The experiment addresses several interrelated operational and technical challenges within a small manufacturing environment where production coordination and quality assurance processes are largely experience-driven and manually executed. Production orders are managed through human supervision, while visual inspections depend heavily on skilled personnel. This creates bottlenecks, variability in inspection consistency and limited structured data for performance analysis. The challenge is not only technological but also organisational, as any introduced system must integrate into existing workflows without disrupting productivity.

From an operational perspective, task coordination across multiple concurrent orders requires continuous managerial oversight. In environments with limited staff, this can lead to suboptimal task sequencing and idle periods between production steps. Introducing a digital assistant must therefore balance automation with human control, ensuring that the system supports rather than overrides managerial decision-making.

Technically, the reliability of voice interaction in an industrial environment presents a significant challenge. Background noise from printers and machinery, short or ambiguous commands, worker accents and potential multi-device interference may reduce speech recognition accuracy. Ensuring robust wake-word activation, reliable intent detection and effective clarification mechanisms is essential to avoid operational disruption caused by misinterpreted commands.

Similarly, computer vision-based quality assurance introduces its own complexity. Variability in lighting conditions, camera positioning and image alignment can directly affect defect detection performance. The system must ensure stable hardware setup, controlled lighting, calibration procedures and threshold tuning to minimise false positives and false negatives while maintaining practical inspection speed.

Deployment within the factory environment presents additional constraints. The introduction of tablets, cameras and embedded computing equipment must not interfere with established production flows or workspace ergonomics. Hardware positioning and mounting structures must therefore be carefully designed to prevent operational disturbance.

Finally, organisational adoption and user trust represent critical challenges. Workers and managers must perceive the system as supportive rather than intrusive. Ensuring intuitive interaction, gradual integration and adequate training is essential to achieving sustainable adoption and long-term impact.

Currently, finding the right support tools for these specific environments is difficult for many SMEs. Traditional monitoring systems are often costly, technically complex or difficult to integrate into agile SME environments, while standard training materials and safety instructions tend to be static and provide no contextual or real-time support during work. Furthermore, generic voice assistants often rely on cloud processing, which raises concerns regarding latency, reliability and data privacy. 

As a result, there is a lack of accessible, privacy-first technologies that align with operational realities and support worker wellbeing without compromising established processes or data protection expectations.

Objective 1: 

Develop and integrate a fully operational Digital Intelligent Assistant that combines voice interaction, task coordination, computer vision-based quality assurance and analytics into a unified system, ensuring functional coherence and technical correctness across all modules.

Objective 2: 

Evaluate and validate the Digital Intelligent Assistant within the real production environment of the manufacturing SME, executing pilot scenarios, measuring performance against defined indicators and assessing its operational feasibility and user acceptance.

Objective 3: 

Prepare, publish and validate the Digital Intelligent Assistant within the WASABI marketplace ecosystem by packaging the solution according to marketplace requirements, integrating Web3-enabled capabilities and completing the necessary interoperability and compliance evaluations.

The experiment is positioned within the industrial electronics manufacturing sector, specifically focusing on membrane switch production. The primary goal is to integrate and test the TERIYAKI Digital Intelligent Assistant directly within real manufacturing conditions at the SKOUPAS production premises in Greece. The validation will take place under normal operating conditions, including screen printing, assembly and quality control, ensuring that the solution is assessed in an authentic industrial environment rather than a laboratory or simulated setting.

The main users involved in the experiment include the screen printing specialist, responsible for operating the printing process and interacting with the quality assurance system during production; the Production Manager, who oversees workflow coordination, scheduling, and prioritization across multiple production orders; and a hybrid professional role combining technical understanding with operational responsibilities, acting as a bridge between production staff and the digital solution deployment. These users will interact with the system through workflow coordination tools, computer-vision-assisted quality inspection, and voice-enabled task support.

Relevant stakeholders include company management evaluating operational efficiency improvements, technical partners responsible for solution development and integration, digital innovation support organizations facilitating the experiment, and potentially end customers interested in improved product consistency and traceability. These stakeholders contribute to defining requirements, validating outcomes, and assessing the broader applicability of the solution within manufacturing environments.

Several operational and technical constraints must be carefully addressed during the experiment. The computer-vision-supported quality assurance system operates in-process during screen printing and therefore requires high sensitivity and stability in image acquisition. Multiple variables may affect consistency and defect detection accuracy, including lighting conditions, camera alignment, surface reflections, vibration, print variability, and environmental factors. Ensuring reliable defect identification under these dynamic production conditions represents a critical technical challenge. In parallel, the voice-enabled interaction must function within a real factory environment characterized by background noise from printers and other machinery. Potential issues such as wake-word false positives or false negatives, worker accents, short command structures, and possible interference between multiple devices must be considered to maintain usability and operator trust. These constraints require robust calibration and careful system integration to ensure that TERIYAKI performs reliably without disrupting production continuity or operator workflow.