Developing warehouse management skills through Learning Factories:a use case

The advancements in the manufacturing sector have necessitated the evolution of educational paradigms to address skills mismatches and enhance workforce adaptability. This paper explores the potential learning use cases related to the incorporation of automated warehouse stations into Learning Factories (LFs) to develop competencies for warehouse management within Industry 5.0 framework. It presents a structured methodology for aligning educational goals with industry demands, emphasizing technical, methodological, personal, and interpersonal skills. A detailed analysis of the skills required for warehouse managers was conducted using the ESCO database and scientific literature. These findings informed the development of a laboratory use case involving an automated warehouse station at the University of Bergamo's SLIM Lab. The study evaluates various warehouse management strategies, emphasizing the relationship between operational choices and key performance indicators (KPIs) like energy consumption, error rates, and processing times. Results highlight the effectiveness of LFs in fostering essential competencies while addressing limitations in current LF configurations, such as inconsistencies in data collection and KPI measurement. The proposed framework demonstrates potential for broader applications across diverse job profiles and LF setups, providing a robust foundation for advancing education and training in automated logistics systems. Future research aims to extend this framework to encompass additional job profiles and advanced LF technologies.