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Transport and mining machinery cabins are still not well adapted to their users, while checklists for their evaluation are not common in the literature. This article proposes a new checklist for ergonomic evaluation and tests its universality empirically with a sample of 96 transport and mining machine operators. The objective of the article is two-fold. First, the article checks whether there are anthropometric dimension differences between different machines’ operators. Second, statistical significance testing regarding items in the proposed checklist is performed to check its universality. Significant differences have not been found between anthropometric dimensions of transport and mining machine operators. Group comparisons prove that mining machines have better ergonomics characteristics of the chair, manual controls and vision field. The recommendation for crane designers is to examine mining machines solutions and analyze the possibility of adapting these solutions, due to anthropometric fit. Wide usage of the checklist is recommended.

The paper presents research into the possibility of applying machine learning algorithms in the prediction of stoppages and/or failure of excavator units. Regression trees, Random Forest and Support Vector Machine (SVM) algorithms were tested with different hyperparameter variations on the collected set of data on the causes and downtime of stoppages of the observed excavator units. The result indicates that the trained SVM algorithm with sufficient accuracy (MSE 0.106) can predict the stoppages of the observed excavator units. Further research is aimed at expanding the database and further improving the possibility of predicting the level of danger for various causes of stoppages and/or failure of the observed excavator units.

Mining equipment working time is critical for ensuring output and fulfilling mining project deadlines. In order to find trends and possible downtime minimization measures, this article analyses numerous types of excavators, dump trucks, loaders, backhoe loaders, bucket wheel excavators, and surface-top hammer drill rigs in Serbian mining sites. The study collects data on mechanical, electrical, technological, organizational, abuse and meteorological downtimes types with the aim to highlight efficient ways for minimizing non-operational periods, hence improving the overall productivity and safety of mining operations in the industry. Special emphasis is put on excavators whose role in the mining industry is pivotal, directly impacting project timelines and financial outcomes. The methodology includes analysing the duration and implications of different downtime categories. Collected data showed non-parametric features, according to the descriptive statistics. To compare the data, the non-parametric Mann-Whitney U-test is implemented. The results of the research showed that, in most cases, there is a difference in the duration of downtimes between different categories of excavators` downtimes. However, a comparison between excavator downtimes and other machinery downtimes has not shown any statistically significant differences. This analysis aims to contribute to the optimisation of mining equipment usage, offering valuable insights for mining industry stakeholders.

Previous research shows the significance of maintenance in enhancing performance levels and reducing system costs of equipment. This paper aims to develop a quantitative model for predicting the failure rate of excavators using artificial neural networks (ANN). As an input to the ANN, the duration times of 590 excavator downtimes measured over 198 days at the mining site in Serbia were used to obtain a classification of failures longer than an hour based on the previous 14 days, in aim to prevent potential indirect financial losses which could be over 15000€/hour. A Pareto analysis of the observed data was also performed and showed the technological type of downtime as the most frequent. The results show that the ANN modeling is suitable for mapping the non-linear relationship between excavation activities and the failure rates of excavators. The results showed that the proposed ANN model provides an accurate estimating tool for the early planning stage to predict failure rates of excavators. Future research avenue proposal is directed at monitoring and forecasting the exact duration of excavator downtime in real-time.

In the mining industry, excavator uptime is crucial for maintaining productivity and meeting project deadlines. Downtime, or periods when excavators are not operational, can significantly impact project costs and efficiency. This paper examines various types of excavator downtime, including mechanical, electrical, technological, organisational, and meteorological, to identify patterns and potential mitigating strategies. By comparing the duration and implications of different downtime categories, this study seeks to highlight effective strategies for minimising non-operational periods, thereby enhancing the overall productivity and safety of excavator operations in the industry. The descriptive statistics indicated that the data exhibited non-parametric characteristics. The non-parametric Mann-Whitney U-test was used to compare the data. The results of the research showed that, in most cases, there is a difference in the duration of downtimes between different categories of downtimes. This analysis aims to contribute to the optimisation of excavator use, offering valuable insights for industry stakeholders.

