Digital twins are considered the next step in IoT-based cyber–physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.

Product lifecycle management through innovative and competitive business environment

In the rapidly evolving landscape of Industry 4.0, the demand for skilled professionals well-versed in Internet of Things (IoT) technologies is escalating. However, a significant challenge faced in educational settings is the lack of comprehensive and effective methodologies for imparting practical knowledge and skills in IoT. This paper presents an approach for designing and implementing an Internet of Things (IoT) laboratory in which students may practice and comprehend many components of an IoT environment, such as analysis of sensor data, IoT platform development, and setup of messaging protocols. The teaching methodology adopts a Cyber-Physical System (CPS) framework, which integrates teachers, classrooms, and resources to create a comprehensive learning environment. Bloom's taxonomy is employed to assess the efficacy of the suggested technique in terms of cognitive skills and knowledge acquisition. The evaluation procedure demonstrated the advantages of a hybrid learning environment, which integrated both face-to-face and remote instruction. The case study is conducted in an engineering higher education course on first year students. The findings of this paper lay the groundwork for a remotely deliverable IoT training course, contributing to the advancement of IoT education and equipping students with the necessary skills for the evolving landscape of Industry 4.0.

This paper presents a procedure for selecting, evaluating, and ranking the key performance indicators (KPIs) in the context of warehouse systems. The method aims to help warehouse managers identify and prioritize KPIs most relevant to their business objectives and performance goals, ultimately driving continuous improvement in warehouse performance. The paper provides three main scientific contributions: (i) a selection composed of 70 KPIs extracted from the analysis of 203 scientific articles and clustered according to their business and sustainability objectives, (ii) a KPI ranking based on four different metrics measured analyzing 585 scientific papers from Scopus and, finally, (iii) a fifth metric, measured trough a survey submitted to 15 industrial experts from as many different Italian companies, that is added to improve the ranking system. As a part of the method applied to a case study, the paper provides a comprehensive review of the current scientific literature and industrial sector state of the art on warehouse KPIs. This article proposes an overview of the most used KPIs in research and industry, a support for understanding formalizing definitions, and a guide on how they can be measured and used to manage warehouses.

Purpose: The present paper aims to demonstrate the potential of integration between industrial robotics and Lean Manufacturing (LM) approach to increase the efficiency of an assembly line.Design/methodology/approach: Based on a case study performed in an Italian company, this paper reports a comparative analysis of the results produced on a line balancing study involving a semi-automated production line, aided by an industrial robot.Findings: The results suggest the possibility of implementing industrial robotics in line balancing studies highlighting efficiency gains and idle reduction. Further, it also addresses some concepts directly related to industry 4.0, such as collaborative robotics, artificial intelligence, and lean automation.Practical implications: Line balancing studies may include advanced robotics in order to extend traditional lean practices toward Digital LM.Originality/value: This study adds contributions to the operational excellence literature, demonstrating the symbiosis between industrial robotics and LM practices.