This review article delves into the conceptual framework of digital twins and their diverse applications across research domains, highlighting the pivotal role of machine learning in shaping the development and integration of digital twin technology across multiple disciplines. Emphasising key features like multidisciplinarity and multi-scale aspects, the paper explores how data-driven techniques are employed for modelling, visualisation, monitoring, and optimisation within the digital twin framework, pinpointing the benefits introduced in the current state-of-the-art applications, and elucidates persisting challenges across various research fields, including advanced materials, smart buildings, and manufacturing systems.

Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0.

The increasing globalisation process has led to a radical change in the production concept, moving from a mass production paradigm towards one of mass customisation (MC), and focusing on value creation by pursuing customers’ needs and increasing responsiveness. The rapid development of information technologies has also made it possible to create new manufacturing paradigms, such as Industry 4.0 and cloud manufacturing, in which the increased level of autonomy is one of the key concepts for tackling new market challenges. This paper proposes a decentralised scheduling approach that improves the performance of production systems while minimising the usually high work-in-progress (WIP) requirements of the classic centralised scheduling and inventory production control system. Using a semi-heterarchical Manufacturing Planning and Control (MPC) architecture and integrating the Industry 4.0 innovation in a cloud manufacturing environment, this work contributes to the design of the lower level of the MPC architecture. The resulting production controller can allocate jobs following different dispatching rules dynamically. The performances of the proposed approach were assessed for different production scenarios and control parameter settings through an exhaustive experimental campaign based on hybrid simulation tools. The results showed that the proposed low-level controller led to a productivity increase while delivering increased responsiveness.

This study explores the application of Lean principles in the service sector, addressing the complexities of translating manufacturing-focused methodologies to intangible service activities. Lean Services, a relatively recent concept, lacks a standardised definition, leading to varied interpretations ranging from customer-centric approaches to waste reduction strategies. Through a comprehensive literature review and a case study of a European scooter and motorcycle manufacturer, this research identifies a consolidated list of service-specific wastes, bridging a critical gap in Lean Services research. Additionally, the study compares two prominent methodologies—DMAIC (Define–Measure–Analyse–Improve–Control) from Six Sigma and the Cost Deployment pillar from World Class Manufacturing (WCM)—in the context of Lean Services. The analysis highlights DMAIC’s strength in advanced statistical tools and targeted problem-solving, contrasting with WCM’s systemic approach, emphasising economic feasibility and broader resource integration. By examining their individual and combined applicability, this research provides actionable insights for selecting methodologies based on specific objectives, time constraints, and resources. This work contributes to the evolving understanding of Lean Services, offering a framework for practitioners to enhance efficiency and drive continuous improvement in service-based processes.