Despite being the oldest, the mining industry continues to be a major source of pollution, with more people killed or injured than in all other industries. Additionally, social tension related to this sector is widespread around the world, since mining businesses continue to have a significant negative influence on land, water, air, biota, and people through direct and indirect mechanisms. The mining machinery workplaces, which are in the focus of this study have the largest environmental footprint. The dominance of technology-centered design in present research streams is most likely the explanation for the lack of advancement in the mining industry. The SmartMiner project creates shift from technology-centered design and its concept creates solutions for improving the standard of environmental quality in complex systems and suggests a paradigm change to a Human and Data-Centric Engineering. By aligning advanced operator I4.0&5.0 and society S5.0 standards, the SmartMiner project develops solutions for raising the level of environmental quality in complex interactions between physical, behavioural, and organizational processes field. Proposed paradigm can be easily transferred to other industries. The safety of mining machinery operators in their immediate surroundings and their regular alignment with value chain stakeholders are the first steps in our original idea approval process. Research moves to the operator macro-environment, which is determined by organizational contextual factors, and is encompassed by the development of intelligent, ergonomic, non-invasive, and dependable operator aid systems for regulating physical environment job stressors - noise, human vibration, lighting, temperature, air quality, workplace layout issues, etc., with high potential to solve environmental and human health issues and to influence overall performance.

Excavator operators encounter demanding work environments with very high risks for discomfort, musculoskeletal disorders, and workplace accidents. In line with that, this study examines the relationship between ergonomic design influential factors using the structural model of excavator cabins design factors, using a sample of 32 excavator operators. Descriptive statistics were performed to describe the operator's age, height, weight, working experience, and excavator's lifespan. After that, the structural equations model was developed to describe the impact of latent variables related to ergonomic design of the cabin. This model was constructed by using 17 questions, which were categorized into 5 groups based on ergonomic design characteristics such as seat, armrests, commands, cabin, and working conditions. The findings indicate that the model exhibits favourable reliability and validity coefficients, a substantial effect size, and a satisfactory model fit. Further research is needed to increase the sample size, despite the preliminary nature of the current research and its satisfactory results.

Transport and mining companies are vulnerable to a variety of hazards, and this paper offers a novel conceptual framework for organisational resilience assessment at different organisational levels. It is based on a safety climate and performances assessment in companies in the transport and mining sector. The framework contains factor and reliability analysis and continues by applying the SMART method from the perspective of resilience corners: anticipate, monitor, react and learn. The highest resilience coefficients at all organisational levels are obtained regarding safety awareness, safety training and safety communication areas, while the lowest values are observed for risk assessment and the organisational environment. The obtained results indicate that the ‘monitor’ resilience corner must be improved at all organisational levels, employees trained and procedures changed, since employees do not sufficiently use experience from previous events. The very high values at all organisational levels of the coefficients regarding the ‘anticipate’ and ‘learn’ resilience corners indicate organisational adaptation to changes through corrective activities, rather than planned preventive ones. All hierarchical levels have good resilience indicators, except for middle management, which expressed an adequate resilience index, due to the lower ratings of the safety training, organisational commitment, risk assessment and management support areas.

This manuscript is presenting the process of development of the original software application, e.g. the tool, to be used in assessment of the resilience level and the safety climate indicators in the industrial organizations in the transport and mining sector. The initial data that was used to develop the app was collected using the questionnaire in which employees, segmented in five different organizational levels (e.g. transport and mining machines operators, support workers, first line managers, middle level managers and top level managers), were assessing the present state and the importance of each of the investigated safety indicators. Based on this initial database the starting measurement model was developed that was used to develop the application which calculate the resilience levels of the organizations, based on adequate MCDA methods and four resilience corners approach.

There is a wide spectrum of industries and company-level features that influence the application of Corporate Social Responsibility to improve the business’s sustainability. This study aims to examine the understanding of the Corporate Social Responsibility concept and the impact of its implementation on sustainability in companies. Therefore, the research proposed and tested two scenarios where five industries sectors were ranked according to the importance (scenario 1) and implementation (scenario 2) of Corporate Social Responsibility activities. The integrated Entropy-TOSIS method to obtain results was employed. The research data were obtained from interviewing employees from Serbia, Russia, and Bulgaria working in various industries. The findings of this study revealed certain differences in perception and implementation of Corporate Social Responsibility depending on the country where the concept is applied as well as among the industries with their specific needs for sustainable development goals achieving. This research can be useful for engineering managers as a roadmap for reaching sustainable business while fulfilling economic, social, and environmental norms as well as stakeholder expectations.

For the production planning process to be successful, an enterprise's internal business processes must be effective and efficient, while the DMAIC cycle is important aspect of practice-based continuous improvement. In accordance to those facts, this preliminary research includes the first part of the DMAIC methodology - “define“ and “measure“ tools, applied to a case study in automotive industry, with the aim of improving production planning processes. Analysis in “define“ phase starts with SIPOC, continues with calculation of critical indicators in “measure“ phase and is followed by Pareto charts. Research shows that the existing method of manual collection of production data is not precise enough, and during two months observation period, between 14 and 52 percent of planned production time on 9 observed machines, passed as unnoticed downtime. Further data analysis showed that this time is 1.5 to 9 times higher than the total reported downtime on individual machines. Results show that the further development of data collection tools is crucial, and the recommendation is to move in the direction of automation of that process in order to make the most of available technical resources, in “improve“ phase and to “control“ it by statistical comparison of previous and new state indicators

