PreCoMInd - Predictive Cognitive Maintenance of Industry

https://www.fed4fire.eu/demo-stories/cc/precomind/

Predictive Cognitive Maintenance of Industry addresses the challenge of operational expenditure reduction in large-scale Industry 4.0. PreCoMInd aims to predict the problems/failures during operation of the system using the semantic dataset (SD) integrating heterogeneous data.

Commitmentseer - Semantic integration and planning of orders in the MARKET4.0

CommitmentSeer reduces operational risk for suppliers participating in the MARKET4.0 platform by interactive planning & scheduling of incoming customer orders. It empowers suppliers to make realistic estimates and commitments based on actual factory production plans that include incoming as well as existing production orders.

Learn more on http://market40.eu/commitmentseer/

ASMOSA - Agile Semantic Model driven development for Smart Applications ecosystem in manufacturing

ASMOSA addresses reduction of cognitive burden that is expected to be the main relevant human-related issue for workers in Big Industry including developers and administrators as well as workers in logistics, sales, management, etc. It builds upon the powerful set of middleware components as well as services available in SHOP4CF and provides a showcase for advanced end-user smart applications development environment based on micro front-ends and services supporting scalable agile production process. ASMOSA aims to improve functionality, responsiveness and usability of enterprise applications by empowering any user to simply compose different end-user applications without need for IT expertise.

FASPAS - Figueat Aware Semantics for Planning and Scheduling

One of the critical challenges in discrete manufacturing environments is to improve wellbeing of human workers while scaling up the number of work orders completed. FASPAS addresses the challenge by leveraging automatic generation of plans and schedules. The aim to optimize manufacturing operation as well as workforce in mass production of heterogeneous custom products becomes even more challenging when dynamic aspects such as physiological state and fatigue of employees are taken into consideration. Main benefits include cost reduction, reliable commitments to customers, improved production throughput as well as higher job satisfaction level in the workplace. In order to integrate all aspects of the problem, FASPAS semantically annotates data from FaMS, an AI-based KITT4SME component, that analyzes physiological data indicating fatigue acquired by wearable devices (such as blood pressure, heart rate, galvanic skin response) together with static characteristics (skills, age, work experience and similar). The Enterprise Knowledge Graph. Generated in this way, is used as an enabler for reasoning about different optimizations taking into account all relevant factors.

SAIHAN4EF - Semantic AI-Human Agentic Network for Energy Flexibility

SAIHAN4EF addresses the challenge of managing energy flexibility in decentralized environments, such as residential buildings and tourist attractions. It proposes an AI agentic platform to connect raw IoT measurements, energy analytics services, domain knowledge, policies, planning solutions, and user needs. Virtuona's established TasorSCAS platform is integrated with the O-CEI Cloud-Edge-IoT infrastructure to bring together distributed energy services and data sources. The Tasor Agentic Infrastructure (TAI) introduces a novel semantic harness for agentic AI powered by state-of-the-art Large Language Models (LLMs) that run locally as well as in a cloud. AI agents are guided through predefined semantic actions and validated workflows, ensuring that recommendations are based on operational constraints, available services, and domain knowledge while keeping humans involved throughout the decision-making process. Its modular architecture also enables easy integration of external tools, optimization engines, IoT platforms, and additional AI services. SAIHAN4EF makes energy flexibility services sovereign while accessible using natural language by non-expert users, including citizens and tourism operators. By combining semantic technologies, LLMs, guided execution, human-in-the-loop decision making, and explainable AI, the TAI provides transparent recommendations, simplifies the use of energy flexibility services, and offers a flexible foundation for integrating new services, data sources, and intelligent capabilities.