In light of the Fourth Industrial Revolution, the concepts of flexibility and re-configurability of manufacturing systems and the evolution of their control architectures are becoming increasingly important. The development of Cyber Physical Systems (CPS) and their flexibility and integrated capabilities have paved the way to the transition from centralized control to heterarchical (decentralized) control architectures. In this paper, a comparison between centralized and heterarchical control architectures in a virtual learning environment is presented. The control architectures of the assembly station and the materials handling system of modern manufacturing systems have been conceptualized and tested under different working conditions. The results show that centralized control is the best solution only for deterministic and predictable scenarios, which are very far from reality, whereas, in case of failures, a more flexible control is preferable.

Data Centres serve as the foundation for digital technologies in the energy sector, enabling advanced analytics, optimization, and automation. However, their rapid growth can exert a substantial influence on the environment due to their energy consumption, water utilization, and production of electronic waste. This research begins with an energy overview of the setup and operations of data centres, highlighting their key components and infrastructure, and emphasizing their crucial role in managing energy resources and driving the energy sector’s digital technologies. Building upon this understanding, a holistic framework is proposed to tackle energy sustainability concerns in data centres, with a focus on energy-related aspects. The framework places emphasis on three primary sustainability metrics, namely energy efficiency, water consumption, and waste management. It underscores the significance of green building design principles and energy-efficient equipment as crucial constituents of sustainable data centre infrastructure. The framework delineates optimal energy operational best practices encompassing virtualization and consolidation, effective cooling tactics, and energy management and monitoring, all aimed at reducing energy consumption and enhancing energy performance. Furthermore, the framework emphasizes the significance of incorporating energy-related sustainability metrics into decision-making procedures and adhering to regulatory standards for energy efficiency. Through adherence to this framework, data centres’ environmental impact can be mitigated and a positive contribution towards a sustainable future can be made, particularly in the realm of energy conservation and optimization.

Purpose: The purpose of this paper is to propose an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks (OOSs) by jointly formulating price policies and forecasting retailer’s demand. Design/methodology/approach: The framework is based on the cascade implementation of two artificial neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a real wholesale supply chain. Findings: The application of the ANN framework to a real wholesale supply chain shows that the proposed methodology has the potential to decrease economic loss due to OOS occurrence by more than 56 percent. Originality/value: The combined use of ANNs is a novelty in supply chain operation management. Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to more dependable demand forecasts.

This paper proposes a stock dynamic sizing optimization under the Logistic 4.0 environment. The safety stock is conceived to fill up the demand variability, providing continuous stock availability. Logistic 4.0 and the smart factory topics are considered. It focuses on vertical integration to implement flexible and reconfigurable smart production systems using the information system integration in order to optimize material flow in a 4.0 full-service approach. The proposed methodology aims to reduce the occurring stock-out events through a link among the wear-out items rate and the downstream logistic demand. The failure rate items trend is obtained through life-cycle state detection by a curve fitting technique. Therefore, the optimal safety stock size is calculated and then validated by an auto-tuning iterative modified algorithm. In this study, the reorder time has been optimized. The case study refers to the material management of a very high-speed train.

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.

In a safety management system (SMS), risk management plays a key role for the prevention of accidents. This study aims to propose a safety management model by using a system dynamics approach applied to the Bhopal incident. The model proposed in this paper discusses the relationships among the main causes that have contributed to the occurrence of the incident studied, such as broken safety devices, inadequate personnel experience, operator decisions, manager production strategy, policy decision, as deduced from the relevant literature about the Bhopal incident dynamics. The simulation and the results clarify how to manage the key factors to make the plant safe. Moreover, we want to underline how these key modern aspects of safety could be enhanced by using the enabling technologies of industry 4.0.