Industry 4.0 technologies, originally developed in the manufacturing sector, can be purposefully implemented to improve City Logistics (CL) processes by automatizing some of their operational tasks and enabling real-time exchange of information, with the ultimate goal of providing better interconnection among the actors involved. This work aims to identify the main social and economic contextual drivers for investing in the application of Industry 4.0 technologies to urban logistics. To this end, a dataset based on the primary collection of 105 CL projects exploiting the main 4.0 technologies has been built. After that, a regression model has been completed including potential economic, strategic, and demographic determinants of investments in CL 4.0. According to the obtained outcomes, Gross Domestic Product, Foreign Direct Investments, Research and Development Expenditure, Employment Rate, and Number of Inhabitants are significant contextual factors for the adoption of Industry 4.0 technologies in last mile logistics. The study might support academicians to investigate novel application fields of Industry 4.0 technologies. Also, it can serve as a roadmap for orienting the investments of private organizations and public entities to promote CL innovation and digitalization. Moreover, Industry 4.0 technology providers might find this study interesting to uncover prospective business sectors and markets. Future research efforts will analyse the impacts of internal business factors on CL 4.0 and the satisfaction levels of urban logistics stakeholders.

Automated storage and retrieval systems have become increasingly popular in modern supply chains due to their significant advantages over traditional warehousing systems. Due to the high complexity of these systems, simulation approaches can be used to generate accurate performance measures for a specific system configuration. Simulation models are also the cornerstone of digital twins, one of the latest technological innovations that can further improve warehouse operations. Therefore, the aim of this research is to describe an approach for the development of a discrete event simulation model of an automated storage and retrieval system with a perspective towards the implementation of a digital twin. To be consistent with the objectives of the digital twin, the proposed model represents both the physical system and the overarching information technology architecture, such as the warehouse management system and the warehouse control system. In addition, this paper describes a methodology to validate such a simulation model by setting up an experimental campaign based on the principles of design of experiment. The experiments conducted in a logistics laboratory were used to iteratively calibrate the model until its performance accurately reflected the functioning of the real system. The results obtained demonstrate the effectiveness of the proposed method. Finally, this work contributes to the literature on warehouse digital twins by highlighting new variables to be considered when defining travel time models and their stochastic nature.

In recent years, automated warehouses have become increasingly important to meet the rising demand of supply chain operations. Despite their growing relevance to industry, these systems remain largely underrepresented in academic settings, which contributes to a significant gap in student knowledge and preparedness. Traditional educational approaches often fail to equip future professionals with the practical skills required by modern logistics systems. While the learning factory paradigm partially addresses this gap, it typically places limited emphasis on logistics processes. To bridge this divide between theory and industrial practice, a hands‐on learning experience was conducted in a logistics‐focused learning factory involving Bachelor's and Master's engineering students. A structured questionnaire was administered to evaluate students’ perceptions of automated warehouses, and statistical methods were employed to analyze both the short‐ and medium‐term impacts of the experience. Findings revealed a strong interest among students in industrial logistics, despite limited prior exposure to automation technologies. Consistent with previous research, the hands‐on approach was particularly effective for Master's students, highlighting its potential as a valuable educational tool in logistics engineering.