Bulletin of NTU "KhPI". Series: Strategic management, portfolio, program and project management

DIGITAL TECHNOLOGIES IN CONSTRUCTION PROJECT MANAGEMENT

Asiman Alizada — 2026-05-31

The construction industry represents one of the most complex and risk-intensive sectors of the global economy, significantly contributing to national Gross Domestic Product (GDP) while simultaneously facing persistent challenges such as budget overruns, schedule delays, low productivity, and fragmented decision-making processes. Large-scale and megaprojects, in particular, frequently exceed their initial budgets and timelines due to inaccurate cost estimation, insufficient planning, unclear project scopes, and the predominance of experience-based rather than data-driven managerial decisions. In this context, digital transformation has emerged not merely as an innovation trend but as a strategic necessity for improving efficiency, transparency, and risk management in construction project management. This study investigates the role and impact of four major digital technologies—Building Information Modeling (BIM), the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin systems—within the lifecycle of construction projects. The research adopts a comprehensive analytical and literature-based approach, examining recent academic studies, industry reports, and global case examples to evaluate the effectiveness, implementation levels, and strategic contributions of these technologies. The findings indicate that BIM significantly enhances interdisciplinary coordination, conflict detection, visualization, sustainability analysis, and cost-time management, with the highest impact observed during early project phases where cost influence potential is greatest. IoT technologies contribute to real-time monitoring of safety conditions, environmental parameters, equipment performance, and resource utilization, thereby improving operational control and reducing on-site risks. Artificial Intelligence enables advanced data analytics, predictive modeling, and decision-support mechanisms, particularly when integrated with BIM and IoT platforms, although its adoption in construction remains comparatively limited due to data standardization and organizational barriers. Digital Twin systems extend beyond static modeling by establishing real-time synchronization between physical assets and digital replicas, allowing continuous lifecycle optimization and performance monitoring. The study concludes that the integrated application of BIM, IoT, AI, and Digital Twin technologies forms a synergistic digital ecosystem capable of transforming construction project management into a data-driven, predictive, and strategically optimized discipline. Despite existing challenges such as traditional management culture, fragmented data structures, and resistance to change, the adoption of digital technologies is essential for reducing project risks, improving productivity, and enhancing global competitiveness in the construction industry.

INTERCULTURAL COMPETENCIES AS A FACTOR OF PROJECT TEAM EFFECTIVENESS: THE CASE STUDY OF GERMANY

Kateryna Semenchuk — 2026-05-31

Migration processes, driven by socio-economic and geopolitical transformations in recent years, have significantly influenced labor markets in European Union countries and highlighted the need for effective integration of foreign workers. This issue has become particularly acute since 2022 due to the large-scale displacement of people from Ukraine, posing challenges for host countries, primarily Germany, not only to ensure employment quantitatively but also to achieve sustainable and high-quality professional integration. Despite the increasing employment rates of Ukrainian refugees, quantitative indicators do not fully reflect the actual level of professional adaptation, team effectiveness, or long-term career mobility. This article examines intercultural competencies as a critical factor for successful project team performance and as a key resource for the professional integration of Ukrainian migrants into the German labor market. It is argued that the effectiveness of collaboration in multicultural environments depends not only on professional qualifications and language proficiency but also on social-behavioral skills, adaptability, empathy, tolerance, and the ability to interact constructively with colleagues and management. The theoretical foundation of the study is based on contemporary approaches to intercultural competence analysis, particularly Jürgen Bolten’s concept, which defines it as a dynamic combination of personal, social, and functional-strategic components manifested through interaction in teams and professional activities. The practical implementation of these competencies is analysed through the example of the forwarding and logistics sector in Germany, characterized by high operational complexity and the need for integration of professional, cognitive, language, and intercultural skills. The study demonstrates that formal competency assessments, including professional tests, language tasks, and exercises in logic and analytical thinking, allow for an objective evaluation of Ukrainian specialists’ readiness to work in a structured and multicultural professional environment.

