The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO2-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality.

Comprehensive frameworks for end-to-end digital supply chain planning are leveraging on 3D simulation and digital twin technologies to enhance production process design and planning also in small and medium enterprises (SMEs). The proposed system empowers production engineers by facilitating bill of material (BOM) generation, production routing, and fostering a collaborative co-design approach with external suppliers. Inspired by Product Lifecycle Management (PLM) principles, the framework is particularly geared towards quality management, emphasizing work instruction generation, and providing a seamless Industrial Internet of Things (IIoT) digital representation of the production process during the execution phase. The integration of these technologies not only optimizes production efficiency but also promotes a holistic and collaborative approach to manufacturing, ensuring SMEs can navigate the challenges of modern digital supply chains with agility and precision.

Workplace safety remains a global concern, with millions of occupational accidents occurring annually. Industry 4.0 (I4.0) introduces IIoT, Digital Twins, Cyber-Physical Systems, and wearable devices that can enhance workplace safety by enabling real-time monitoring, predictive analytics, and automated risk mitigation. This study proposes a preliminary conceptual Workplace Safety 4.0 Framework that integrates these technologies to proactively reduce accidents, improve operational resilience, and promote sustainability. A systematic literature review identified key technological contributions, highlighting IoT as the most widely implemented solution, followed by Augmented Reality AR, VR, CPS, DTs, and blockchain. The proposed framework addresses existing gaps by offering a structured, scalable approach applicable across multiple industries. The findings demonstrate that integrating I4.0 technologies enhances safety, reduces operational disruptions, and fosters a safer and more efficient work environment. Future research should explore AI integration and sector-specific applications for further advancements.

The aim of this study is to present the current achievements and identify potential gaps in human-centricity and social sustainability within the Triple Bottom Line (TBL) framework in Supply Chains (SC), particularly through the adoption of Industry 4.0 (I4.0). A systematic literature review was conducted, with the findings categorized by social sustainability aspects and technological focus. Given the recent nature of this topic, most studies mainly focus on a single I4.0 technology. Additionally, barriers to the adoption of I4.0 in SCs are highlighted. As a result, there is a need for a comprehensive framework that integrates multiple I4.0 technologies to enhance sustainability in SCs. Copyright (c) 2025 The Authors.

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.

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.

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.

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.

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.

This study aims at investigating the economic viability, at the pre-feasibility level, of a 5 MW electrolyser base-methanol production plant, coupled with a PV power plant. The Authors investigated the impact of different parameters, such as the PV plant size, the electrical energy cost and the components capital costs on the methanol production cost and on the system economic viability. It was also analyzed the minimum recommended sale price of the methanol in order to assure an adequate time frame for the return of the investment, considering a different combination of the investigated parameters. An economic sensitivity analysis, based on the RSM approach, was performed in order to define the most promising economic conditions under which the plant can be considered a profitable investment in terms of ARR. A guide for an economically viable plant design, allowing for the identification of the most suitable combination of the economic parameters, was proposed as a kind of "maps of existence". For the reference case, the Methanol Production Cost (MPC) resulted around 324 (sic)/ton and the minimum methanol sale price to achieve a PBP of 10 years. The sensitivity analysis identified the cost of electricity and the capital cost of the electrolyser as the most affecting parameters for the system economic viability. In terms of ARR, the methanol price represents the most significant factor. Considering a methanol sale price ranging between 400 and 1200 (sic)/ton, the ARR varied from 5% (20 year of PBP) to 20% (5 years of PBP). From the environmental point of view, it is worth underling that the methanol production plant here proposed allows to recycle about 5800 tons of CO2 per year and to avoid the consumption of about 5.2 MNm(3) of NG per year (compared to the traditional production). (C) 2019 Elsevier Ltd. All rights reserved.

The problem of sizing and managing contingency reserve is always critical in project managementProject Management , because of its impact on the project margin. A correct assessment of the contingency reserve to be allocated is, therefore, a main requirement to lead to success the project manager actions. In this research, the Authors propose an innovative Decision Support SystemDecision Support System to size, starting from an objective phase of risk assessment, the correct contingency reserve. The proposed solution provides the project manager a clear vision of the residual risk of cost overruns to be managed. The Decision Support SystemDecision Support System uses Failure Mode Effect Analysis and Monte Carlo SimulationMonte Carlo Simulation .