MOH operates a diverse fleet of CNC machines and already collects selected production information, but the current workflow requires manual interpretation of dashboards and scattered data sources. As a result, decision-making on throughput losses, idle time, and energy waste depends heavily on expert availability and on time-consuming data analysis. This creates a practical gap between data visibility and operational action, especially when rapid responses are required for scheduling changes, tooling issues or unexpected downtime.

A second challenge is sustainability-driven competitiveness. Automotive customers increasingly require evidence of energy and resource efficiency, yet energy consumption is often monitored at a high level rather than per machine or per part. Without fine-grained, explainable insights, it is difficult to identify specific energy loss mechanisms such as long idle periods with high standby power, suboptimal sequencing of jobs, or recurring micro-stoppages that increase energy per part.

Finally, the adoption of AI-based assistance in manufacturing depends on trust, usability and compliance. Recommendations must be transparent and kept under human oversight, and the technical solution must integrate with existing shop-floor infrastructure using open interfaces. The consortium must therefore deliver a robust, interoperable system and a conversational assistant that production staff can rely on in daily work without creating new operational burdens.

Objective 1:

Deploy a unified, real-time data acquisition pipeline that connects MOH’s ten CNC machines to a central platform through industrial edge controllers and energy-monitoring sensors, enabling reliable capture of machine state, downtime events, cycle counts and power consumption using open protocols and formats.

Objective 2:

Develop and validate an analytics layer that computes at least four core KPIs, including OEE, machine utilization, idle time ratio and energy consumption per part, and that can quantify measurable improvements in productivity and energy intensity during the experiment.

Objective 3:

Implement a WASABI-aligned Digital Intelligent Assistant that automatically generates daily natural-language reports and supports interactive queries for production personnel, and package the resulting assistant skill for distribution through a WASABI White Label Shop instance to enable replication by other manufacturing SMEs.

The experiment is carried out in the metal machining sector, with MOH d.o.o. producing precision-turned and milled parts for automotive customers. The pilot will take place at MOH’s production site in Skofja Loka, Slovenia, in a real operational environment with live production constraints, including machine availability, shift schedules, safety requirements and IT/OT security policies.

Target users are production managers, shift leaders and machine operators who need rapid, interpretable insights into machine efficiency and energy performance. Key stakeholders include MOH management responsible for productivity and sustainability, and Inkolteh as the technology provider aiming to productize the solution as an advanced ccLEAP module. DIH Slovenia supports alignment with trustworthy AI and regulatory expectations, and will use the results as a demonstrator for SME digital transformation and compliance best practices.

Integration constraints include the need to interface with heterogeneous CNC equipment from multiple vendors and to operate reliably in an industrial network. To address this, the architecture relies on standard industrial interfaces (such as Modbus and OPC UA) and IoT messaging (MQTT) with open data representations (e.g., JSON payloads and REST APIs). The assistant will be deployed with containerized components (Docker-based stack) to support maintainability and reproducibility, and all user-facing outputs will remain under human oversight, with clear traceability from recommendations to underlying KPI evidence.