HYBRID MODELING METHODOLOGY FOR PREDICTING RISKS IN EMERGENCY MANAGEMENT

Mir Ramin Mir Gasim Yunusov — 2026-05-31

This research addresses the critical challenge of forecasting natural and man-made emergency situations, with a specific focus on industrial and forest fire dynamics. Traditional emergency management often relies on deterministic models that, while physically accurate, struggle to incorporate the inherent stochasticity of environmental variables. Conversely, purely statistical approaches frequently fail to account for unique, non-linear scenarios where historical data is insufficient. To bridge this gap, this paper proposes a robust hybrid modeling methodology that integrates fundamental physico-mathematical equations with advanced probability theory methods. The core of the deterministic component is based on the parabolic partial differential equation of heat conduction, which describes the thermal evolution of objects under stress. To account for real-world uncertainties, environmental parameters such as wind speed and ambient temperature are treated as random variables, modeled using Weibull and Gaussian distributions respectively. A comprehensive computational experiment was conducted using the Monte Carlo simulation method, executed via Python-based algorithms to perform 10,000 iterations for dynamic fire risk assessment. The Finite Difference Method (FDM) was employed to solve the heat transfer equations iteratively. The results indicate that while a static deterministic model predicts a failure time of 24.5 minutes, the hybrid approach reveals a significant stochastic variance, with failure times ranging from 15 to 45 minutes. Notably, the model identified a "Tail Risk" where 5% of the simulations resulted in failure within less than 18 minutes—a critical safety window that traditional models overlook. Furthermore, a counter-intuitive physical correlation was observed where higher wind speeds occasionally delayed failure due to enhanced convective cooling effects. This methodology provides a more realistic and granular tool for decision-makers in emergency management, offering not just a single risk value but a comprehensive probability interval essential for life-saving evacuation planning.

DOCKERIZED BLOCKCHAIN ARCHITECTURE FOR SECURE SUPPLY CHAIN MANAGEMENT SYSTEMS

Pavlo (Zherzherunov — 2026-05-31

Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.

FORMATION THE BASIC CONCEPT OF A METHOD FOR MANAGING THE RISK OF TIME LOSSES IN IT PROJECTS BASED ON MODELING THE TEAM’S COGNITIVE PROFILE

Vadym Ziuziun — 2026-05-31

This scholarly work addresses the pressing problem of managing the risks of time losses in IT projects that arise from cognitive interruptions experienced by specialists while performing complex tasks. The author notes that contemporary Agile methodologies create an inherent conflict between the need for deep concentration (the «flow state») and the intensity of team communications. This conflict leads to the accumulation of cognitive debt – a latent risk reflecting a reduced ability of the team to return to productive work after interruptions. The purpose of the study is to develop a method that treats the team’s cognitive profile as a dynamic project resource for the quantitative forecasting of deadline-failure risks. The scientific novelty of the work lies in the introduction of formalized metrics for cognitive debt and interruption cost, which make it possible to assess the systemic consequences of cognitive losses along the critical path of the project dependency graph. The proposed method is based on modeling an individual specialist’s cognitive viscosity and calculating the reconcentration time required to restore the task’s mental models. The mathematical core of the method transforms a planned work schedule into a probabilistic model in which each interruption acts as a factor that extends lead time. A key element is the determination of interruption cost, which accounts not only for the personal losses of an individual developer but also for the cascading idle time of all dependent team members. The practical significance of the study lies in the possibility of integrating these models into IT project management systems for adaptive regulation of communications. Based on probabilistic risk assessment, the system can propose preventive measures such as a «cognitive quarantine» (blocking non-priority notifications) or dynamic sprint or Agile process rescheduling. In summary, the method enables a shift from reactive acknowledgment of delays to proactive management of cognitive resources. This provides a scientific foundation for protecting developers’ workspaces, minimizing cascading risks, and increasing the overall predictability of delivery timelines in cognitively intensive projects.

DEVELOPMENT OF A BASIC MODEL FOR CHOOSING AN INFORMATION SYSTEM CLOUD MIGRATION STRATEGY

Maksym Ievlanov — 2026-05-31

Cloud migration of information systems is currently one of the critical transitional stages of enterprise development on the way to digital transformation. The problem of choosing a strategy for cloud migration of the information system at the pre-migration stage is considered as a task of achieving the target characteristics of quality and productivity, an acceptable level of costs and manageability of implementation in conditions of limited initial data. It is determined that the existing approaches to migration planning are often diverse and fragmented. The formalization of the choice of strategy as a comparison of alternatives in the common space of key performance indicators is proposed. The target indicator profile is introduced as a set of minimum acceptable thresholds after migration, as well as important weights that reflect the priorities of stakeholders and allow you to explicitly capture which requirements are critical. The suitability of each alternative as a measure of failure to achieve the target thresholds for indicators where the predicted state did not meet the requirements is determined. The compromise between the achievement of the target profile and the complexity of implementation is considered, which made it possible to control the balance between the expected improvements and resource costs for the transformation of the information system. Experimental verification of the model on a given set of input data was performed, which confirmed the determinism and reproducibility of the results. With fixed initial values of indicators, normalization rules, threshold goals, weights of importance and compromise parameters, the model formed an unambiguous ranking of strategies and made it possible to identify which indicators formed the main deficit and in which directions changes were needed to reduce it. It is substantiated that such a formulation strengthened the transparency and reasoning of the choice of a cloud migration strategy. Further areas of research are defined as calibration of model parameters on empirical examples, analysis of the sensitivity of results to weights and thresholds, considering the uncertainty of indicator forecasts, as well as expanding the mechanisms of scenario comparison to increase the portability of recommendations for different domains and types of information systems.

