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The issue of female representation in top management teams is a growing area of interest among practitioners, policymakers, and researchers around the world (Ahmadi, Nakaa, & Bouri, 2018). In particular, there has been an ongoing debate about whether gender diversity in top management teams relates to company performance, more specifically financial performance (Farag & Mallin, 2017; Hernandez-Nicolás et al., 2015). The existing body of literature suggests that top management team gender diversity has both positive and negative effects on company performance. Additionally, the results of empirical studies on this issue remain mixed (e.g., Ali & Konrad, 2017; Eger & Indruchová, 2014; Julizaerma & Sori, 2012). On the one hand, there is evidence that gender diversity in top management is associated with higher financial performance (e.g., Campbell & Mínguez-Vera, 2008; Perryman, Fernando, & Tripathy, 2016).

Health care economists estimate that 40-50% of annual cost increases can be traced to new technologies or the intensified use of old ones (Callahan, 2008). However, any limitation of their application is massively criticized as unethical. Patients (supported by journalists) believe that new expensive technology will speed-up their treatment and miraculously enhance their quality of life, while physicians are fascinated by fanciful possibilities of state-of-the-art devices. Nevertheless, due to limited resources of health care, each particular utilization of a medical device should be put to the test of the clinical effectiveness and cost-effectiveness (Markiewicz, van Til, & Ijzerman, 2014; Rosina et al., 2014). The typical approach used above all in drugs is to calculate cost-effectiveness when the technology is in routine use.

Industrialization, a major force in structural change, shifts resources from labour-intensive activities to more capital technology-intensive activities. It will remain crucial to the future growth of developing countries. Manufacturing’s share of GDP has remained stable over the last 40 years. Technology and capital equipment are the main drivers of both manufacturing growth and aggregate growth in developed and developing countries, although in developing countries energy and natural resources use affects growth in the medium- and low-tech industries (Unido, 2018). Currently, the industrial value creation is shaped by the development towards the fourth stage of industrialization, so-called Industry 4.0. Industry 4.0, referred to as the “Fourth Industrial Revolution”, also known as “smart manufacturing”, “industrial internet” or “integrated industry”, is currently a muchdiscussed topic.

Mechanical engineering belongs to the key industries in Slovakia. In terms of achieved sales and the rate of employment, it ranks among the largest manufacturing industries. The industry currently employs 12 per cent of the population and accounts for up to 42 per cent of total output of the Slovak Republic. Many of the enterprises operating in this industry are small or mediumsized. This industry apparently plays an essential role in the global economy, it is a source of entrepreneurship, innovations and new jobs. These are some of the reasons for which SMEs´ profitability and ways of its improvement should draw particular attention. It is therefore obvious that the issues of the financial analysis in SMEs are receiving constant attention. Since SMEs are the backbone of the Slovak economy and mechanical engineering is one of its key industries, our intention in this article is to focus attention on profitability and factors influencing it in SMEs active in the mechanical engineering industry.

The statistical process control (SPC) is widely used in industry. Its aim is to achieve proces stability and improve capability through the reduction of variability and it is included for example in the Six Sigma or Lean Six Sigma methodologies. The control of attribute data represents a considerable part of it. Until recently, the same approach was used to monitor variables or attributes data. In this approach, subgroups of items are taken from a process and sample characteristics are plotted in a control chart to see whether their variation is only random or whether it is affected by an assignable cause. The presence of such cause is indicated by exceeding the control limits that are based on the sample characteristic distribution.