Marco Mosca

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.

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.

Industry 4.0 represents the main paradigm currently bringing great innovation in the field of automation and data exchange among production technologies, according to the principles of interoperability, virtualization, decentralization and production flexibility. The Fourth Industrial Revolution is driven by structural changes in the manufacturing sector, such as the demand for customized products, market volatility and sustainability goals, and the integration of artificial intelligence and Big Data. This work aims to analyze, from a bibliometric point of view of journal papers on Scopus, with no time limitation, the existing literature on the application of AI in SMEs, which are crucial elements in the industrial and economic fabric of many countries. However, the adoption of modern technologies, particularly AI, can be challenging for them, due to the intrinsic structure of this type of enterprise, despite the positive effects obtained in large organizations.

Background: Industry 4.0 (I4.0) technologies have transformed supply chain (SC) logistics and production. However, their environmental impact, particularly on CO₂ emissions and carbon footprints, remains underexplored. This study examines the impact of I4.0 tools on SCs sustainability, focusing on reducing carbon footprints. Methods: A bibliometric analysis was conducted in October 2023 to quantitatively evaluate the scientific literature, examining publication characteristics to assess current research and forecast future trends. The Scopus database was utilized with specific filters to identify studies on the impact of I4.0 technologies in SC domains on sustainability, focusing on CO₂ emissions and carbon footprint reduction. VOSviewer software version 1.6.15 was used to analyze selected papers, revealing key keyword clusters and relationships. Results: Five clusters were identified, offering insights for supply chain managers and highlighting links between I4.0 and CO₂ reduction in supply chains: “LCA towards zero carbon”, “Supply chain carbon footprint”, “Risk and decarbonization analysis”, “Industry 4.0 and stochastic models for sustainability”, and “Biodiversity and environmental impact”. Key findings emphasize the strong connection between LCA, carbon footprint analysis, emission control, and the role of I4.0 technologies like blockchain and IoT in reducing emissions. Conclusions: This study highlights the environmental benefits of I4.0 in SC management, supporting global decarbonization goals.

Purpose: The 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. Method: The 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). Result: The 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. Conclusion: The 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.

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.