GOVERNECE BY PROJECTS AS A METHODOLOGICAL FRAMEWORK FOR THE STRATEGIC DEVELOPMENT OF EDUCATIONAL AND SCIENTIFIC INSTITUTIONS IN UKRAINE

Vitaliy Kukharskyy — 2026-05-31

The transformation of Ukraine’s higher education system under conditions of European integration, decentralization of public governance, and wartime challenges necessitates a shift from administrative–hierarchical models toward adaptive, results-oriented forms of governance. The purpose of this article is to substantiate the concept of governance by projects as a methodological foundation for the strategic development of educational and scientific institutions in Ukraine. The methodological framework of the study is based on governance theory, project and portfolio management concepts, and approaches to interactive and adaptive governance, combined with methods of systems analysis, bibliometric review, and formalized modeling. The evolution of university governance models within the European Higher Education Area is analyzed, and institutional drivers of the transition toward project-oriented forms of strategy implementation are identified.The external and internal dimensions of project governance are examined, within which projects are conceptualized as the basic units of strategic goal implementation, while project portfolios serve as instruments for aligning resources, priorities, and expected outcomes. The study synthesizes approaches from leading international project management frameworks and demonstrates the importance of integrating contractual, relational, and governmental governance mechanisms to ensure strategic coherence and institutional resilience of universities.The scientific novelty of the research lies in the development of a mathematical model of adaptive project governance that formalizes the relationships among strategic development objectives, project portfolios, resource constraints, risks, and performance indicators. The proposed model enables quantitative evaluation of managerial decisions, supports scenario-based planning, operationalizes “stop / review / scale” decision mechanisms, and enhances the robustness of strategic choice under conditions of uncertainty. The practical significance of the results consists in the applicability of the model for designing strategic management systems in Ukrainian higher education institutions during periods of systemic transformation and post-war recovery.

AN ALGORITHM FOR COORDINATING THE START TIME, WORK VOLUMES, AND EXECUTION RATES IN TECHNOLOGICAL SYSTEM PROJECTS

Pavlo Lub — 2026-05-31

The article addresses an information and management problem aimed at improving the efficiency of managing projects of technological systems in arable farming, taking into account the probabilistic influence of agrometeorological conditions and the combined impact of the project environment. Emphasis is placed on the fact that the probabilistic nature of natural and climatic conditions significantly complicates the coordination of schedules, work volumes, and execution rates, which directly affects crop yields and the economic performance of agricultural enterprises. The expediency of applying modern information technologies, modeling methods, and information-analytical systems to support managerial decision-making during the planning and implementation of technological system projects is substantiated. The theoretical prerequisites related to the systemic nature and partial controllability of the processes forming the efficiency indicators of such projects are outlined. A methodological approach based on the integration of open information resources, long-term agrometeorological data, and production observation results with IT services for simulation modeling of seasonal work execution in virtual projects of technological systems is presented. An algorithm for coordinating the components of arable farming technological system projects has been developed, along with a software tool that enables statistical simulation modeling of the relevant project works and, consequently, the evaluation of performance indicators, timeliness, statistical characteristics, risks, and patterns of change. The structure and main stages of functioning of a statistical simulation model of a virtual project are described, allowing the reproduction of the probabilistic dynamics of the project environment, the assessment of the naturally determined time fund for work execution and the productivity of technical equipment, as well as repeated analysis of alternative project implementation scenarios. The methodology was practically tested using sugar beet harvesting projects based on long-term data from an agrometeorological station. The results of computer experiments made it possible to determine the statistical characteristics of the optimal start time of operations and the actually harvested areas, as well as to substantiate the corresponding distribution laws. The study confirms the possibility of improving the validity of managerial decisions, reducing risks, and ensuring maximum crop harvesting by coordinating the start time, volumes, and execution rates of works while accounting for the probabilistic behavior of the project environment.

