Diagnostics of the enterprise personnel sustainability

Abstract

Ensuring Ukrainian enterprises’ personnel sustainability is a real challenge because the structural transformation of the labor market in the context of globalization of the world economy leads to increased competition for qualified personnel. In the Ukrainian labor market, the tendency to imbalance professional and qualification structure is the result of the progress of migration, urbanization, and demographic crisis. Thus, the study aims to develop a procedure for diagnostics and predicting the personnel sustainability of the enterprises due to the construction of the forecast model. The formation of “Predictable Model of the Personnel Sustainability” (“PMPS”), which is based on an analysis of the personnel sustainability indicators and predictors of release, was proposed. Approbation was carried out on the example of the Ukrainian enterprises of a recreational complex. The results of the personnel sustainability forecasting showed that the extent of the violation of the personnel sustainability is significant because the high projected performance of the indicators of fluctuation movement of the personnel and employee turnover changes, the structural components associated with wages indicate its further growth, and trends in the predictors of the violation of sustainability signal the possible dismissal of the personnel in the future.

Predictors of the layoffs in the system of provision of the personnel stability

“…Additionally, Bezugla, (2018) define staff resilience in their work and provide indicators for its evaluation. Trunina et al (2020), offered a diagnosis of personnel stability; while Vartanova and Povzun (2023) developed an ontology for personnel stability. However, the problems of forecasting layoffs in the system of provision of personnel stability require further research.…”

Intelligent Process Automation, Robotic Process Automation and Artificial Intelligence for Business Processes Transformation

“…In smart factories, automated work processes, improved tracking, and scheduling synchronization processes, along with optimized energy consumption, allow for increased uptime, improved product quality, minimized costs, and reduced waste [48]. For instance, Siemens employs machine learning models trained on historical sensor readings to identify turbines at risk of failure.…”

Information system for forecasting sales of building materials

“…Розглянувши можливі програмні рішення реалізацій продукту, ми вибрали такі інструменти для імплементації інформаційної системи прогнозування продажів асортименту будівельних матеріалів на торговельному онлайн-майданчику на основі використання нейронної мережі Tensor flow, як вебзастосунок. Найпридатніші технології, які дозволять якісно і швидко втілити MVP версію застосунку, сьогодні такі: Webstrom, HTML, CSS, Bootstrap, Tensor flow, Gulp, Jquery, Javascript [16][17][18][19][20][21][22][23][24][25][26]. На рис.…”