Purpose: This study explores how Industry 4.0 technologies are revolutionizing Formula 1 vehicle performance, race strategy, and operational efficiency. The research examines the impact of Artificial Intelligence, the Internet of Things, Digital Twins, Augmented Reality, and 3D Printing in enhancing real-time decision-making, vehicle reliability, and driver safety. Given the extreme competitiveness of Formula 1, integrating these technologies provides a decisive edge in both performance optimization and sustainability.Method: The research follows a structured three-phase approach. First, a literature review was conducted using specialized motorsport and engineering sources, focusing on the latest applications of AI, IoT, DT, AR, and 3D Printing in Formula 1. Second, a technology integration framework was developed to illustrate how these systems interact within a real-time decision-making environment, combining telemetry data, AI-driven analytics, and predictive simulations. Finally, the impact and potential benefits were assessed, focusing on how these technologies improve race strategy, vehicle reliability, and team efficiency.Findings: The results highlight significant advancements in performance optimization and race management. IoT enables real-time telemetry, allowing teams to monitor tire wear, fuel efficiency, and aerodynamic load, leading to data-driven strategic adjustments. AI models analyze telemetry and radio communications, uncovering competitor strategies and enhancing pit stop timing and race tactics. DTs simulate vehicle behavior, providing teams with pre-race setup optimizations and real-time strategy refinements. AR assists mechanics, reducing repair and assembly times, while 3D Printing allows for rapid prototyping of aerodynamic components, improving vehicle adaptability across different circuits.Conclusions: The integration of Industry 4.0 in Formula 1 is redefining vehicle design, race execution, and team operations. The remaining challenges regard infrastructure costs, technological implementation within budget constraints, and data security. Future research should focus on enhanced AI-driven decision-making, advanced simulation techniques, and real-time multi-sensor fusion to further optimize vehicle performance and competitive edge.

Background: Every company has a supply chain (SC) and must deal with its uncertainty, which can provoke a bullwhip effect; resilience of SCs is a main characteristic to be achieved. However, studies on the creation of digital SCs adopting Industry 4.0 (I4.0) are very scarce and require more attention. Objectives: Industry 4.0 is very little studied in the field of resilience of SCs, despite the huge benefits it can provide. This study aims to evaluate I4.0 to improve both strategic and operational performance. Method: Initially, a deep literature has been carried out to find out the requirements to improve the resilience of a SC and how I4.0 can contribute. Then, a framework has been developed using Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR). Results: The resilience of SC is a very new topic, and I4.0 can provide great benefits. The designed framework can improve resilience by integrating new technologies. Conclusion: Adopting I4.0 into the SC can be challenging, but it is mandatory to integrate it to keep competitiveness high and improve the resilience of the company. Internet of Things can collect data, analysed by AI and made available with AR and VR to operators. Contribution: This study helps in closing the gap between the need of resilience in SC and technological solutions based on I4.0. This improves warehousing, inventory management and demand forecasting with distribution communications and information technology.

Objective: the paper aims to address the surveillance of long-term bedridden patients. Although often necessary, continuous monitoring cannot be carried out due to cost constraints and limited personnel availability. Current solutions involve wearable devices and cameras, but they have limitations, as discomfort and concerns about privacy. Method: firstly, a thorough review of existing literature has been carried out. Then, it was developed the innovative system, focusing on bed monitoring instead of direct patient control. After identifying necessary patient condition data, various sensors were tested. To leverage data potential, the authors explored methods of centralizing and analyzing information. A platform was designed to assist operators in anticipating accidents. Finally, Digital Twins and Cyber Physical Systems were considered for their potential value in the system. Results: The proposed system monitors patient’s bed without direct contact, in contrast to wearable devices. This innovative approach utilizes applied sensors capable of identifying risky behaviors, signs of specific pathologies, unusual movements, tremors, or abnormal humidity in the bed. The benefits include improved service levels, reduced operator surveillance, freeing up time for value-added activities, timely intervention where necessary, prevention of bed falls and sores, detection of wet beds, enhancement of sleep quality, and monitoring of weight trends. Conclusions: Literature indicates that lack of bed surveillance in healthcare is a global issue expected to worsen due to the progressive aging of the population. Authors’ 4.0 solution can effectively address this problem, improving service levels by monitoring and simulating patient behavior rather than direct monitoring, thus minimizing patient discomfort.

