The CIRC-SHOE experiment addresses a set of interlinked operational, technical, data, and sustainability challenges typical for EVA footwear production based on injection moulding. At the host SME (ТОВ «Взуття Поділля», TM GIPANIS), key production outcomes—output volume, scrap and defect rates, temperature stability of moulds/injector, and energy consumption—are strongly influenced by machine settings and process conditions that can fluctuate during shifts. When deviations are detected late, the result is avoidable waste: EVA runners/sprues, nonconforming parts, and rework that consumes additional time, energy, and raw material.

A core pain point is the lack of real-time visibility and early warning. Process data is often monitored locally at the machine or captured manually, while consolidated reporting is delayed and fragmented. This creates a “reaction gap”: technologists and mechanics learn about drift in temperature regimes, oil temperature, injection parameters, or abnormal energy patterns after defects have already accumulated or a machine has already operated inefficiently for hours. In practice, this leads to unstable quality, higher defect rates, and additional material losses, especially when multiple product types (soles, insoles, sabo, boots) are produced under varying mould and cycle conditions.

Another challenge is inefficient use and low-value handling of EVA waste. In footwear manufacturing, EVA waste streams (sprues/runners, trimming residues, nonconforming products) are frequently disposed of or sold at low value because there is insufficient traceability of waste origin, composition, and quality, and limited operational support for systematic reuse. Without structured accounting and analytics, it is difficult to identify which waste fractions are suitable for safe reintegration into production, how reuse affects quality, and what process adjustments are needed to maintain consistent output.

Energy consumption is a further critical issue. EVA injection moulding is energy intensive due to heating/cooling cycles, temperature maintenance, and downtime losses. When heating and cooling regimes are not optimized—especially under frequent changeovers or suboptimal temperature control—energy per unit increases and indirectly raises the carbon footprint of each pair produced. For SMEs facing volatile energy prices and competitiveness pressure, even small percentage improvements translate into meaningful cost savings and resilience.

The experiment also addresses a data-related and technical integration barrier: industrial data exists but is not turned into actionable intelligence. Sensor and controller signals (temperatures, cycles, energy readings, alarms) are typically stored in proprietary formats or remain unused beyond basic monitoring. SMEs often lack the tooling and expertise to collect time-series data reliably, normalize it, and apply descriptive/predictive analytics to detect anomalies and prevent defects. As a result, decision-making remains heavily dependent on individual experience, and process optimization is not reproducible across shifts or product batches.

Regulatory and trust considerations matter as well, especially as SMEs increasingly align with EU sustainability expectations and digital governance. Even when the system focuses on production data rather than personal data, it must demonstrate robust cybersecurity, role-based access, auditability, and transparent human oversight. In addition, any AI-enabled support tool must be deployed in a way that aligns with GDPR principles and a risk-based approach consistent with the EU AI Act—avoiding autonomous control of equipment, ensuring clear user awareness of AI involvement, logging actions, and enabling escalation to responsible staff in critical cases.

Finally, the challenge has a broader sustainability and market relevance. Reducing EVA waste and improving energy efficiency supports circularity goals and improves the environmental profile of products—an increasingly important factor for supply chains and customers seeking “greener” manufacturing. For Ukrainian and EU footwear SMEs, a replicable digital assistant that reduces scrap, energy use, and process variability can become a practical pathway to strengthen competitiveness while advancing Green Deal–aligned production practices.

Objective 1: 

The first objective is to design, implement, and operationalize the AI Waste Valorization & Monitoring Assistant (AI-WVMA) as a Digital Intelligent Assistant (DIA) integrated into the EVA injection moulding line at LLC «Vzuttia Podillia». The system will collect and process time-series production data (output, defect rate, temperature regimes, energy consumption, and related parameters), provide real-time dashboards and alerts, and apply descriptive and predictive analytics to detect deviations and support timely human decision-making—without autonomous control of equipment.

Objective 2: 

The second objective is to achieve measurable improvements in production efficiency and sustainability by using AI-driven analytics to optimize process parameters and enable structured EVA waste valorization. Within 12 months, the experiment aims to reduce EVA waste by 8–10%, decrease energy consumption per unit by at least 3%, increase the share of reusable EVA waste to at least 8%, and lower overall production costs by 5–8% through reduced scrap, improved stability of temperature regimes, and more efficient resource use.

Objective 3: 

The third objective is to validate the technical, operational, and business feasibility of AI-WVMA in a real industrial environment and prepare it for broader deployment via the WASABI ecosystem and EDIH networks. This includes documenting the architecture, ensuring compliance with GDPR and a risk-based approach under the EU AI Act, developing a sustainable exploitation and business model, and publishing the solution as a replicable module that can be adapted by other footwear and light industry SMEs in Ukraine and the EU.

The experiment takes place in the context of high-precision manufacturing. These environments require consistent procedures, careful handling of materials, and adherence to strict quality and safety requirements. Workers often perform repetitive or fine-motor tasks that demand sustained concentration and static postures. In many cases, the use of protective equipment or specialised work attire further limits conventional interaction methods such as touch-based interfaces, reinforcing the need for accessible and non-intrusive interaction modalities.

The pilot is carried out together with our manufacturing partner who is experienced in regulated medical device production environments. The experiment focuses on an assembly process involving wearable devices and sensitive electronic components. It examines how a conversational Digital Intelligent Assistant (DIA) can be introduced as a supportive layer within existing workflows. Particular attention is given to privacy-by-design principles, user acceptance and maintaining a non-intrusive interaction model.

Primary users are assembly workers performing detailed tasks, while stakeholders include technical teams and organisational decision-makers interested in practical approaches to human-centred digitalisation. The exploration seeks to better understand how such assistants are perceived in everyday work contexts and what conditions support meaningful adoption.