SciTransfer
IoTwins · Project

Digital Twins That Predict Factory Breakdowns Before They Cost You Money

manufacturingPilotedTRL 7
iotwins.eu

Imagine having a virtual copy of your entire factory floor that watches every machine in real time and tells you when something is about to break — before it actually does. IoTwins built exactly that: a three-layer system where small sensors on each machine feed data to smarter computers at the plant level, which in turn connect to powerful cloud simulations. The result is a digital mirror of your production line that spots anomalies, diagnoses faults, and recommends the best actions to keep everything running at peak performance. They tested this across at least 5 different industrial test-beds covering both manufacturing and building management.

By the numbers
29
consortium partners
9
countries in the consortium
19
industry partners
66%
industry partner ratio
5+
industrial test-beds with delivered digital twins
36
total project deliverables
13
demo deliverables
The business problem

What needed solving

Manufacturing plants lose significant revenue to unplanned downtime, quality defects, and inefficient maintenance schedules — problems caused by operating blind without real-time visibility into how machines and systems actually perform. Facility managers face the same challenge: running complex buildings with outdated scheduling and reactive repairs instead of data-driven optimization.

The solution

What was built

IoTwins built a three-tier digital twin platform (IoT-level, edge-level, cloud-level) with edge-based event generation software, AI fault libraries, simulation services, and explainability tools — all tested and iterated across at least 5 industrial test-beds for both manufacturing and facility management.

Audience

Who needs this

Manufacturing plant managers dealing with unplanned downtime and quality issuesFacility management companies optimizing large building portfoliosIndustrial equipment OEMs wanting to offer predictive maintenance servicesAutomotive suppliers running complex multi-stage production linesSmart building integrators looking for proven digital twin technology
Business applications

Who can put this to work

Automotive & Mechanical Manufacturing
enterprise
Target: Mid-to-large manufacturers with complex production lines

If you are a manufacturer dealing with unplanned machine downtime and quality defects — this project developed a hierarchical digital twin system tested across multiple industrial test-beds with 29 partners that detects anomalies, diagnoses faults, and determines optimal actions to maximize your key performance metrics. The platform processes data from embedded sensors, APIs, and historical records to build accurate models of your specific assets and processes.

Commercial Real Estate & Facility Management
mid-size
Target: Property management companies and large facility operators

If you are a facility management company struggling to optimize building operations and maintenance schedules — this project built digital twins specifically for facility management environments. The system was tested in dedicated facility management test-beds with second-version iterations, combining IoT sensor data with deep learning to monitor, control, and tune building systems automatically.

Industrial Equipment & Machinery
enterprise
Target: Equipment makers offering after-sales maintenance services

If you are an industrial equipment manufacturer looking to offer predictive maintenance as a service — this project created edge-based event generation modules and AI fault libraries that can run directly at your customer's plant. Coordinated by Bonfiglioli SPA, a leading industrial gearbox maker, the platform was designed with 19 industry partners to work across diverse production environments.

Frequently asked

Quick answers

What would this cost to implement in my factory?

Pricing details are not publicly available from the project data. However, the platform was designed with a hierarchical architecture — lightweight IoT twins on sensors, edge twins at plant gateways, and cloud twins for heavy computation — which means you can start small with edge-level monitoring and scale up. Contact the coordinator through SciTransfer for implementation cost discussions.

Has this been tested at industrial scale?

Yes. The project delivered digital twins across at least 5 numbered industrial test-beds (N.8 through N.12), plus separate manufacturing and facility management test-bed groups. Each went through at least two iteration cycles, with first and second version deliveries. With 19 industry partners across 9 countries, this was tested in real production environments, not just labs.

Who owns the IP and how can I license it?

The project was an EU Innovation Action coordinated by Bonfiglioli SPA with 29 consortium partners. IP ownership typically follows EU grant rules where each partner retains rights to their contributions. Licensing arrangements would need to be negotiated with the relevant consortium members through SciTransfer.

Can this integrate with my existing factory systems?

The architecture was specifically designed for integration. It combines data from diverse sources including data APIs, historical data, embedded sensors, and Open Data sources. The edge-based modules can deploy at existing plant gateways and Multi-access Edge Computing nodes, making it compatible with standard industrial setups.

Is this ready to deploy or still experimental?

The project is closed (ended August 2022) and delivered final versions of nominal models, a fault library, and AI services for digital twins. With 36 total deliverables including tools for explainability and visualization, the technology has moved well beyond experimental. Some components may need adaptation for your specific environment.

What industries does this work for?

The project was specifically tested in manufacturing production plants and facility management environments. The coordinator, Bonfiglioli SPA, is an Italian industrial drive and gearbox manufacturer, and the consortium of 19 industry partners spans 9 countries across diverse industrial sectors.

Consortium

Who built it

This is a large, industry-heavy consortium with 29 partners across 9 European countries — and that matters for a business buyer. With 19 industry partners (66% of the consortium), this was not an academic exercise. The coordinator is Bonfiglioli SPA, a major Italian industrial drive manufacturer, which means the technology was shaped by a company that actually builds factory equipment. Only 2 partners are SMEs and 3 are universities, confirming this was an implementation-focused effort. The geographic spread across Austria, Belgium, Germany, Denmark, Spain, France, Italy, Luxembourg, and the UK suggests the platform was tested across different industrial contexts and regulatory environments.

How to reach the team

Bonfiglioli SPA (Italy) — contact through SciTransfer for warm introduction

Order a report on this consortiumSee BONFIGLIOLI SPA profile →
Next steps

Talk to the team behind this work.

Want to explore how IoTwins digital twin technology could reduce downtime in your factory? SciTransfer can connect you directly with the right consortium partner for your specific use case.

Order a report on this topicMore in Manufacturing & Industry 4.0
More in Manufacturing & Industry 4.0
See all Manufacturing & Industry 4.0 projects →
Prediction Software That Tells You How Polymers Behave Before You Make Them
PolySolv
Open →
Unbreakable Pivot Pins That Eliminate Heavy Machinery Downtime and Joint Failures
BTPin
Open →
First Large-Scale Plant Turning Wood Pulp Into High-Performance Bio-Based Material
EXILVA
Open →