The aim is to face the problem of uncertainty of forecasting. It evaluated the integration of Artificial Intelligence (AI) into a simulator to improve its accuracy in the energy prediction, applied to industrial field. Firstly, a literature review on the main applications of AI into energy consumption forecasting was carried out. Then a case study has been taken and Random Forest has been applied to improve forecasting. The AI model improved accuracy of the prediction, being able to consider real-time data of weather and consumption. Therefore, AI has been proved to be successfully implementable for energy forecasting, in synergy with simulation.

Neural Networks are powerful tools and can be easily used for modeling industrial plants. This study refers to a sewage sludge treatment installation where neural networks are implemented in plant design and management support development systems. The initial plant modeling phase compared neural networks with traditional systems based on regressive polynomial meta-models. The subsequent phase presents an innovative method for developing management support tools.

The Authors, in this article, present a case study reporting the management and economic comparison between the traditional methods used for sanitizing confined spaces and an innovative process, performed by trained Operators using a 4.0 machine, created by the same Authors, able to produce and dismiss dry Ozone (thus replicating the Chapman Cycle which happens in the Ozonosphere) and to emit UVC-rays in different wave lengths, so providing distinct functions for surface or surface-fabrics sanitization. The machine represents a significant step forward compared to the current sanitation methods, providing guarantees of absolute sanitization of the treated rooms at decidedly favorable costs. Contrary to traditional methods it is to be noted also the full compatibility with critical environments containing elements like paper or electronics. It makes it possible, as always necessary but even more so in a Pandemic period, to carry out this operation daily, rather than bimonthly as is currently the case in most residences for the elderly. The case study presented compares, on a typical structure, the economic sustainability of such incremental, use of the new technology.

PurposeThe purpose of this research is to provide an effective contribution to contrast the spread of Covid19. Therefore, the authors aimed to model a new strategy (technologies and processes), using the principles made available by Industry 4.0.MethodThe strategy consists in an IoT thermoscanner (developed by the authors, strategically placed throughout the settlement), and an innovative method of disinfection (achieved by redesigning the sanitization processes, using UV-C rays and gaseous Ozone produced by IoT machines, again conceptualized and developed by the authors, being capable of reproducing the Chapman Cycle and its associated benefits). This method was discussed in the article "Sanitizing of Confined Spaces Using Gaseous Ozone Produced by 4.0 Machines," which was presented at the WCE 2021 IAENG Congress (Best Paper Award of the 2021 International Conference of Systems Biology and Bioengineering).ResultThe results consist in: 1. an absolute disinfection system based on a reversible cycle Oxygen-Ozone-Oxygen, with quick re-habitability of the treated rooms, at a minimum treatment costs, without expensive and harmful chemicals or moist water vapor (incompatible by nature with paper and electronics); 2. a 4.0 device for quick detection of fever; 3. clear processes for disease spread prevention.ConclusionThe target contribution was widely achieved, providing machinery, processes and procedures. The authors aim now to extend the solution proposed to any other type virus, bacteria, or pathogen agent introduced by subjects who, despite being unaware of acting as vectors, develop infection along their stay in hotels, offices or any other public place.

Ansaldo Energia is a Major Player in Italy for metal mechanic production which decided to adopt Industry 4.0 standards. One of the main projects, at this purpose, was to develop a new vision concerning Safety. The goal was achieved by a Team built specifically by the Company. Particular importance was assigned by the Team to PPE (Personal Protective Equipment), devices designed to improve safety of the Operators in carrying out their duties. The problem that the Team has clearly warned, given the frequency of occurrence, concerns the periodic maintenance of PPE, to be carried out by law for each device, according to precise rules in order to preserve their efficiency as well as safety certification. Therefore, the Team studied a new methodology, the subject of this paper, based on hardware and software tools designed to monitor the legal revisions and critical deadlines of each PPE in use.

