This work aims to review literature related to the latest cyber-physical systems (CPS) for manufacturing in the revolutionary Industry 4.0 for a comprehensive understanding of the challenges, approaches, and used techniques in this domain. Different published studies on CPS for manufacturing in Industry 4.0 paradigms through 2010 to 2019 were searched and summarized. We, then, analyzed the studies at a different granularity level inspecting the title, abstract, and full text to include in the prospective study list. Out of 626 primarily extracted relevant articles, we scrutinized 78 articles as the prospective studies on CPS for manufacturing in Industry 4.0. First, we analyzed the articles’ context to identify the major components along with their associated fine-grained constituents of Industry 4.0. Then, we reviewed different studies through a number of synthesized matrices to narrate the challenges, approaches, and used techniques as the key-enablers of the CPS for manufacturing in Industry 4.0. Although the key technologies of Industry 4.0 are the CPS, Internet of Things (IoT), and Internet of Services (IoS), the human component (HC), cyber component (CC), physical component (PC), and their HC-CC, CC-PC, and HC-PC interfaces need to be standardized to achieve the success of Industry 4.0.

The design of a disassembly line is a long-term investment. Therefore, it is important to optimise its performance and demonstrate its financial viability. The design process must consider the unique End-of-Life product characteristics and disassembly operation particularities. These peculiarities are mainly expressed by uncertainty in post-consumer products quality, variability of task processing times and presence of hazardous material. To achieve efficient disassembly line design, efficient decision-making tools are needed. The purpose of this work is to propose such a tool. The objective is to efficiently design a disassembly line, as a disassembly system, that provides optimal income and takes into account Enf-of-Life (EoL) product quality uncertainty and task processing times variability. The proposed tool allows the decision-maker to select the optimal disassembly process and depth while assigning retained tasks to the line workstations. Hazardous material of EoL products is handled and the designed line guarantees a certain level of service set by the decision-maker. The product part revenue depends formally on its EoL state or quality. Several product examples from remanufacturing sectors in industry are utilised to demonstrate interest and relevance of the proposed decision-aiding tool in an industrial context.

With the increasing demand for digital transformation and (digital) technology transfer (TT), digital innovation hubs (DIHs) are the new piece of the puzzle of our economy and industries’ landscapes. Evidence shows that DIHs can provide good opportunities to access needed innovations, technologies, and resources at a higher level than other organizations that can normally access them. However, it is critically important to note that DIHs are still evolving, under research, and under development. That is, there are many substantial aspects of DIHs that should be considered. For example, DIHs must cater to a wide spectrum of needs for TT. From this perspective, the contribution of this work is proposing a generic and flexible learning framework, aiming to assist DIHs in providing suitable education, training, and learning services that support the process of (digital) TT to companies. The proposed learning framework was designed, evaluated, and improved with the support of two EU projects, and these processes are discussed in brief. The primary and leading results gained in this way show that the learning framework has immense potential for application to similar cases, and it can facilitate and expedite the process of TT to companies. The study is concluded with some directions for future works.