PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022
PROJECT TITLE: Prescriptive digital twins for cognitive-enriched competency development of workforce of the future in smart factories (RESILIENCE)
CUP CODE: D53D23003710006
Budget: € 67.774
MUR contribution allocated to the research unit: 47.338 Euro
University/Institution co-funding allocated to the research unit 20.436 Euro
Total cost: 67.774 Euro
U.R. Coordinator (Poliba)Giorgio Mossa
Other Research Units:
- University of Calabria
- University of Salerno
- University of Brescia
Brief project description
The RESILIENCE project addresses the need to innovate workforce training processes in advanced manufacturing contexts, characterized by high complexity and an increasing presence of non-routine activities. The project proposes a new paradigm based on prescriptive digital twins, integrating technologies such as digital twin, prescriptive analytics, Building Information Modelling (BIM), and structured On-the-Job Training (OJT) strategies. This approach makes it possible to generate contextualized digital training environments and to activate “on-demand” training, delivered only when needed and adapted to specific operational conditions.
The research unit of the Polytechnic University of Bari contributes through the cognitive modeling of tasks and human performance, integrating factors such as cognitive load, task complexity, and individual characteristics into training and decision-making processes.
Purpose
The main objective of the project is the development of an integrated prescriptive training system for improving workforce skills in smart factories. In particular, the project aims to:
• develop prescriptive analytics models for activity recommendation;
• generate digital training environments through BIM and digital twin;
• define learning models based on cognitive factors;
• model task complexity and human performance;
• promote technology transfer and innovation in Industry 4.0 contexts.
Expected results
The project envisages the development of:
• a prototype training system based on prescriptive digital twins;
• improved worker skills, safety, and adaptability;
• reduced training times and increased skills retention;
• decision-support tools for activity and training planning;
• scientific advances in the fields of digital twins, artificial intelligence, and human-centric systems.
Achieved results
The research unit of the Polytechnic University of Bari developed a human-centric approach for modeling human performance and training strategies in advanced industrial contexts. In particular, the following results were achieved:
• development of analytical frameworks linking task complexity, cognitive load, and human performance;
• systematic analyses of methods for measuring cognitive load and technologies supporting the operator;
• definition of models for learning and skill decay (learning/forgetting);
• experimental validation of training strategies under realistic industrial conditions;
• evidence supporting personalized and adaptive training approaches, based on the individual characteristics of workers.
Overall, the results contribute to defining a prescriptive training paradigm in which operational and training decisions take into account both the needs of the production system and the cognitive abilities and conditions of workers.
