Final DAI-DSS Prototype, Documentation and Test Report – D4.3
Herwig Zeiner, Lucas Paletta, Julia Tschuden, Michael Schneeberger, Gustavo Vieira, Rui Fernandes, Johanna Lauwigi, Alexander Nasuta, Damiano Falcioni, Marlene Mayr, Christian Muck, Rishyank Chevuri
This deliverable, “D4.3 – Final DAI-DSS Prototype, Documentation and Test Report”, provides a comprehensive overview of the final implementation of the DAI-DSS. Building on the foundations established in “D4.1 – DAI-DSS Architecture and Initial Documentation and Test Report” and “D4.2 – Initial DAI-DSS Prototype”, this document details the integration, orchestration, and deployment of the components for AI-based decision-making within industrial applications. The prototype is designed to enhance operational efficiency and support decision-making for various scenarios. In this last version, the DAI-DSS Prototype extends its applicability across multiple industrial use cases, including workforce allocation, production planning, machine maintenance, and validation of documents. The demonstration materials can be accessed via the following link:
https://innovationshop.fairwork-project.eu/
By leveraging a modular and scalable architecture, the system facilitates the interaction between AI services, UIs, and structured data repositories. The implementation of DAI-DSS consists of several integrated building blocks:
The DAI-DSS User Interface collects multiple UI components for the different scenarios and AI services to enable stakeholders to visualize data, interact with decision-making tools, and monitor industrial workflows.
The DAI-DSS Orchestrator component serves as the central coordination engine, managing workflows, microservices, and AI-driven recommendations to ensure the system operation. It includes different approaches that range from centralized to decentralized prototypes.
The DAI-DSS Configurator consists of a tool designed to enhance decision support systems through configuration and integration frameworks. It consists of the Configuration Framework, which assists in creating decision models and strategies, and the Configuration Integration Framework, which generates system configurations. It allows for microservices and workflow configuration, featuring an interface with a wizard for UI components combination.
The DAI-DSS Knowledge Base is highlighted as a central data repository, storing user properties, sensor data, and processed data. It plays a key role in the system's data flow, integrating with the Configurator and using REST API for data retrieval.
The DAI-DSS AI Enrichment incorporates various decision-making techniques and AI services, including neural networks, decision trees, constraint programming, Multi-Agent Systems (MAS), Large Language Model (LLM), and retrieval-augmented generation (RAG), to provide tailored recommendations and automation support.
The final DAI-DSS Prototype delivers several advancements over previous iterations by 1) supporting AI-driven decisions that aim to enhance decision-making and information access with different AI techniques while including reflections on AI and data reliability, 2) ensuring scalability and adaptability of the system by its component-based architecture that allows for integration with various applications and expansion into new domains, 3) advancing data utilization and processing with efficient storage, retrieval, and processing of industrial data, in the Knowledge Base and Vector Databases and 4) proposing a flexible approach to enable the extension with new prototypes.
The DAI-DSS marks a step forward in AI-powered decision support for industrial environments. Its modular and scalable architecture provides a foundation for future AI enhancements, data integration, and broader enterprise adoption. In particular, the results and prototypes aim to be used as starting point for use cases in the area of robots in manufacturing settings for example supporting decisons in maintenance or optimal robot-task and line allocation. Furthermore, findings and implementations documented in this deliverable contribute to advancing intelligent, and effective decision-support solutions in industrial ecosystems, and aim to contribute to future European AI research and reference architectures.
https://innovationshop.fairwork-project.eu/
By leveraging a modular and scalable architecture, the system facilitates the interaction between AI services, UIs, and structured data repositories. The implementation of DAI-DSS consists of several integrated building blocks:
The DAI-DSS User Interface collects multiple UI components for the different scenarios and AI services to enable stakeholders to visualize data, interact with decision-making tools, and monitor industrial workflows.
The DAI-DSS Orchestrator component serves as the central coordination engine, managing workflows, microservices, and AI-driven recommendations to ensure the system operation. It includes different approaches that range from centralized to decentralized prototypes.
The DAI-DSS Configurator consists of a tool designed to enhance decision support systems through configuration and integration frameworks. It consists of the Configuration Framework, which assists in creating decision models and strategies, and the Configuration Integration Framework, which generates system configurations. It allows for microservices and workflow configuration, featuring an interface with a wizard for UI components combination.
The DAI-DSS Knowledge Base is highlighted as a central data repository, storing user properties, sensor data, and processed data. It plays a key role in the system's data flow, integrating with the Configurator and using REST API for data retrieval.
The DAI-DSS AI Enrichment incorporates various decision-making techniques and AI services, including neural networks, decision trees, constraint programming, Multi-Agent Systems (MAS), Large Language Model (LLM), and retrieval-augmented generation (RAG), to provide tailored recommendations and automation support.
