The situation in the hard coal industry in Poland is forcing the identification of effectual and practical indicators of the effectiveness of machinery and equipment. In the article, the authors discuss the possible use of the OEE indicator for the evaluation of production processes in hard-coal mines. In summary, recommendations are made to enable efficiency assessment of mining machinery using the OEE.

In the course of management, three basic resources are used: material, financial and human resources. However, the last of the above resources - employees, is a demographic and unpredictable resource that rarely performs what is required by the employer or supervisor, loyally and with appropriate commitment. Therefore, effective methods of motivating employees are needed, and in mining enterprises with a small range of other incentives, the most common incentive should be a financial factor. For this to happen a remuneration structure should include, among others, a variable bonus based on the work results obtained by an employee or related to the effects of the entire enterprise. With rational rules for its allocation, it can be one of the key elements of improving production efficiency in mining enterprises. Well-designed remuneration should stimulate employees to work and develop. Analysing the structure of an average wage of a physical underground worker in a randomly selected mining company in Silesia, such pro-effective structures have not been found, which is a very unfavourable situation. In addition, in the search for the incentive nature of these wages, the changes in their amount have been analysed in the light of changes in the volume of hard coal production and labour productivity in hard coal mining in Poland.

The market economy means that only those companies that are characterised by the generation of positive economic results and liquidity can function, survive and thrive. Due to the importance of the coal industry in economic and social terms—due to the number of people employed in the coal industry—it is necessary to constantly search for methods to improve management and business efficiency. This paper proposes the use of artificial neural networks to group mines into sets of similar mines. These sets can be used to make different business decisions for these companies. These sites can be easily compared with each other, in search of the areas that need to be restructured. In addition, developing pro-efficiency strategies for designated groups of similar mines is simpler than for each mine individually. This reduces the number of such studies in real terms and allows effective business measures to be applied more quickly.

A work accident comprises a number of interrelated adverse technical, organizational, environmental, and human events. However, it has been claimed that the main contributing factor responsible for work accidents is a human error (disruption). The causes of human errors may include: incapacity, nature of tasks being performed as well as physical and social environment. It is important to note that the first element is strongly related to both age and seniority. Based on available literature, the impact of seniority on work accidents is not clearly defined. Advanced age of employees favors the acquisition and consolidation of professional knowledge, but it can also be associated with greater automation of behaviours in work environment. This can lead to an error resulting in a dangerous situation or accident. Bearing in mind the above, this study examined the relationship between seniority and the number of work accidents among individuals employed in mining enterprises. The study was carried out using the Pearson's correlation coefficient. The source of analysis included statistical data on the size of employment in individual seniority groups and the number of work accidents in mining enterprises between 2003-2017. The research showed a correlation between age and the number of work accidents in hard coal mines.

Entrepreneurs carrying out mining works under seismic hazard conditions are obliged to conduct studies in the field of engineering geophysics, including measuring, interpreting and evaluating the effects of rock mass tremors on ground vibration parameters, and thus the occurrence of harmful impacts on surface objects. However, for technical reasons, this is a difficult task to implement at all points subject to the influence of mining activities. Therefore, it becomes expedient to look for solutions that would provide greater accuracy in forecasting the distribution of ground vibration parameters. This paper proposes a method for forecasting the distribution of peak ground accelerations (PGAs) induced by mining activities, taking into account the directionality of vibration attenuation. In many cases, the explanation of the variation in the magnitude of recorded ground surface vibrations after a rock mass tremor cannot always be explained by only the variation of epicentral distances and the value of the vibration amplification factor by quaternary formations. Therefore, it is reasonable to take into account the directionality of vibration attenuation. The authors analyzed and evaluated the accuracy of predicting the distribution of ground vibration accelerations induced by mining activities, taking into account the directionality of vibration attenuation, using three models: the first, a classical model assuming isotropic vibration attenuation; the second, a model taking into account the anisotropy of vibration attenuation with elliptical isolines; the third, a model without assuming the shape of the isolines of vibration intensity parameters. For both models that took into account anisotropy of vibration attenuation, better results (more accurate descriptions of observed ground vibration accelerations) were obtained than for the model assuming isotropy. The most accurate estimates of vibration magnitude were obtained using the latter model.