challenges 2026

Brief Challenge Description

We are IQF producers of Frozen vegetables & fruits. We have 4 IQF Freezing tunnels with different capacities and different operating parameters per product. Production scheduling and optimization takes place with more traditional methods like excel files with limited options in optimization, "what if" scenarios and "bottleneck" identification in time. Taking into consideration the limited time of operation per crop we are looking for an automized solution for Frozen Food Production Optimization

Expected Results from the Solution

Reduction of production planning time, bottleneck identification, what if scenarios and production forecasting in connection with sales demand and production days utilization during the campaign.

Brief Challenge Description

Aluminium window manufacturing remains a complex, multi-stage process heavily dependent on skilled manual labor, which is increasingly scarce. Small and medium-sized manufacturers struggle to meet rising demand for faster, high-quality production, while existing automation solutions are too costly and inaccessible. This creates a critical need for affordable, practical innovations that enhance productivity and consistency for everyday workshop teams.

Expected Results from the Solution

Faster and more consistent production, reduced dependence on highly skilled labor, improved product quality, and fewer errors. The solution should enhance daily workflows, increase efficiency, and enable small and medium manufacturers to scale operations competitively through affordable, easy-to-use tools.

Brief Challenge Description

Flood protection solutions today fail to address large-scale openings (1–6 m) in facilities such as hospitals, logistics hubs, and underground parking. Existing systems are costly or impractical when scaled, leaving critical infrastructure and non-standard residential openings vulnerable. This gap matters for property owners, operators, and communities facing increasing flood risks due to climate change.

Expected Results from the Solution

The solution should provide scalable flood protection (1–6 m width, up to 2 m water), withstand hydrostatic and dynamic loads, ensure low leakage, and be durable in harsh conditions. It must be cost-effective (≤€150/m²), easy to install without expertise, adaptable to various structures, and allow fast manual or automated deployment. (Aluminium is preferred but not required.)

Brief Challenge Description

As agrivoltaic (agri-PV) installations expand in rural and remote areas, a major industrial and technical challenge emerges: how to connect and operate large-scale agri-PV systems within weak or underdeveloped electricity distribution grids.

Expected Results from the Solution

Secure and stable operation of agri-PVs in rural and off-grid contexts, Reduced grid congestion and voltage instability Higher utilization of locally produced renewable energy, Scalable, cost-effective deployment models for large agri-PV farms

Brief Challenge Description

BRITE faces a significant industrial challenge in optimizing the transparency of photovoltaic (PV) modules used in agrivoltaic systems. Currently, transparency levels are selected empirically, relying on manual trial-and-error methods that balance light transmission for crops with energy yield. This approach is time-consuming, non-standardized, and may lead to suboptimal performance.

Expected Results from the Solution

Develop a systematic, data-driven digital tool that automatically calculates the optimal PV transparency based on key parameters such as climate zone, crop type, and local solar conditions.

Brief Challenge Description

Galinos.gr owns a battle-proven decision support tool for drug-drug and drug-disease cross-check. Its operation is based on documented and curated medical rules with reference to the official documents from competent authorities. Currently, the creation of such rules for Adverse Drug Reactions (ADRs) is manual and time-consuming since they are usually listed in PDF tables. An automated tool would reduce the entry time while enhancing data review time and, thus, quality of rules.

Expected Results from the Solution

A tool to automate table data extraction will have a positive impact both to the company’s operations and at the user level. It will allow us to input large amounts of data in a short time, increasing completeness of our knowledge base while freeing up time for data review and further development of new features. This will ensure quality improvement and good user exprerience.

Brief Challenge Description

Current amino acid solutions are valuable biostimulant with strong agronomic potential, yet their overall quality and market versatility can be further enhanced through the removal of residual ions and inorganic load. Achieving a cleaner and more refined composition would improve product performance, formulation flexibility, storage stability, and suitability for demanding agricultural and international markets, benefiting producers, distributors and end users.

Expected Results from the Solution

Expected outcomes include the production of a cleaner, more refined and higher-value amino acid solution with reduced residual ions, enhanced physical and chemical stability, and improved consistency in downstream formulations. The solution is expected to increase commercial value, widen application possibilities, support access to demanding global markets and enable the development of advanced pr

Brief Challenge Description

Software teams generate vast data across Git, Jira, and communication tools, yet lack unified intelligence to detect bottlenecks, workload imbalances, and process drift in real time. Decisions rely on intuition rather than evidence, leading to delayed deliveries, burnout, and compounding inefficiencies as organizations scale. Engineering leaders need a proactive, AI-driven approach to optimize how work actually flows from ideation to deployment.