The final DAI-DSS Prototype delivers several advancements over previous iterations by 1) supporting AI-driven decisions that aim to enhance decision-making and information access with different AI techniques while including reflections on AI and data reliability, 2) ensuring scalability and adaptability of the system by its component-based architecture that allows for integration with various applications and expansion into new domains, 3) advancing data utilization and processing with efficient storage, retrieval, and processing of industrial data, in the Knowledge Base and Vector Databases and 4) proposing a flexible approach to enable the extension with new prototypes.
The DAI-DSS marks a step forward in AI-powered decision support for industrial environments. Its modular and scalable architecture provides a foundation for future AI enhancements, data integration, and broader enterprise adoption. In particular, the results and prototypes aim to be used as starting point for use cases in the area of robots in manufacturing settings for example supporting decisons in maintenance or optimal robot-task and line allocation. Furthermore, findings and implementations documented in this deliverable contribute to advancing intelligent, and effective decision-support solutions in industrial ecosystems, and aim to contribute to future European AI research and reference architectures.
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@techreport{fairwork_d4.3,
title = {Final DAI-DSS Prototype, Documentation and Test Report - D4.3},
author = {Herwig Zeiner, Lucas Paletta, Julia Tschuden, Michael Schneeberger, Gustavo Vieira, Rui Fernandes, Johanna Lauwigi, Alexander Nasuta, Damiano Falcioni, Marlene Mayr, Christian Muck, Rishyank Chevuri},
editor = {Marlene Mayr},
url = {https://fairwork-project.eu/deliverables/D4.3%20Final%20DAI-DSS%20Prototype%20v1.0-preliminary.pdf},
year = {2025},
date = {2025-02-28},
abstract = {This deliverable, “D4.3 – Final DAI-DSS Prototype, Documentation and Test Report”, provides a comprehensive overview of the final implementation of the DAI-DSS. Building on the foundations established in “D4.1 – DAI-DSS Architecture and Initial Documentation and Test Report” and “D4.2 – Initial DAI-DSS Prototype”, this document details the integration, orchestration, and deployment of the components for AI-based decision-making within industrial applications. The prototype is designed to enhance operational efficiency and support decision-making for various scenarios. In this last version, the DAI-DSS Prototype extends its applicability across multiple industrial use cases, including workforce allocation, production planning, machine maintenance, and validation of documents. The demonstration materials can be accessed via the following link:
https://innovationshop.fairwork-project.eu/
By leveraging a modular and scalable architecture, the system facilitates the interaction between AI services, UIs, and structured data repositories. The implementation of DAI-DSS consists of several integrated building blocks:
The DAI-DSS User Interface collects multiple UI components for the different scenarios and AI services to enable stakeholders to visualize data, interact with decision-making tools, and monitor industrial workflows.
The DAI-DSS Orchestrator component serves as the central coordination engine, managing workflows, microservices, and AI-driven recommendations to ensure the system operation. It includes different approaches that range from centralized to decentralized prototypes.
The DAI-DSS Configurator consists of a tool designed to enhance decision support systems through configuration and integration frameworks. It consists of the Configuration Framework, which assists in creating decision models and strategies, and the Configuration Integration Framework, which generates system configurations. It allows for microservices and workflow configuration, featuring an interface with a wizard for UI components combination.
The DAI-DSS Knowledge Base is highlighted as a central data repository, storing user properties, sensor data, and processed data. It plays a key role in the system's data flow, integrating with the Configurator and using REST API for data retrieval.
The DAI-DSS AI Enrichment incorporates various decision-making techniques and AI services, including neural networks, decision trees, constraint programming, Multi-Agent Systems (MAS), Large Language Model (LLM), and retrieval-augmented generation (RAG), to provide tailored recommendations and automation support.
The final DAI-DSS Prototype delivers several advancements over previous iterations by 1) supporting AI-driven decisions that aim to enhance decision-making and information access with different AI techniques while including reflections on AI and data reliability, 2) ensuring scalability and adaptability of the system by its component-based architecture that allows for integration with various applications and expansion into new domains, 3) advancing data utilization and processing with efficient storage, retrieval, and processing of industrial data, in the Knowledge Base and Vector Databases and 4) proposing a flexible approach to enable the extension with new prototypes.
The DAI-DSS marks a step forward in AI-powered decision support for industrial environments. Its modular and scalable architecture provides a foundation for future AI enhancements, data integration, and broader enterprise adoption. In particular, the results and prototypes aim to be used as starting point for use cases in the area of robots in manufacturing settings for example supporting decisons in maintenance or optimal robot-task and line allocation. Furthermore, findings and implementations documented in this deliverable contribute to advancing intelligent, and effective decision-support solutions in industrial ecosystems, and aim to contribute to future European AI research and reference architectures.
},
howpublished = {https://fairwork-project.eu/deliverables/D4.3%20Final%20DAI-DSS%20Prototype%20v1.0-preliminary.pdf},
keywords = {deliverable},
pubstate = {published},
tppubtype = {techreport}
}