Project Results

From Research
to Impact 

Throughout its lifetime, DaCapo identified and validated a set of Key Exploitable Results (KERs) that capture its main scientific, technological, and innovation outcomes. These KERs are grouped below into Research KERs and Commercial KERs according to their primary exploitation pathway, as defined in the project's final exploitation plan.

Research KERs

Research KERs comprise conceptual, methodological, and enabling results validated in the DaCapo pilots. They establish a scientific and technical foundation for future research and innovation activity, to be taken forward through follow-up Horizon Europe projects, academic-industrial collaborations, and open dissemination.

Commercial KERs

Commercial KERs are results with a direct path to market: they are being integrated into partners' existing product and service portfolios, or are pursuing SaaS, licensing, or turnkey commercialisation routes beyond the project's lifetime.

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Research Result TRL 5-6

KER1 – Interoperable Data Management Infrastructure for Product Traceability

A modular, interoperable data infrastructure enabling end-to-end product traceability in circular manufacturing, built around a Minimum Viable Product (MVP) of a Digital Product Passport (DPP). It combines a web-based traceability application, a DPP reference architecture with visualisation tools, and a middleware API layer that harmonises heterogeneous manufacturing data while preserving data sovereignty.
Benefits: Role-based access to product data; alignment with the Asset Administration Shell (AAS), RAMI 4.0 and the EU Data Spaces initiative; a reusable reference architecture rather than a one-off tool, cutting integration effort for future circular-manufacturing projects.
Areas of Application: Automotive, electronics, aerospace and consumer-goods manufacturers needing transparent lifecycle tracking and sustainability reporting under the Ecodesign for Sustainable Products Regulation (ESPR); researchers and technology providers building Digital Product Passport and industrial data-space solutions.

AIMEN (Lead) – Leads the overall development and integration of the interoperable data infrastructure.

VTT – Key research partner contributing to data spaces alignment and system architecture.

ENG – Technology integration and middleware component contributor.

Validated as an MVP (TRL 5-6) across DaCapo's aeronautics, electronics and logistics pilots. AIMEN will maintain it as a reference implementation for future EU/national R&D projects; further development is driven by follow-up funded initiatives rather than direct commercialisation.

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Research Result Living Framework

KER3 – Agile Methodology for Circular SMEs

A structured, adaptable methodology helping manufacturing SMEs adopt circular-economy practices by addressing organisational and human barriers, change management, leadership alignment and workforce engagement, alongside the technology itself. It also helps SMEs understand the opportunities and risks of AI/LLM-supported sustainability tools.
Benefits: Practical, cost-effective circularity practices tailored to SME resource constraints; an openly accessible, easy-to-share knowledge resource; already tested with 150+ SMEs, so it is dissemination-ready rather than a lab prototype.
Areas of Application: Manufacturing SMEs across sectors and countries; sustainability consultancies; industry clusters and non-profit support organisations (e.g. Circonnect, Circo); policymakers promoting industrial sustainability.

TNO – Developed the structured methodology and engaged with over 150+ SMEs for real-world validation and dissemination.

Already validated through direct engagement with more than 150 SMEs. Designed as a living framework, to be refined iteratively with industrial feedback and extended to new sectors, a formal TRL staging does not apply to this methodological result.

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Research Result TRL 5-6

KER5 – Eco-case Architect

An AI-assisted generative design tool that lets designers create sustainable product enclosures while seeing real-time eco-efficiency indicators (material use, energy, print/manufacturing time and complexity). Available as a Blender plug-in for designers and as a headless REST service for integration into other platforms. First demonstrated on the Fairphone smartphone casing, but geometry-agnostic and adaptable to other small consumer products.
Benefits: Brings sustainability assessment into the earliest, most impactful stage of product design; connects free-text aesthetic customisation directly to quantitative environmental indicators; interoperable with DaCapo's CE-DSS and Digital Product Passport infrastructure via AAS submodels.
Areas of Application: Industrial design and product-engineering teams, especially in consumer electronics; SMEs producing polymer-based enclosures; 3D-printing and maker communities; researchers in eco-design and Digital Product Passports.

AIMEN – Lead developer of the generative design tool algorithm, Blender plugin, and REST interface.

Robust integrated prototype at TRL 5-6 by project end, calibrated for Fairphone-relevant materials. Next steps: validate KPIs against real lifecycle-assessment data, harden for operational use, and build a mature web interface, positioned as a reusable R&D asset for future EU projects rather than an immediate commercial product.

