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EN
Purpose: The aim of the article was to analyse the pro-environmental approach to products improvement by SMEs from the Visegrad Group (V4) countries. Design/methodology/approach: The research was survey. The survey research was carried out in traditional (paper) and electronic form (MS FORMS). The survey was directed at electronic industries (machinery processing) belonging to small and medium enterprises (SMEs) from V4 countries. The results presented in the article refer to sample size of 379 SMEs, obtained between March and September 2023. Results were analysed in statistical way, i.e.: ANOVA test and Mann Whitney U test by significance level equal to a = 0.05. Findings: SMEs in V4 countries have a similar approach to pro-environmental improving of products. It was shown that pro-environmental activities of SMEs from the V4 countries, the lowest consistency of assessments occurs in the following countries: Poland and Slovakia, Poland and Hungary, Slovakia and Hungary. Practical implications: The research results may contribute to more effective and coherent development activities of SMEs in the V4 countries as part of their sustainable development, and also to take appropriate actions that will be supporting qualitative and environmental products. Social implications: The results can help SME entrepreneurs improve their products to protect the natural environment. In addition, they can be the basis for the development of SMEs from the V4 countries and their unidirectional development, which includes not only improving the quality of products, but also caring for the environment. Originality/value: The originality of the research is the determination of the current pro-environmental approach to product improvement in SMEs in the V4 countries. The novelty is the provision of current data on the importance of improving pro-environmental products in SMEs from the V4 countries and their comparison between companies in the electrical machinery industry.
EN
Due to advances in machine learning techniques and sensor technology, the data driven perspective is nowadays the preferred approach for improving the quality of maintenance for machines and processes in industrial environments. Our study reviews existing maintenance works by highlighting the main challenges and benefits and consequently, it shares recommendations and good practices for the appropriate usage of data analysis tools and techniques. Moreover, we argue that in any industrial setup the quality of maintenance improves when the applied data driven techniques and technologies: (i) have economical justifications; and (ii) take into consideration the conformity with the industry standards. In order to classify the existing maintenance strategies, we explore the entire data driven model development life cycle: data acquisition and analysis, data modeling, data fusion and model evaluation. Based on the surveyed literature we introduce taxonomies that cover relevant predictive models and their corresponding data driven maintenance techniques.
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