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The article presents a method of generating key performance indicators related to electric motor energy efficiency on the basis of Big Data gathered and processed in a frequency converter. The authors proved that using the proposed solution, it is possible to specify the relation between the control mode of an electric drive and the control quality-energy consumption ratio in the start-up phase as well as in the steady operation with various mechanical loads. The tests were carried out on a stand equipped with two electric motors (one driving, the other used to apply the load by adjusting the parameters of the built-in brake). The measurements were made in two load cases, for motor control modes available in industrially applied frequency converters (scalar V/f, vector Voltage flux control without encoder, vector voltage flux control with encoder, vector current flux control, and vector current flux control with torque control). During the experiments, values of the current intensities (active and output), the actual frequency value, IxT utilization factor, relative torque, and the current rotational speed were measured and processed. Based on the data, the level of energy efficiency was determined for various control modes.

This paper puts forth a systematic approach to evaluating the maturity level of the robotization of production processes in the context of digital transformation for manufacturing companies. The method was developed to address the absence of a sector-specific framework for assessing robotization maturity growth, in line with the Industry 5.0 guidelines (incorporating sustainability, the circular economy, and human-centeredness). The survey covers six core areas for manufacturing companies: the automation and robotization of production processes, digitization of warehouse processes, flexibility and intralogistics, and end-to-end integration of key data management processes. The study aimed to advance digitalization through improved robotization maturity. The study surveyed 200 small and medium-sized businesses operating in Poland from 2022 to 2024. The study presents a method to assess enterprise operational maturity, covering current and planned levels and development plans for the next three years.

This paper puts forth a systematic approach for evaluating the maturity level of production process automation in the context of digital transformation for manufacturing companies. The method was developed to address the absence of a sector-specific framework for assessing automation maturity growth, in line with the Industry 5.0 guidelines (incorporating sustainability, the circular economy, and human-centeredness). The survey covers six core areas for manufacturing companies: automation, robotization of production processes, digitalization of warehouse processes, flexibility, intralogistics, and end-to-end integration of key data management processes. The study aimed to advance digitalization through improved process automation maturity. The study surveyed 200 small- and medium-sized businesses operating in Poland from 2022 to 2024. The study presents a method for assessing enterprise operational maturity, covering the current and planned levels and the development plans for the next three years.