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Research Result TRL 4-5

KER6 – Eco-storage Architect

An AI-driven tool that generates and compares alternative 2D layouts for industrial storage facilities, letting engineers weigh operational efficiency against environmental performance from the earliest planning stage. Designed for future integration with 3D visualisation and simulation environments, including a collaboration with PESMEL.
Benefits: Massively expands the number of design alternatives planners can explore compared to manual layout planning; embeds sustainability indicators, not just space and throughput, into facility-design decisions.
Areas of Application: Warehouse and storage-facility designers, logistics engineers, manufacturers running automated storage/retrieval systems, and engineering consultancies delivering layout solutions.

AIMEN – Developed the generative AI 2D layout planning algorithms and sustainability decision engine.

Validated prototype at TRL 4-5. A current dependency on proprietary visualisation software is the main blocker to commercial deployment; next steps are migrating to open/licensable 3D visualisation, extending to full 3D generation, and integrating with Digital Twin environments, positioned as a first-generation research and experimentation platform.

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Research Result TRL 5-6

KER7 – Energy Estimation DT

A Digital Twin that uses data-driven modelling to simulate machining operations and estimate their energy consumption under different process configurations, aimed at repair and remanufacturing activities. Demonstrated in DaCapo's aeronautics use case (GKN) for CNC repair of fan blades.
Benefits: Lets manufacturers quantify the energy impact of different machining/repair strategies before executing them, supporting more sustainable repair decisions and extending product lifetime while minimising resource use.
Areas of Application: Aerospace repair and maintenance organisations, remanufacturing companies, and machining-intensive manufacturers with sufficient production-monitoring and sensor infrastructure to supply operational data.

LMS – Developed the energy simulation digital twin algorithms and predictive machining models.

Validated at TRL 5-6 in the aeronautics use case. As a public university, LMS does not plan immediate commercialisation; next steps are validation in additional repair/remanufacturing environments and broader industrial datasets, pursued through future EU research projects, with SaaS/licensing routes to be considered later via LMS's technology-transfer office.

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Research Result TRL 6-7

KER12 – DisAssembler Tool

A software tool that automatically generates the most efficient disassembly sequence for a product, guiding repair technicians (or end users) on which component to remove next and which tool to use. Built and demonstrated on the Fairphone 4, using a dynamic-programming algorithm that also minimises device-orientation changes during repair.
Benefits: Turns repair guidance into an interactive, optimised, step-by-step process instead of static manuals or videos; the optimisation engine is product-agnostic and can be reused for other repairable products by updating the underlying disassembly data.
Areas of Application: Repair operators in authorised or third-party repair centres; product manufacturers pursuing repairability/circular-economy strategies, starting with Fairphone's professional repair network and customer-facing self-repair site, with potential extension to other consumer electronics and repairable products.

POLIMI – Developed the disassembly optimization algorithm and dynamic programming framework.

Mature prototype at TRL 6-7. As a university, POLIMI will retain ownership of the core algorithm and license usage rights (e.g. to Fairphone) rather than sell the software outright. Short-term (0-12 months, ~3-4 person-months): adapt to more Fairphone models and package for web/repair-centre deployment; mid/long-term: extend to other product families and reuse in future repairability research.

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Commercial Result TRL 7

KER2 – WEAVR AI Circularity Assistant

An AI + Extended Reality (XR) operational assistant, built as an extension of TXT/PACE's existing WEAVR platform, that guides operators through inspection and maintenance procedures while showing the economic, energy and environmental impact of repair/circularity decisions in real time.
Benefits: Combines XR-based training with an AI assistant and a unified interface, so workforce upskilling and circularity awareness happen inside daily operations rather than as a separate exercise; leverages TXT/PACE's existing customer base and infrastructure to shorten time-to-market.
Areas of Application: Manufacturing companies with complex maintenance/inspection workflows, Maintenance-Repair-Overhaul (MRO) organisations, and enterprises facing growing sustainability-reporting and circularity-compliance requirements.

TXT – Commercial provider of the core enterprise platform.

PACE – Co-developer of the WEAVR platform and AI/XR integration modules.

Already at TRL 7, building on the commercially available WEAVR platform. Short-term (0-12 months): productisation, sales training and lead generation (~€66,000); mid-term: continued R&D and customer deployment (~€20,000); long-term: scaled commercial operation.

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Commercial Result TRL 6

KER4 – GRETA DSS

An AI-powered Decision Support System that turns product/process data (bills of materials, manufacturing steps, material choices) into actionable Life Cycle Sustainability Assessment (LCSA) insights, covering EF 3.1 indicators such as CO2-equivalent emissions, recycled content, separability and expected lifetime, without requiring LCA expertise from the user.
Benefits: Makes advanced sustainability assessment usable by non-experts; designed to plug into PLM/CAD systems and complementary DaCapo tools (WEAVR, Warehouse DT); differentiates from established LCA software (SimaPro, OpenLCA, Sphera) through an AI-assisted, operational, integration-first approach.
Areas of Application: Product designers and engineering teams, sustainability/compliance managers, manufacturing SMEs and mid-caps in regulated sectors, PLM/CAD/ERP vendors, and sustainability consultancies.