This paper proposes real application example of industrial reorganization based on the use of the Simulation. Simulation development and use is presented as a methodological approach to solve the problem of warehouse reorganization. This is the first step to develop new innovative techniques, obtained through a combination of Artificial Intelligence (AI) methodologies; these new techniques linked to the simulator can become the support to the re-engineering process in a highly detailed scenario.

This paper proposes neural networks together with plant simulations as the right tool for solving complex industrial problems. In this field, the large number of analysis ranges sometimes makes traditional approaches rather difficult to use. Therefore, since it was necessary to use this innovative Simulator/Neural Network combination, a method had to be developed which could match each result with a statistical evaluation and a correct estimate of its specific reliability. The proposed application of this approach is very effective in determining the critical elements of a real plant.

In this paper which is the extended version of the paper presented at Sysint 2020 and published by Springer on Proceedings [10] the authors address the problem of surveillance of bedridden patients in healthcare, who cannot be supervised by operators 24 hours a day, given the associated costs. This problem is already faced by wearable devices, with some limits. The system proposed consists in monitoring the bed, instead of the patient, through applied sensors. By centralizing and analyzing the data collected it is possible to inform the operative center of the occurrence of risky events. The scope is preventing such risks or mitigating their effects with a real time intervention. Important 4.0 features are added to the new release presented in this extension, like Digital Twins and Cyber Physical Systems enhanced with Artificial Intelligence. The case study proposed in the original paper has been updated with new figures, widely improving the sustainability of the initiative.

A major player in metal mechanic manufacture in Italy that has opted to implement Industry 4.0 standards. One of the key projects undertaken was the creation of a new vision for safety. The aim was met by a team created expressly for the company. The Team placed a high value on PPE (Personal Protective Equipment), which is equipment meant to increase the safety of the Operators while doing their responsibilities. The problem that the Team has clearly warned about, given the frequency of occurrences, is the periodic maintenance of PPE, which is required by law for each device and must be carried out according to strict guidelines to maintain their efficiency and safety certification. As a result, the Team investigated a novel methodology, the topic of this article, based on hardware and software technologies designed to monitor the legal revisions and crucial dates of each piece of PPE in use.

The paper describes how, on behalf of Ansaldo Energia Spa, a multidisciplinary team developed a methodology based on Industry 4.0 technologies, an approach that allows rescue teams to quickly intervene in the event of a man-down in isolated areas of the plant, where the unfortunate person would risk being found with significant delay and consequent problems for his physical well-being. Under the supervision of the team, a highly specialized supplier created a suitable hardware and software device to achieve this outcome. Such a device can immediately warn rescue crews in real time as soon as an incident occurs, as well as geo-locate the man on the ground with exceptional precision. Once developed, the approach was standardized in a set of sequential and generic procedures in order to make it adaptable to any sort of firm, construction site, or workshop where a man-down event may happen. The methodology is set up as a real toolkit to protect operators from severe damage that can result from long waits for rescue teams, whenever operators experience negative events for their safety being them exogenous (fainting illnesses, heart attacks, epileptic attacks, strokes, etc.) or endogenous (accidents in the workplace).

Ansaldo Energia is a Major Player in Italy for metal mechanic production which decided to adopt Industry 4.0 standards. One of the main projects, at this purpose, was to develop a new vision concerning Safety. The goal was achieved by a Team built specifically by the Company. Particular importance was assigned by the Team to PPE (Personal Protective Equipment), devices designed to improve safety of the Operators in carrying out their duties. The problem that the Team has clearly warned, given the frequency of occurrence, concerns the periodic maintenance of PPE, to be carried out by law for each device, according to precise rules in order to preserve their efficiency as well as safety certification. Therefore, the Team studied a new methodology, the subject of this paper, based on hardware and software tools designed to monitor the legal revisions and critical deadlines of each PPE in use.