Expected Results from the Solution

The solution will reduce cycle times by identifying and eliminating workflow bottlenecks, enable balanced task distribution to prevent burnout, and provide predictive insights for smarter sprint planning. Teams gain real-time visibility into process inefficiencies, while AI-driven recommendations replace guesswork with evidence-based decisions - resulting in faster delivery and higher quality

Brief Challenge Description

ISOMAT, a building-chemicals manufacturer with a portfolio of more than 3,500 SKUs, operates across diverse packaging formats and multiple international markets with varying language requirements. Limited label space, evolving regulatory content, and manual labeling processes create significant complexity. The challenge is to streamline multilingual labeling efficiently while ensuring compliance, scalability, and cost control.

Expected Results from the Solution

The solution is expected to automate language selection based on market needs and regulations, reduce packaging and artwork preparation time, improve accuracy and compliance, and enable more flexible, data-driven labeling strategies. It should also minimize waste, optimize inventory, reduce costs, and enhance collaboration across Marketing, Regulatory, and Supply Chain teams.

Brief Challenge Description

Maintenance services in industry are currently fragmented across multiple providers, systems, and data sources, limiting coordination and the ability to deliver integrated, efficient solutions. This affects industrial companies seeking reliable, end-to-end services, as well as providers lacking a shared digital environment. Addressing this gap can enable collaboration, improve performance, and support scalable, technology-driven solutions

Expected Results from the Solution

The solution is expected to enable coordinated service delivery, integrate data and processes across stakeholders, and support the development of scalable, technology-driven maintenance solutions. It should improve efficiency, decision-making, and access to new markets for participating companies.

Brief Challenge Description

Warehouse receiving processes rely heavily on manual inspection, counting, and verification of incoming materials. This leads to errors, delays, and increased operational costs. The lack of automation limits scalability and real-time visibility, affecting warehouse staff, logistics teams, and overall supply chain efficiency. This challenge drives innovation in industrial processes, enhances operational efficiency, and promotes more responsible resource management.

Expected Results from the Solution

The solution should automate material identification, counting, and verification using computer vision and AI. It should improve accuracy, reduce errors and processing time, and enable real-time validation of deliveries. Integration with ERP/WMS systems and scalability across multiple warehouse environments are expected.

Brief Challenge Description

Battery traceability is limited to basic production data, with no end-to-end lifecycle visibility. This restricts compliance with the new EU Battery Regulation and limits ESG reporting, recycling, and reuse. At the same time, the absence of structured data prevents the development of scalable digital services and reduces transparency across the value chain.

Expected Results from the Solution

The solution should enable unique battery identification and full lifecycle tracking from production to end-of-life. It should support ESG and compliance reporting, connect all stakeholders, and facilitate recycling and reuse. At the same time, it should enable the development of scalable digital services and new data-driven revenue streams.

Brief Challenge Description

FODSA Central Macedonia oversees biowaste treatment across multiple existing and planned composting facilities that use different processes and monitoring practices. Today, data collection is fragmented and compost quality is checked mainly through periodic lab tests, limiting real-time oversight, performance comparison, and optimization.. This matters for operators, regulators, municipalities, and end users who depend on safe, compliant, high-quality compost.

Expected Results from the Solution

Expected outcomes include real-time remote monitoring of composting operations, automatic quality indicators, early alerts for deviations, and digital traceability of compost windrows. The solution should improve compost quality, reduce lab analysis costs, optimize facility performance, support certification and compliance, and increase the market value of compost.

Brief Challenge Description

Significant quantities of low-density polyethylene (LDPE) plastic film, mainly supermarket bags, are recovered from mixed municipal waste in Mechanical Biological Treatment (MBT) facilities. Due to high contamination with organic residues, moisture, and low density, recycling remains economically unattractive. As a result, the material accumulates without viable outlets, posing operational, environmental, and market challenges for waste management authorities and recycling industries.

Expected Results from the Solution

Pilot a scalable process that turns contaminated LDPE film from MBT into a marketable secondary raw material. Expected outcomes: reduced moisture/contamination, higher recycling and diversion from residue, proven technical/economic viability (LCA/CBA), new local value chain and replicable model for other facilities.

Brief Challenge Description

THPA S.A. manually reviews 500+ daily customs release applications by comparing shipping line manifests against broker submissions. Current exact-string matching flags harmless wording differences as errors, forcing operators to decide case-by-case. This creates bottlenecks that delay cargo release, strain port throughput, and increase compliance risk. An AI-powered semantic verification engine would eliminate this bottleneck, directly impacting stakeholders across the supply chain.

Expected Results from the Solution

An AI engine that automatically approves or rejects customs release applications by semantically matching cargo data across sources. It scores discrepancy severity, generates plain-language explanations for borderline cases, and learns from operator feedback. The result: fewer manual reviews, faster cargo release, consistent decisions, and an auditable verification trail compliant with trade regul