Manufacturing SMEs, particularly in sectors with intensive logistics and traceability requirements such as food production, still rely heavily on manual data entry and screen-based interfaces for warehouse and inventory operations. Operators frequently need to interrupt physical tasks to input information into terminals, scan codes, or navigate complex software menus. This constant switching between physical handling and digital interaction increases execution time, creates opportunities for human error, and adds cognitive strain to workers.

In the case of Conservas Pinhais & Cia, logistics processes such as goods reception, picking preparation, stock movements and inventory counting require precise data registration to ensure traceability, compliance with food safety standards, and accurate stock control. Even small registration errors can lead to discrepancies in inventory levels, delays in order fulfilment, and additional verification procedures. In regulated environments such as the agri-food sector, maintaining accuracy and auditability is essential, making reliable data capture a critical operational requirement.

Another significant challenge relates to workforce onboarding and training. Traditional Manufacturing Execution Systems often require structured training before operators become fully autonomous. This creates a dependency on experienced staff and extends the learning curve for new employees. In a context where SMEs face increasing labour shortages and the need for higher operational agility, simplifying interaction with digital systems becomes a strategic priority.

From a technological perspective, integrating conversational artificial intelligence into industrial systems also presents challenges. Voice recognition must operate reliably in noisy factory environments, system integration must be secure and interoperable with existing software, and data handling must comply with European data protection regulations. Ensuring transparency, user control, and GDPR-compliant data management is fundamental when introducing AI-based assistance into operational workflows.

The CALIE experiment addresses these combined operational, organisational, and technological challenges by rethinking how workers interact with digital systems in logistics and inventory management, moving from screen-based input to natural, voice-enabled interaction while preserving traceability, compliance, and system reliability.

Objective 1:

Design and develop a voice-enabled Digital Intelligent Assistant integrated with the Flow Manufacturing system, enabling operators to execute logistics and inventory tasks through natural language interaction in a secure, modular and interoperable architecture aligned with the WASABI framework.

Objective 2:

Deploy and validate the assistant in real factory operations at Conservas Pinhais & Cia, demonstrating measurable improvements in logistics performance, including reduction of human errors, faster task execution, increased productivity, reduced training effort and improved operator satisfaction.

Objective 3:

Package and publish the developed conversational skill in the WASABI White Label Shop, ensuring replicability, scalability and accessibility for other manufacturing SMEs interested in adopting human-centric AI solutions for logistics and inventory management.

The CALIE experiment is implemented in the agri-food manufacturing sector, specifically in canned fish production. Conservas Pinhais & Cia operates a traditional yet export-oriented manufacturing facility in Portugal, where logistics, traceability, and inventory control are critical to daily operations. The experiment will be carried out directly at the company’s production and warehouse site, in a real operational environment rather than a laboratory setting.

The assistant will be deployed within the existing logistics and inventory workflows supported by the Flow Manufacturing system. The operational environment includes goods reception areas, storage facilities, picking preparation zones, and inventory control processes. These activities involve frequent material handling, barcode scanning, stock confirmations, and data registration tasks that must comply with strict traceability and food safety requirements.

The primary users of the solution are warehouse operators, logistics staff, and supervisors responsible for stock accuracy and order preparation. The system must therefore operate reliably in a factory environment characterised by background noise, movement, and time-sensitive operations. Usability, clarity of voice interaction, and minimal disruption to existing workflows are key considerations.

The experiment must also comply with regulatory and operational constraints. As a food manufacturing company, Conservas Pinhais & Cia follows strict food safety and traceability standards, requiring accurate and auditable data registration. In addition, the solution must ensure secure authentication, controlled access to operational data, and full compliance with European data protection regulations. Integration with the existing Flow Manufacturing system must preserve data integrity and ensure real-time synchronisation of inventory records.

By deploying the conversational assistant in a live manufacturing setting, the experiment demonstrates how voice-enabled interaction can be embedded into real industrial workflows without compromising compliance, traceability, or operational reliability.