The problem of production flow and evaluation of productivity in the manufacturing line is analysed. Machines can be operated by humans or by robots. Since breakdowns and human factors affect the destabilization of the production processes, robots are preferred. The main problem is a proper methodology—how can we determine the real difference in work efficiency between human and robot at the design stage? Therefore, an analysis of the productivity and reliability of the machining line operated by human operators or industrial robots is presented. Some design variants and simulation models in FlexSim have been developed, taking into consideration the availability and reliability of the machines, operators and robots. Traditional productivity metrics, such as the throughput and utilization rate, are not very helpful for identifying the underlying problems and opportunities for productivity improvement in a manufacturing system, therefore we apply the OEE (overall equipment effectiveness) metric to present how the availability and reliability parameters influence the performance of the workstation, in the short and long terms. The implementation results of a real robotic line from industry are presented with the use of the overall factory efficiency (OFE) metric. The analysis may help factories achieve the level of world class manufacturing.

This paper aims to check the impact of investment and institutional determinants on the energy efficiency gap. The findings of the bibliometric analysis confirmed the growth of research interests in identifying the core determinants of the energy efficiency gap. The central hypothesises are: the increasing quality of the institutions leads to an increase of green investments in the energy sector and the dual relationships between investment and institutional determinants lead to additional synergy effects, which allow boosting the decline of energy efficiency gaps of the national economy. For the analysis, the times series were collected from the World Data Bank, Eurostat, Bloomberg, for Ukraine for the period of 2002–2019. The following methods were used: the unit root test—for checking the stationarity of data—and the Johansen test and VEC-modelling—for the cointegration analysis. The findings prove that to reduce the energy efficiency gaps in Ukraine by 1% next year, it is necessary to increase green energy investments by 1.5% this year, and the political stability and public perception of corruption by 3% and 1%. The increase of the public perception of corruption by 1.47 points and of political stability by 2.38 points leads to maximising the recovery speed of the Ukrainian energy sector. Thus, while developing the policy to decrease the energy efficiency gaps, the Ukrainian government should consider the level of public perception of corruption and political stability.

The aim of this article is to show the use of the analysis of the failure causes and effects as a prevention tool in controlling the quality of a given production process in the company. The scope of the work covers an analysis of a selected process, definition of inconsistencies present in this process, and then the FMEA analysis. In the production company one should implement thinking and actions based on the so-called ‘quality loop’ – it is an interdependence model of the undertaken actions which affect the quality shaping. It is carried out from the possibility for identifying a customer’s requirements through a project, production process, up to the assessment of effective capability for meeting the defined requirements. The application of such an approach enables to take the actions improving the operation of quality management in a systemic way.

Nowadays, production processes become more and more complicated because of global market competition and the need for flexible changes in production in order to meet the customer needs. That is related to frequent reorganization of production systems and require adequate operations management, internal and external logistic and project management from the stage of a conceptual design to the industrial implementation. Currently, we may observe an increased use of automation and robotization, which replace human labor. Modern industrial robots can work similarly to a human operator, but the replacement of a human operator with robot is related with some technical problems and excessive cost, therefore robotic workstations must be properly designed. The aim of the study is developing a method which allows estimation of productivity growth associated with the replacement of human labor with industrial robots at an early design stage. The production flow in the manufacturing system and internal logistic processes were modeled in the Enterprise Dynamics software, which enables simulation and visualization of discrete production processes. The models were built taking into account availability, performance and quality parameters, which enable a direct calculation of OEE (Overall Equipment Effectiveness) indicator, which is one of most important Key Performance Indicator (KPI) used in World Class Manufacturing (WCM) methodology, especially in automotive industry. The use of OEE indicator enables comparison with similar manufacturing systems. The example of conceptual design of the flexible manufacturing cell, with CNC machine tools, which can be operated by humans or robots, is presented. The production process includes two machining operations of large and heavy parts, which are difficult to handle, therefore robots are preferred to apply. The computer simulation of the sophisticated model of flexible manufacturing system with machines, operators and robots with reliability parameters allows better representation and understanding of a real production process at the stage of conceptual design. The experiment results confirm the advantage of application of robotic operated production systems comparing to manually operated machines with production growth about 17%. However, robotization is related to very high investment cost, therefore detailed economic analysis is required, including comparison of labor cost and robot cost. Obtained results can be used for detailed designing of manufacturing systems and robotic workstations.

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