Szczegóły publikacji
Opis bibliograficzny
Using Digital Twin (DT) technology for workforce demand forecasting in the post-war reconstruction of Ukraine / Zoriana Dvulit, Liana Maznyk, Kyrylo Maznyk, Lesia Brych, Mariia-Mariana Dvulit, Natalia IWASZCZUK, Aleksander Iwaszczuk // W: SmartIndustry 2025 [Dokument elektroniczny] : proceedings of the 2nd international conference on Smart automation & robotics for future industry : April 03–05, 2025, Lviv, Ukraine / ed. by Nataliya Shakhovska, [et al.]. — Wersja do Windows. — Dane tekstowe. — [Niemcy : CEUR], cop. 2025. — ( CEUR Workshop Proceedings ; ISSN 1613-0073 ; vol. 3970 ). — S. [199–212]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [211–212], Abstr.
Autorzy (7)
- Dvulit Zoriana
- Maznyk Liana
- Maznyk Kyrylo
- Brych Lesia
- Dvulit Mariia-Mariana
- AGHIwaszczuk Natalia
- Iwaszczuk Aleksander
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 160396 |
|---|---|
| Data dodania do BaDAP | 2025-07-08 |
| Tekst źródłowy | URL |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | CEUR Workshop Proceedings |
Abstract
This article explores the application of Digital Twin (DT) technology for forecasting workforce demand during the post-war reconstruction of U changes, assessment of labor shortages, and planning of workforce allocation across various economic sectors. The proposed Digital Twin Workforce Navigator in Post-War Ukraine Reconstruction Planning model takes into account migration dynamics, demobilization, educational initiatives, and shifts in regional workforce availability. Integrating Digital Twin technology into workforce planning enhances labor management efficiency, minimizes labor market imbalances, and improves the accuracy of workforce demand forecasting. The use of machine learning algorithms, particularly LSTM and Random Forest, facilitates predictions of workforce fluctuations across regions, supports optimal resource allocation, and aids in developing adaptive strategies for the reintegration of demobilized workers. Such models enable the construction of potential economic development scenarios, making them valuable tools for workforce demand forecasting in post-war recovery. The implementation of digital twins in both public and private human resource management strategies can significantly accelerate reconstruction efforts and stabilize Ukraine’s labor market. Moreover, the proposed approach has been demonstrated through workforce forecasting for the years 2012-2030, providing a first approximation of labor demand trends and offering valuable insights for the practical implementation of Digital Twin models in workforce planning during post-war reconstruction.