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.

In this paper, an assessment of the failure frequency of machines in a series-parallel structure was conducted. The analyses contain the decomposition of the system according to the general theory of complex systems. Based on the results obtained, a model for an optimal determination of the mean time to failure (MTTF) according to the expected value of the gamma distribution was proposed. For this purpose, the method of moments was used to determine the optimal values of the parameters of the estimated gamma distribution. The article is designed to be analytical. The object of consideration in this analysis is the real production system working in accordance with make-to-order, with a high degree of product customisation. Moreover, in the considered system occurs a dichotomy of mutually exclusive flows: push and pull. In the article, the main emphasis was placed on the applicability of the proposed MTTF value-shaping algorithm. Then, the maintenance strategy for each machine (reactive, preventive or predictive) was proposed. Maintenance strategy selection considered sustainable development principles in the criterion of minimizing maintenance actions, fulfilling the assumption of not interrupting the flow of the processed material. Based on inductive analyses, the concepts of improvement actions individually for each machine in the analysed subsystem were deductively defined. As a result, it was proved that a reactive maintenance strategy is appropriate for machines that have manufacturing reserves and are low priority. The equipment possessing manufacturing reserves but also having an impact on the risk of interrupting the flow of the processed material should be operated in accordance with a preventive maintenance strategy. A predictive maintenance strategy was proposed for the machines with the highest priority, which simultaneously do not have manufacturing reserves and the risk of manufacturing line operation interruption is high. The considerations were conducted with a holistic approach, taking into account the main functional areas of the enterprise.

The increasing number of heavy-duty vehicles (HDVs) on roads has become a major contributor to harmful emissions, posing critical environmental challenges and exacerbating global warming. This study aims to establish correlations between road types and the emissions they generate, offering actionable insights for logistics planning and strategies to mitigate diesel vehicle emissions. The analysis is based on input data from a selected transport company, covering parameters such as vehicle type, average mileage, speed, and driving style, as well as environmental conditions like ambient temperature and humidity. Emissions and energy consumption levels are estimated using the COPERT model. A key research challenge involves accurately predicting and managing air pollution caused by HDVs under varying vehicular, technological, and fuel conditions, as well as fluctuating atmospheric and operational factors. The findings indicate that highway driving produces the highest emissions of pollutants such as Se and Zn, while urban peak hours record the highest levels of NO_x, NO, and NO₂. These results emphasise the critical role of strategic route selection in reducing total emissions and managing levels of individual harmful substances. This research highlights the importance of integrating sustainable practices into transport planning to reduce environmental impacts, align with global climate objectives, and advance sustainable development in the transport sector.

Manufacturing a variety of final products in small (short) production series – requires the production systems to be flexible and to adapt almost instantly to the quickly changing customer requirements. Dynamics of changes and no predictability of system’s state in time t+1 lead to increase of the operational costs. It particularly affects those companies which are producing goods using make – to – order method in short series. This article describes research around a product for which 3 production technologies exist. The examined flow stream variants take into account a resources load with the tasks resulting from clients orders placed within Δt time. The goal of this article is to establish the parameters being the main determinants of production structure fluctuations.