Healthcare is one of the most complex systems to manage. In recent years, the control of processes and the modelling of public administrations have been considered some of the main areas of interest in management. In particular, one of the most problematic issues is the management of waiting lists and the consequent absenteeism of patients. Patient no-shows imply a loss of time and resources, and in this paper, the strategy of overbooking is analysed as a solution. Here, a real waiting list process is simulated with discrete event simulation (DES) software, and the activities performed by hospital staff are reproduced. The methodology employed combines agile manufacturing and Six Sigma, focusing on a paediatric public hospital pavilion. Different scenarios show that the overbooking strategy is effective in ensuring fairness of access to services. Indeed, all patients respect the times dictated by the waiting list, without “favouritism”, which is guaranteed by the logic of replacement. In a comparison between a real sample of bookings and a simulated sample designed to improve no-shows, no statistically significant difference is found. This model will allow health managers to provide patients with faster service and to better manage their resources.

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.

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.

Risk management and industrial maintenance have evolved into proactive approaches supported by emerging technologies. This article explores the integration of mathematical models and artificial intelligence (AI) techniques in the prediction and prevention of industrial accidents. Through systematic review, predictive modeling, and case study analysis, significant improvements in operational efficiency and occupational safety are evidenced. It is concluded that AI and quantitative models are key tools in the digital transformation of industrial maintenance.