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Data Automation and Retrieval Technology (DART)

Reference number
Coordinator Linköpings universitet - Linköpings tekniska högskola Inst f ekon & industruell utv IEI
Funding from Vinnova SEK 6 554 147
Project duration September 2024 - August 2027
Status Ongoing
Venture Advanced digitalization - Enabling technologies
Call AI for advanced digitalization 2024

Purpose and goal

DART addresses the critical challenge of efficiently managing and utilizing the diverse data formats prevalent in industrial settings, which currently lead to significant inefficiencies in product development and production processes. DART´s use of Retrieval-Augmented Generation (RAG) for comprehensive data analysis and management directly supports the program´s ambition to develop advanced, interoperable digital solutions. The aim of the project is to highlight possible opportunities and potential challenges for eventual commercialization strategies.

Expected effects and result

Demographic trends, combined with structural skills mismatches and insufficient education and training, are leading to shortages of appropriately skilled worker. Based on this, it is evident that systems are needed to support the generational shift, where experienced workers and engineers are soon retiring, and a new generation must take up the work and swiftly digest company specific knowledge. A system like DART will support the transition of knowledge from one generation to another, by simplifying knowledge access, retrieval and presenting it in a manner suitable for the user.

Planned approach and implementation

The project is managed through biannual steering group meetings with representatives from LiU and partners, ensuring adherence to the project plan. An agile methodology is employed, facilitating flexibility and iterative development. This collaborative structure is designed to enhance knowledge transfer and co-creation, aiming to produce innovative solutions in technology development.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 17 September 2024

Reference number 2024-01420