Fast-paced technological advancements and ever-growing demand for customized products trigger the need for a transition from traditional manufacturing to intelligent manufacturing. Digital Twin assists this transition and is one of the most propitious technologies that is receiving noteworthy attention from both industry and academia. This review paper focuses on identifying the benefits of Digital Twin applications for the themes of product design and development processes. Benefits are analyzed according to the 10 knowledge areas defined by the Project Management Institute’s project management body of knowledge (PMBOK). Classification by knowledge area helps to understand where benefits can best be found according to desirable product characteristics and corporate goals. This paper analyzes which PMBOK knowledge area has proven, practical applications and sound theoretical underpinning, thus indicating the likelihood of a successful implementation of Digital Twin.

Industry 4.0 Paradigm unleashed tremendous opportunities to boost economical and societal transitions for the business and improved living standards of society, while Industry 5.0 is extending previous technological breakthrough in steering transformation, stimulating industry and society to be human-centric, sustainable, resilient, and green. Digital Twins, in turn, play a key role as enabler in such transformation. Heterogeneity of manufacturing technologies imposes specific challenges in developing and adopting Digital Twins. The main goal of this paper is to propose, assess and justify a robust and open framework of Digital Twins for manufacturing technologies, such as collaborative and mobile robots, as well as subtractive manufacturing machines. A framework eventually can be used by SME, while also in Educational and Research Institutions.

Digital twins have the potential to revolutionize the way we design, build and maintain complex systems. Digital twins are high-fidelity representations of physical assets in the digital space and thus allow advanced simulations to further optimize the behaviour of the physical twin in the real world. This topic has received a lot of attention in recent years. However, there is still a lack of a well-defined and sufficiently generic data structure for representing data-driven digital twins in the numerical space. Indeed, the development of digital twins is often limited to particular use cases. This research proposes a data structure for developing modular digital twins that maintain the coherence between the digital and physical twins. The data structure is based on a hierarchical representation of the digital twin and its component; the proposed data structure uses concepts from distributed systems and object-oriented programming to enable the integration of data from multiple sources. This enables the development of a digital twin instance of the system and facilitates maintaining the coherence between the digital twin and physical twin. We demonstrate the effectiveness of our approach through a case study involving the digital twin of an industrial robot arm. Our results show that the proposed data structure enables the efficient development of modular digital twins that maintain a high degree of coherence with the physical system.