USING DIGITAL TWINS TO IMPROVE THE EFFICIENCY OF PRODUCTION PROCESS MANAGEMENT IN UKRAINIAN INDUSTRY

Abstract

The article examines the essence and role of digital twins in managing production processes and synthesizes their significance for improving management in the Ukrainian industry. Based on an analysis of contemporary scholarly approaches, it establishes a precise «data – models – decisions» loop, in which the digital twin is viewed as a channel for converting streaming production data into formal state estimates, forecasts, and control actions. A multilayer architecture is proposed that combines Industrial Internet of Things (IIoT) infrastructure with tiers of models (physicsbased, data-driven, and hybrid) and decision-support services integrated with MES/SCADA/APS. Mechanisms are demonstrated for closing the «plan – actual» loop in dispatching, localizing decisions at the machine and work-cell levels, and embedding energy constraints into schedules and operating regimes. Requirements are formulated for identifiers, events, attributes, and access policies to support tracing managerial actions. Research data and industrial case studies show that applying digital twins reduces equipment and cell downtime, stabilizes line and operation cycle time, lowers electricity and other energy consumption, decreases rework and scrap, and improves adherence to the production plan; taken together, these effects strengthen operational control and reduce unit cost. The novelty lies in the focus on management efficiency, the harmonization of terminology and twin roles in the control context, and the clarification of links between model outputs and enterprise KPIs. The limitations of implementing digital twins include heterogeneous legacy infrastructure, a lack of standard interfaces, uncertainty in hybrid modeling, and cybersecurity risks. The paper proposes further directions for research and deployment: profiling the architecture for typical production scenarios; methods for attributing effects to managerial decisions; managing model uncertainty; integrating energy twins into scheduling; and sectoral pilots for scaling.