This research presents a comprehensive examination of the SWOT (Strengths, Weaknesses, Opportunities, Threats) and quantified TOWS (Threats, Opportunities, Weaknesses, Strengths) frameworks, aiming to elucidate their significance in strategic planning in the SME sector. The emphasis will be given to the SWOT analysis application that was developed with the aim of collecting and analyzing the responses of entrepreneurs and SMEs owners during self-evaluation of their businesses. The primary objective is to explore the potential of this tool to facilitate strategic decision-making and enhance organizational performance in SMEs. The research also seeks to uncover practical applications and limitations through empirical evidence and case studies in education.

The world is globalized and highly dependent on efficient supply chains, so logistics activities and suppliers are becoming more and more important under such conditions. Supplier selection represents a multi-criteria decision-making problem, where the decision is influenced by several mutually conflicting factors in the procurement process, therefore a trade-off analysis between the defined criteria must be performed. The supplier selection process would be simple if only one criterion influences the decision. However, in most cases decision makers must consider a number of criteria when making this appropriate decision. Taking into account the problems that companies may have due to an inadequate selection of suppliers, starting from the wrong quantities assessment when purchasing materials, or preferring only one criterion (eg price), as well as other problems, the AHP model represents a possible solution so that, in the process of selecting suppliers, errors are eliminated or reduced to a minimum. The subject of this research paper is the multi-criteria selection of suppliers using the AHP methodology with the help of the Expert Choice 11 software package. The aim of the research is the ranking of suppliers based on defined decision criteria.

A company’s development performance and growth may be impacted by a wide range of different factors, which unquestionably affect number of the employees in the loop. Taking into account all influencing factors, companies would benefit if have possibility to predict the degree of change in the number of employees in future period in order to adjust their internal strategy or to make appropriate decisions that enable the survival and progress of the company in the market. The aim of this research is to predict the change in number of employees based on current state of contingency and quality management factors, using information obtained from a survey of 67 different companies from Serbia. In the first part of the research, a correlation analysis is used with the aim to identify the specific contingency and quality management factors that are most closely associated to the subject of interest, which is, in this case, degree of change in the number of employes. The second part of the research involves feedforward neural network training for prediction of the degree of change in number of employees based on feature extraction of main factors. The training accuracy that proposed network achieved is 77.36%, while testing accuracy amounts 71.43%.

For mining processes to operate safely, information about safety is crucial. Numerous authors pay attention to fostering a good safety climate without paying attention to safety communication across organizational structure, which motivates this survey. Accordingly, this paper aims to check the hypothesis whether there is a difference between the attitudes of managers, operators, and auxiliary workers in the mining industry regarding safety communication, since differences on their attitudes about safety communication issues have potential to cause safety performance. A survey of 123 respondents working in different positions in Serbian mining companies, which have evaluated the importance of communication as well as the current state regarding the quality of communication in their organizations, was conducted. After evaluating the data and conducting descriptive statistics, followed by the Mann-Whitney test, it was determined that there are no statistically significant differences in the attitudes of managers, operators, and support staff toward safety communication. This indicates that issues with an organization's safety performance are not brought on by the differences on safety attitudes at different hierarchical levels. Proposal for the future research is to examine other possible causes, such as cognitive biases in risk perception, safety training and education and similar, and to use larger sample that may confirm our hypothesis.

Mining industry has a vital role in economy every country. That is the reason why is necessary to detect, remove or mitigate and prevent risk failures of mining machinery. In order to achieve that the first step is to identify and compare downtimes/failures and estimated risks. In this paper it was conducted the preliminary research that included 348 mining machines divided in 7 different types. The aim of the research was to determinate is there statistically significant difference between different types of mining machines in two criteria: level of danger of consequences of downtime/failure and estimated failure risk by using Kruskal - Wallis H Test. The results indicate that when it comes to level of danger of consequences there is no statistically proof that there is difference between different types of the machines. In case of estimated failure risk, there is statistically significance between some types of mining machines.

