Digitalization of production environment, also called Industry 4.0 (the term invented by Wahlster Wolfgang in Germany) is now one of the hottest topics in the computer science departments at universities and companies. One of the most significant topics in this area is augmented reality (AR). The interest in AR has grown especially after the introduction of the Microsoft HoloLens in 2016, which made this technology available for researchers and developers all around the world. It is divided into numerous subtopics and technologies. These wireless, see-through glasses give a very natural human-machine interface, with the possibility to present certain necessary information right in front of the user’s eyes as 3D virtual objects, in parallel with the observation of the real world, and the possibility to communicate with the system by simple gestures and speech. Scientists noted that in-depth studies connected to the effects of AR applications are presently sparse. In the first part of this paper, the authors recall the research from 2019 about the new method of manual wiring support with the AR glasses. In the second part, the study (tests) for this method carried out by the research team is described. The method was applied in the actual production environment with consideration of the actual production process, which is manual wiring of the industrial enclosures (control cabinets). Finally, authors deliberate on conclusions, technology’s imperfections, limitations, and future possible development of the presented solution.

The new generation Manufacturing Executions System (MES) is considered as one of the most important solutions supporting the idea of Industry 4.0. This is confirmed by research conducted among companies interested in the implementation of the Industry 4.0 concept, as well as the publications of researchers who study this issue. However, if MES software is a link that connects the world of machines and business systems, it must take into account the specifics of the supported production systems. This is especially true in case of production systems with a high level of automation, which are characterised by flexibility and agility at the operational level. Therefore, personalization of the MES software is proposed for this class of production systems. The aim of the article is to present the MES system personalization method for a selected production system. The proposed approach uses the rules of Bayesian inference and the area of customisation is the technological structure of production, taking into account the required flexibility of the processes. As part of the developed approach, the variability index was proposed as a parameter evaluating the effectiveness of the production system. Then, the results of evaluation of the current system effectiveness by use of this index are presented. The authors also present the assumptions for the developed MES personalization algorithm. The algorithm uses the rules of Bayesian inference, which enable multiple adjustments of the model to the existing environmental conditions without the need to formulate a new description of reality. The application of the presented solution in a real facility allowed for determining production areas which are the determinants of system instability. The implementation of the developed algorithm enabled control of the generated variability in real time. The proposed approach to personalization of MES software for a selected class of production systems is the main novelty of the presented research and contributes to the development of the described area of research.

The next industrial revolution, which coincided with the COVID-19 pandemic, is prompting a different look at the issue of supply chain change management. A new perspective should take into account the aspect of supply chain efficiency at multiple levels. Efficient logistics is green and energy-saving, both of which need to be systematically integrated with the logistical planning processes. The dynamic changes on the demand and supply side resulting from social, political, and economic transformations have significantly influenced the shaping of long-term supply chains. The development of new manufacturing and logistics technologies prompts the development and implementation of new integrated planning methods to support supply chain management processes. Modern supply chains are oriented towards operations in a dynamically changing socio-economic environment. The new methods are capable of incorporating dynamic adaptation of logistics infrastructure which respond to changing relationships between supply and demand. To meet the identified problems of complexity, relevance, and time-consumption of the logistic planning process in modern enterprise management, the PL9A method of logistic planning and 9A LOGPLANNER application for logistics planning were developed. The article presents the results of experimental and simulation studies on the improvement of logistic processes in a working manufacturing enterprise with application of the PL9A method. The results of the experimental work indicate that the application of the PL9A method embedded in the 9A LOGPLANNER software makes it possible to dynamically simulate any number of logistics system variants in a short period of time, while reducing risk and obtaining tangible benefits in terms of energy and ecological efficiency.

This study addresses the critical gap in the literature regarding the energy efficiency of intermodal terminals in smart cities, mainly focusing on crane operations during train loading processes. Novelty’s contribution lies in developing a comprehensive simulation model in FlexSim, where quantitative analysis of crane energy consumption, factoring in container location in the storage yard, rehandling operations, and crane movement strategies were performed. Moreover, the analysis of hoist, trolley, and gantry movements was performed to evaluate their impact on overall container loading process energy efficiency. The findings reveal that the choice of train loading method significantly influences crane energy consumption, thereby affecting the operational costs, environmental footprint, and energy efficiency of the logistics hub in the form of an intermodal terminal. This research provides a methodology for assessing and enhancing the energy efficiency of intermodal terminals and highlights the broader implications for smart city sustainability goals, including reduced greenhouse gas emissions, lower operating costs, and improved transportation infrastructure. The outcomes of this research can possibly support smart city planners and logistics managers in making informed decisions to optimise intermodal terminal operations, contributing to urban areas’ sustainable development and economic resilience.