This article uses Markov and semi-Markov models as some of the most popular tools to estimate readiness and reliability. They allow to evaluate of both individual elements as well as entire systems—including production systems—as multi-state structures. To be able to distinguish states with varying degrees of technical readiness in complicated and complex objects (systems) allows to determine their individual impact on the tasks performed, as well as on the total reliability. The application of the Markov process requires, for the process dwell times in the individual states, to be random variables of exponential distribution and the fulfilling Markov’s property of the independence of these states. Omitting these assumptions may lead to erroneous results, which was the authors’ intention to show. The article presents a comparison of the results of the examination of the process of non-parametric distribution with an analysis in which its exponential form was (groundlessly) assumed. Significantly different results were obtained. The aim was to draw attention to the inconsistencies obtained and to the importance of a preliminary assessment of the data collected for examination. The diagnostics of the machine readiness operating in the studied production company was additionally performed. This allowed to evaluate its operational potential, especially in the context of solving process optimization problems.

The number of shipments is growing every year, and as a result, new transport companies arise. The increase in competition requires from entrepreneurs to apply solutions increasing the level of services provided in order to best satisfy the needs of the customers. In this aspect, minimizing the time of deliveries is extremely important, and it can be achieved, for example, by implementing the cross-docking method. It consists in consolidation of cargo from different shipment locations that is delivered in the same direction. The main feature of the above method is to keep the labor intensity of operations and the interference in the cargo to the minimum. The purpose of this article is to present a research on a logistic operator working based on a cross-docking warehouse with a capacity significantly lower than the average daily quantity of shipments handled. This requires both effective management of the available space and minimizing the time spent on manipulation activities. Therefore, it is important to know the expected number of parcels that are planned to be received and shipped on a given day in order to coordinate the work in the warehouse. It is possible to estimate it by using mathematical methods of forecasting. One of them - the multiple regression - is presented in this article. The calculations were made on the basis of collected empirical observations concerning orders for pallet spaces placed by customers. Such a forecast allows for improvement of the processes of planning and management of the possessed resources. It allows to adjust the number of warehouse workers or vehicles necessary for internal transport to the expected needs. Ultimately, it may translate into more efficient functioning not only of the surveyed branch, but also of the whole network.

Production companies operate in a complex economic, technological, social and political environment. There are a number of factors contributing to a satisfactory market position, the most important one being a properly defined and implemented strategy. It needs, however, to be continuously monitored and, if necessary, modified. One of the elements subject to such evaluation is the efficiency of the production processes, which has become the genesis of this article. In response to the methods presented in the literature, a proposal using the logistic regression method for this purpose is presented. The dichotomous form of the dependent variable makes it possible to make such an evaluation in an unambiguous manner and to determine the significance and influence of selected factors on the result thereof.

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Purpose: The article aims to analyze the opportunities and threats stemming from remote learning, to assess the impact of the use of modern technologies on the quality of the education process, as well as to study the level of students' satisfaction with distance learning. Design/Methodology/Approach: The deduction method was used to assess the observations made in connection with the introduction of restrictions related to higher education in individual countries worldwide, while the induction method was used to formulate conclusions based on the relevant information collected. The empirical observation method was used to diagnose the main problems resulting from remote education and was also relied upon to examine the potential use of modern technologies to improve the quality of the process. Data was collected based on a survey concerned with the participation of logistics students in classes conducted remotely. Findings: The surveys conducted aimed at assessing the organization and conducting classes in the remote mode, based on modern educational methods. The research conducted allows us to perform a comprehensive evaluation of the quality of remote education of logistics students and determine its impact on acquiring knowledge and practical skills. The research provides information concerning the impact that modern training technologies have on the level of students' training and the expansion of their professional competence and experience. Practical Implications: The present article will make it possible to assess the functioning of higher education institutions in the pandemic and show opportunities and threats for distance education and perspectives for the future functioning of higher education. Originality/Value: The article presents an analysis of opportunities and threats and examples of good practices and recommendations, which can be followed if classroom learning continues to be suspended.

Urban transport systems operate according to fixed, strict timetables, which requires high timeliness and technical readiness of the fleet. Therefore, this article proposes a detailed study of the punctuality of the public transport system using a multiple regression model for the main modes of transport (trams, buses, and Warsaw Metro). The analysis made it possible to go beyond the framework of the overall assessment and to identify the factors that have a significant effect on the punctuality index and to indicate the degree of this effect. The obtained results are a universal tool to assess the punctuality level of the urban transport fleet and to support decision making in the scope of organization of their work, which can be implemented in any similar transport system. The specification of the number of breakdowns, road accidents, or unauthorized stopping of a vehicle as the main causes of delays is the basis for taking corrective measures related to the improvement of the fleet operation system, or for preventive measures. The development of such models is practical in both public transport systems and similar companies providing transport services. For such institutions, the parameter of punctuality is extremely important and affects the quality of the services offered and the reputation of the company, which translates into the number of customers and potential profit. Therefore, it is important to investigate the factors that shape the punctuality of the tasks performed. It allows for shaping the processes of fleet control and management. It is also worth emphasizing the scientific aspect of the publication, which is the presentation of the possibilities of applying selected mathematical models in such analyses, indication of the conditions of their application, and presentation of possible results together with their interpretation.

One of the main threats to ecological safety is the increased emissions of greenhouse gases. Promoting the purchase of electric vehicles and increasing their share among all cars in a given country can be considered as activities reducing the emissions of CO2 into the atmosphere. Based on Environmental Performance Index, in 2021, Poland is in 37th place among the most climate-friendly countries in the world, and 30th among similar countries in Europe. The aim of the article was to model the prices of electric vehicles as one of the elements of promoting climate security in Poland. For the purposes of the study, an analysis of data from electric vehicle sales advertisements on one of the Polish automotive services was carried out. Moreover, on this basis, the most important factors influencing the price of the vehicle were analyzed. For this purpose, forecasting models were built based on neural networks and selected models of decision trees based on the CART algorithm, boosted trees, and random forest. We assessed the developed models and compared their prognostic abilities.

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