SUPSI – Developed the GRETA Decision Support System engine and LCSA AI models.

LCSA module near TRL 6, AI advisory features at TRL 4-5. Short-term (0-12 months, ~€100k-200k): consolidate the LCSA module and early-adopter deployments; mid-term (~€200k-400k): scale and integrate with PLM/ERP; long-term (~€300k-400k): full commercial deployment, potentially via a dedicated spin-off or licensing structure.

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Commercial Result TRL 8-9

KER8 – Warehouse Digital Twin (Warehouse DT)

An AI-enhanced Digital Twin of PESMEL's Warehouse Management System (WMS), sharing the same codebase as the live production system, that supports layout optimisation, virtual commissioning before physical investment, and operational forecasting, now extended with circular-economy KPIs (modularity, reusability, material efficiency).
Benefits: Native integration with the operational WMS avoids the inconsistencies typical of stand-alone simulation tools; reduces commissioning risk and downtime; adds sustainability metrics to an already-proven industrial product.
Areas of Application: Industrial operators of automated warehouses and logistics systems, particularly in paper, steel and tyre manufacturing; warehouse and automation integrators.

PESMEL – Lead commercial partner driving industrial rollout and live WMS integration.

Expected to reach TRL 8-9 by project end, one of DaCapo's most mature results. First live customer deployment acts as the reference case; short-term (0-12 months, ~€50,000) focuses on production validation and go-to-market; longer term on replication across additional customer sites.

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Commercial Result TRL 8-9

KER9 – SmartHub

A plug-and-play industrial data platform that centralises and synchronises real-time data from robots, PLCs, cameras and sensors on a dedicated industrial PC, using customisable Python plugins so new devices can be connected without touching the protected software core.
Benefits: Combines an industrial-grade, protected core with an open plugin layer, robust but not locked to one vendor; produces standardised, traceable data streams ready to feed AI and Digital Twin applications.
Areas of Application: Manufacturing SMEs and large enterprises pursuing digitalisation and Digital Twins; system integrators and OEMs; discrete manufacturing, logistics, robotics and process industries.

AIMEN – Technology provider behind the plug-and-play SmartHub industrial edge platform.

Already operational in industrial settings, entering the post-project phase at an advanced commercial maturity. Short-term (0-12 months, ~€32,000): IP/licence due diligence, certification and go-to-market planning; longer term: scale deployments and strengthen integrator partnerships.

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Commercial Result TRL 6

KER11 – Hurry Factor

An AI/ML-based predictive API that continuously analyses warehouse operational data to forecast workload peaks, congestion and bottlenecks in real time, the "Hurry Factor", helping schedule warehouse activities more intelligently than static, rule-based tools.
Benefits: Lets WMS providers add predictive AI without building it in-house; improves labour planning and reduces congestion and idle time for warehouse operators.
Areas of Application: Warehouse Management System providers and, through them, warehouse operators in retail distribution, e-commerce fulfilment, third-party logistics and manufacturing logistics.

AIMEN – Core developer of the predictive AI/ML algorithms.

PESMEL – Commercial implementation and live warehouse deployment partner.

Approaching TRL 6, validated live with a PESMEL customer. Next steps: extended proof-of-concept in more warehouses, integration into production WMS platforms, and productisation into a scalable module, licensing model (exclusive/non-exclusive, royalties vs. upfront) still to be agreed with PESMEL.

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Commercial Result TRL 8-9

KER10 – CE DSS

A modular, AI-enabled Decision Support System that combines real-time operational data, machine learning models and sustainability indicators to generate operational recommendations. Demonstrated across three DaCapo pilots: battery-lifespan management for Fairphone, defect detection and repair guidance for GKN Aerospace, and warehouse orchestration for PESMEL.
Benefits: A single configurable platform rather than a one-off tool, the same knowledge and reasoning layer supports very different use cases (battery health, defect detection, warehouse orchestration) by swapping modules; integrates with existing systems instead of replacing them.
Areas of Application: Manufacturing, aerospace, consumer electronics, logistics and warehousing companies, plus remanufacturing and maintenance service providers; already extended beyond DaCapo into the REUMAN project, with initial licensing discussions underway with Fairphone.

CORE Innovation Centre (CORE) – Lead provider and developer of the AI CE DSS system.

TRL 8, expected to reach TRL 9 through final end-user validation before project close. Short-term (0-3 weeks, ~€10,000): feasibility and requirements analysis; medium-term (~6 months, ~€100,000): AI model retraining, productisation, deployment infrastructure; long-term: new modules and expansion to further sectors.