In the repeated interventions carried out by the authors in the healthcare sector [1–4] (hospitals, outpatient clinics and clinics), including assistance facilities (residences for the elderly and outpatient medical offices) the problem of so-called hospital or nosocomial infections has always been reported to the team by the medical and nursing staff. Starting from an age-old experience of sanitization of confined environments, achieved by the authors by using a 4.0 machine, for the production of gaseous ozone and UVC rays [8], it was required to the team to extend the benefits achieved to the healthcare sector. This goal was possible by generating a dedicated approach, for an effective action to combat this serious problem of global significance. The machine mentioned was conceptualized, designed and developed by the authors by specific Engineering 4.0 methodologies, meaning with this term the use of all Engineering technologies, techniques, software, tools, and devices characterizing the fourth industrial revolution.

In this paper the authors address the problem of surveillance of bedridden patients in hospitals and residences for elderly. Unfortunately, patients cannot be supervised by operators 24 h a day, given the associated costs. An attempt to solve this problem is already provided by wearable devices. This paper describes a 4.0 system implemented to overcome the limits (identified by interviewing a sample of nurses belonging to different facilities) of the wearable devices available on the market. The system proposed consists in monitoring the bed, instead of the patient, through applied sensors. By centralizing and analyzing the data collected it is possible to promptly inform the operative center of the occurrence of risky events to which bedridden patients are normally subjected. The scope of the system is preventing such risks, where possible, or mitigating their effects with a real time intervention. A case study on an active facility, conducted as a pilot project, confirms the humanitarian and economic benefits for patients and facility.

A fundamental aspect of the fight against the Coronavirus and against any other virus, is represented by the sanitization of the sites and objects contained therein. This operation is normally carried out using mixtures of ozone and steam and it is certainly effective but also limited due the damages that the vapor can cause to rooms and objects. The following paper introduce machines able to overcome this issue thanks to innovative systems based on the principles of Engineering 4.0. Those systems reproduce the Chapman cycle in the to-be sanitized environments which allows producing ozone in a gaseous state, in the proper quantity and for the time necessary for sanitization. At the end of the operation, the ozone will be converted back into oxygen, leaving the environment re-habitable by humans and pets in a short time. The operation has low costs and times and guarantees positive results. This is therefore a real revolution to be considered today against the COVID-19. © 2021 Newswood Limited. All rights reserved.

Despite the number of scientific contributions on the topic, there are still several challenges to be faced before the principles of Industry 4.0 become widely applicable. This study aims precisely to overcome some of the limitations of existing technologies and applications for material handling and picking. The current market solutions often require high investment costs being unsustainable by small and mediumsized enterprises and suitable exclusively for managing large volumes and a mix of highly diversified products. In this study, the authors aimed to conceptualize and design an automatic order fulfillment system applicable to small and medium-sized companies performing frequent shipments characterized by low volumes, variable product mix, reduced overall dimensions for products and products typically sold in bulk, such as those operating in the beauty and cosmetics sector. The proposed solution consists of a series of smart drawers, controlled by a communication architecture designed in 4.0 Logic, equipped with hardware and software interfaces that can be easily integrated with any existing management or departmental system. By the Cloud and WEB Portal, the warehouse thus conceived can be monitored and controlled in real-time from any part of the Globe. The solution is completely modular and easily adaptable to future changes in quantities and mixes in stock. The proposed system, as demonstrated by the cost vs. benefit analysis conducted on a real case, allows getting a significant saving in terms of manpower and space, as well as a strong increase in terms of precision, efficiency in order fulfillment and safety.

The Vehicle Routing Problem (VRP) is one of the most optimized tasks studied and it is implemented in a huge variety of industrial applications. The objective is to design a set of minimum cost paths for each vehicle in order to serve a given set of customers. Our attention is focused on a variant of VRP, the capacitated vehicle routing problem when applied to natural gas distribution networks. Managing natural gas distribution networks includes facing a variety of decisions ranging from human resources and material resources to facilities, infrastructures, and carriers. Despite the numerous papers available on vehicle routing problem, there are only a few that study and analyze the problems occurring in capillary distribution operations such as those found in a metropolitan area. Therefore, this work introduces a new algorithm based on the Saving Algorithm heuristic approach which aims to solve a Capacitated Vehicle Routing Problem with time and distance constraints. This joint algorithm minimizes the transportation costs and maximizes the workload according to customer demand within the constraints of a time window. Results from a real case study in a natural gas distribution network demonstrates the effectiveness of the approach.