RISK FORECASTING IN THE MANAGEMENT OF EXTERNAL FACTORS AFFECTING IT PROJECTS UNDER A FIXED BUDGET

Viktor Morozov — 2026-05-31

The study investigates project management methods and risk forecasting in fixed-budget IT outsourcing projects considering the nonlinear dynamics of the external environment. An integrated approach is proposed, combining traditional project management practices with modern risk forecasting techniques, including statistical models, time series analysis, and simulation methods. The research models the impact of market fluctuations, technological uncertainty, and regulatory changes on key project indicators, such as timelines, costs, and final product quality. Results demonstrate that risk forecasting enhances budget and resource planning accuracy, reduces the probability of schedule overruns, and ensures stability in project execution. The proposed methodology encompasses risk identification, quantitative assessment of probability and impact, and adaptation of the project management plan to environmental changes. Modeling outcomes define decision-making priorities and optimal response strategies to unforeseen factors. Practical implementation of this approach allows organizations to increase the efficiency of fixed-budget IT outsourcing projects, minimize financial losses, and improve product quality under variable market conditions. The study also outlines directions for future research, including the application of machine learning and big data to enhance risk forecasting accuracy and automate project management processes.

A VERBAL DESCRIPTION OF THE TECHNOLOGY FOR PLANNING THE IMPROVEMENT OF THE QUALITY OF THE SOFTWARE DEVELOPMENT PROCESS

Volodymyr Sokol — 2026-05-31

It is noted that to solve the problem of developing information technology for planning the improvement of the quality of the software development process (SDP) based on quantitative scales of maturity models, firstly, it is necessary to develop a technology at the verbal level that consists of separate stages for solving the problem at hand. At the first stage of the technology, the structure of the SP is formalised and its quality is assessed based on the point-based qualitative scales of the Capability Maturity Model Integration (CMMI) and Software Process Improvement and Capability Determination (SPICE) maturity models. The second stage identifies the main shortcomings of using qualitative point scales and proposes a method for synthesising balanced quantitative scales based on the use of utility functions. The third stage of the technology involves assessing the quality of individual components of the software development process based on balanced scales of CMMI and SPICE maturity models. At the fourth stage, quantitative scales are used to synthesise dynamic models for planning quality improvement in a set of focus areas of the CMMI model and SPICE model processes. The fifth stage is devoted to the use of the ‘Kyiv broom’ algorithm, which allows solving the problem of planning software quality improvement. The sixth stage considers the main components of applied information technology, including the architectural synthesis of the system that should implement the developed models, methods, and algorithms. The seventh and eighth stages are devoted to parametric analysis and the formation of effective solutions, on the basis of which the decision-maker determines the development option for the software product. The ways for further research are presented, including: the development of static models for planning the improvement of software development quality based on quantitative balanced scales of maturity models; the development of applied information technology for sliding planning based on static and dynamic maturity models.

INTELLIGENT FORECASTING OF ENVIRONMENTAL RISKS IN CIRCULAR ORGANIC WASTE MANAGEMENT SYSTEMS

Аnatoliy Тryhuba — 2026-05-31

An intelligent system for forecasting and integrated assessment of environmental risks in circular organic waste management systems is developed with a focus on the municipal governance level. The proposed approach is based on the integration of multi-source data flows from sensor networks, geographic information systems, public registries, municipal information platforms, and utility services into a unified analytical and predictive framework. A structural–functional model of the intelligent system is constructed as a multi-layer architecture that combines data acquisition and aggregation modules, an analytical–integration layer, a predictive modeling block, an environmental risk assessment module, and a decision support system, complemented by feedback, adaptation, and learning mechanisms. A structural-logical model for the integrated assessment of environmental risks is developed to formalize the forecasting of hazardous events by combining probabilistic estimation of event occurrence with quantitative evaluation of potential environmental impacts. An integral risk index is proposed as a unified multi-factor indicator of environmental hazard, enabling standardized risk measurement, classification of risk states, and their transformation into formalized management decisions. A risk-oriented categorization logic is implemented to support scenario-based response strategies and adaptive management processes. The obtained results establish a coherent methodological foundation for building intelligent environmental safety systems within the framework of circular economy and digital governance. The proposed models provide systemic integration of forecasting, analytics, risk assessment, and decision support, forming a unified management contour for organic waste systems. This creates practical preconditions for the implementation of risk-oriented management strategies at the community level, strengthening the ecological resilience of municipal infrastructures and supporting scientifically grounded digital transformation of environmental governance processes.