Industry 4.0 is a new paradigm in the manufacturing world and it has deeply changed the Human-machine interaction. This paper focus is on the nature of this interaction, which is made possible thanks to the Internet of Things (IoT), and Cyber-Physical System (CPS). These Industry 4.0 key technologies are studied related to the standard Deming cycle, in order to underline the importance of Humanmachine interaction. The Fourth Industrial Revolution involves several changes in the workforce's key features. In this paper, a new perspective based on the centrality of humans is given in the new Industry era. The importance of the human factor will be deeply studied through the implementation of the 'Sand Cone Model'. A new framework is proposed in order to explain the quality measures addiction on the workforce quality skills, and how it engraves on improving efficiency and effectiveness of an industrial process. ARTICLE HISTORY

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.

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.

In the highly technological and innovative scenario of Industry 4.0, characterized by a series of enabling technologies and a strong interconnection of resources, it is necessary to take into account the impact that the introduction of increasingly sophisticated sensors and collaborative machines on the safety aspects. In addition to the introduction of so-called "smart" technologies, the context of the use of new technologies and the tasks of front-line operators has also changed. The worker increasingly assumes the role of supervisor and when some types of work require particular human skills, there is a real "collaboration" between man and machine. In the new factories, the interaction with "smart machines" on one hand simplifies the operations of the worker making them less complex and less susceptible to errors and on the other hand increases the information and communication of these systems and leads to a complexity that requires new man-machine interface modes. The support of sensors and new technologies allows the detection of a series of data necessary to make the most reliable predictions on the state of health of the equipment so that it is possible to plan target interventions. This implies that the cognitive interaction effort of the machine operator moves from the skill level to the knowledge level because the human is required to manage a huge amount of data (big data) that must be acquired, analysed and interpreted. This paper, starting from consolidated human reliability methodologies in the literature, which allows for evaluating human error in different work fields, aims to highlight how human performance improves even if it implies an increase in cognitive demand due to the use of new smart technologies.

While a significant number of companies around the world are still trying to adapt to Industry 4 [...]

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The Failure Mode and Effect Analysis (FMEA) is a useful instrument born in the aerospace industry and widely used to improve a process or product's efficiency. Over the years, this instrument has been adopted in increasingly different contexts, such as HealthCare. This paper proposes an approach aimed at improving the defects typical of the classic FMEA in the design phase, that is, in a scenario full of uncertainties and with little information available, using a new hybrid Multiple‐criteria decision‐making (MCDM) method in order to obtain a priority index more performant than Risk Priority Number (RPN). In the proposed method, the three assessment criteria have a different weighting in the index's final computation, differently from the classical RPN. These weights are obtained with a scientific technique, thus avoiding that excessive subjectivity influences the final result. A more efficient priority index is obtained through a new hybrid approach that solves some classical RPN gaps. A case study concerning an endoscope Ear Nose Throat Entropy (ENT) prototype is examined to illustrate the proposed method. FMEA analysis in HealthCare is increasingly used for its flexibility and reliability. This study focuses on using new techniques to eliminate certain defects or exploit some qualities better. The use of a robust and elastic innovative MCDM method to calculate a new priority index and a scientific technique to obtain the weight of the selection are the interesting insights proposed in this paper.