from both industry and research.

Frequently asked

Quick answers

What would it cost to implement these digital manufacturing tools?

The project does not publish pricing or licensing costs for its tools and services. Since DiManD was a training network (MSCA-ITN), the primary outputs are knowledge, trained researchers, and prototype tools rather than commercial products. Implementation costs would depend on negotiations with individual consortium partners who developed specific components.

Can these tools work at industrial scale, not just in a lab?

DiManD developed and tested a pilot that integrates the researched techniques and technologies into a working demonstration. With 13 industry partners (48% of the consortium) and 9 SMEs involved, the tools were shaped by real manufacturing needs. However, full industrial-scale deployment would likely require further customization and integration work.

Who owns the intellectual property and can I license it?

IP is distributed among the 27 consortium partners across 8 countries, following standard EU project rules. Each partner typically retains ownership of their contributions. To license specific tools or services, you would need to contact the relevant partner — the coordinator is Mondragon University in Spain.

How does this integrate with my existing factory systems?

DiManD specifically designed standardized architectures and methodologies for integrating cyber-physical production resources into existing ICT infrastructure. The services are described as 'self-contained and combinable,' meaning they can be added modularly rather than requiring a full system overhaul.

Is this ready to deploy now or still experimental?

The project closed in March 2024 with 30 completed deliverables including a tested pilot. The production monitoring and optimization services reached demonstration level. Based on available project data, the tools are past the research stage but would need adaptation for specific factory environments before full deployment.

What data standards and security does this support?

DiManD explicitly addressed security and privacy by design for data sharing between different organizations in the manufacturing chain. The project applied big data and data mining techniques with built-in protections, recognizing that manufacturers need to share production data without exposing trade secrets.

Consortium

Who built it

DiManD assembled a strong industry-facing consortium of 27 partners across 8 countries, with a 48% industry ratio — unusually high for a training network. The 13 industry partners and 9 SMEs ensured that research stayed grounded in real manufacturing problems rather than drifting into pure academia. The geographic spread (BG, ES, IE, IT, PT, SE, UK, US) covers major European manufacturing hubs plus a US connection. Mondragon University in Spain coordinated — part of the Mondragon Corporation cooperative, which itself is a major industrial group. The mix of 7 universities and 5 research organizations provided the scientific backbone, while industry partners validated results against actual production challenges.

How to reach the team

Mondragon University (Mondragon Goi Eskola Politeknikoa), Spain — part of the Mondragon cooperative group. Contact via university's engineering faculty.

Order a report on this consortiumSee MONDRAGON GOI ESKOLA POLITEKNIKOA JOSE MARIA ARIZMENDIARRIETA S COOP profile →
Next steps

Talk to the team behind this work.

Want to know which DiManD tools fit your production line? SciTransfer can map specific project outputs to your factory's digital upgrade needs and arrange a direct conversation with the right consortium partner.

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