The contingent/contextual approach to the theory and practice of the organization arose in the 60s of the previous century, and its verification and empirical improvement are still ongoing, with increasing topicality and the presence of an increasingly strong trend of quantifying the influence of contingent/contextual factors and the increasing complexity of research with the same aim - it is expected that by mastering the effect of given factors, the overall efficiency of the company can be increased and that standardization in this field can bring great benefits. So far, the best researched and quantified is the impact of company size and technology on organizational structure factors. However, a large number of interdependencies are still not resolved among contingent factors, such as: a) the connection of decentralization with other contingent factors - company size, environment, etc., b) the influence of digitalization on the range of management, c) the direction of the influence of the degree of mass production on the number of hierarchical levels, d) the impact of innovation as a part of strategy on the organizational structure and many others. Various authors try to solve the above-mentioned questions, but the conclusions between them have not been confirmed so far; what's more, numerous results are released to the scientific public, which are mutually contradictory, as shown in the tabular presentation in the paper. In particular, the contribution of the latest version of the ISO 9001 standard from 2015, which for the first time recognizes the context in the given area, should be highlighted. Finally, it can be concluded that the empirical verification and theoretical enrichment of the contingent theory has been going on for decades, with the presence of an increasingly strong trend of quantifying the impact of contingent factors and the increasing complexity of research with the same goal - it is expected that mastering the effect of contingent factors can

The results presented in this manuscript, are one segment of large international research, conducted during the International Visegrad project: “Possibilities and barriers for industry 4.0 implementation in SMEs in V4 countries and Serbia”. The project was financially supported by the International Visegrad Fund, during the period 2021-2022. The awareness of the entrepreneurs and the employees in the SME sector on the possibilities and barriers of digitalization of their business operations were collected through specially designed questionnaire. Obtained results were analyzed using the linear and non-linear statistical analysis, including linear regression analysis and artificial neural networks.

Corporate social responsibility (CSR) has become a vital element for building a long-term relationship with a company’s stakeholders. Different dimensions of a company’s social initiatives in terms of internal and external CSR activities influence the satisfaction of employees with the purpose of improving the CSR application. The aim of this research is to examine the level of employees’ awareness of the implementation of CSR in Serbian and Russian companies. A comparative analysis between these two countries was carried out in order to perceive the differences in attitudes of employees, their job satisfaction, and consequently the implementation of CSR. The hypotheses of the developed model were tested by using the Multi-group Confirmatory Factor Analysis. The obtained results offered several implications for scholars and practitioners that should be considered when formulating and implementing CSR actions.

The accelerated development of information and communication technologies has caused notable changes in all spheres of life and business. In order to contribute to their own economic goals and the economy in general, companies are forced to embrace technological innovations. This study aims to test a hypothesis that the digitalization of business operations in small and medium-sized enterprises (SMEs) positively influences economic development. Since the degree of digitization is a complex phenomenon, it is approached through several quantitative indicators. The data from the Eurostat database were used for the research. The analysis results obtained using the structural equations modeling confirm the positive relationship between the level of digitization of SMEs and gross domestic product (GDP).

Digital progress of the society is associated with trends in energetic sector. The top ten digitally developed European countries, according to the most recent ICT development index report in 2017, were chosen as testing countries to conduct a deeper investigation on the issue of energy production and consumption in the digital era. Data on (1, 2) coal and oil-fired electricity generation, (3) share of GHG emissions from energy production, (4) final electricity consumption in industry, (5) final electricity consumption in households, and (6) energy intensity were included in the initial database. For determining criteria weights and final ranking of alternatives, a hybrid CRITIC-TOPSIS MCDM model was utilized. The CRITIC approach was used to calculate objective weights of criteria by employing standard deviation to measure the contrast strength of each criterion. The findings highlight the dominance of three criteria in comparison to others: energy intensity (wei = 0.200), share of GHG emissions in total energy production (weGHG = 0.198), and ultimate energy consumption in households (wfch = 0.194). The TOPSIS approach was used to complete the final ranking. Switzerland, Luxembourg, and Sweden were ranked first, second, and third, respectively, in terms of advanced digitalization performance and awareness of the need of providing green energy for economic activity.

This manuscript is presenting the results of the research, conducted during the International Visegrad project: “How to prevent SMEs from failure (Actions based on comparative analysis in Visegrad countries and Serbia)”. The project was realized during the period 2018-2019. The importance of the factors that can influence SMEs failure were collected through specially designed questionnaire that included individual and non-individual factors. In the group of non-individual, external and internal factors were analyzed. The ranking of collected factors were done using the contemporary multi criterion decision making tools. The results of this previous project will be introduced, in the first part of the paper, as the input for the results obtained in 2021-2022 in frame of the new research project, which will be presented in the second part of the paper

Application SWOT V4 is developed as the activity of the project: “Possibilities and barriers for Industry 4.0 implementation in SMEs in V4 countries and Serbia”, which was financially supported by the International Visegrad Fund, with reference number: 22110036. The application is developed by Professor Ivan Mihajlović, from University of Belgrade - Technical faculty in Bor, who was the project coordination of the project. The application is small simulation of the SWOT analysis method, dedicated to entrepreneurs, SMEs owners and employees. It is developed to be used under the Windows operation system.