Digital monitoring toolkit for resource and time parameters under conditions of dynamic project interaction

Abstract

The toolkit for digital monitoring of resource and time parameters in construction projects is formed as a multi-level information and analytical system that ensures continuous tracking, forecasting, and adjustment of project dynamics within a changing environment. In contemporary project management practice, monitoring extends beyond the mere recording of actual indicators and transforms into a comprehensive predictive analytics system integrated with BIM, ERP, and IoT digital platforms. Such integration synchronizes spatial models, resource flows, and sensor data received in real time, thereby creating a unified digital management environment.

The study reveals the conceptual foundations for the formation of a digital monitoring toolkit that combines mathematical models of resource depletion, logarithmic assessments of utilization efficiency, integral indicators of productivity losses, and stochastic risk models. The feasibility of applying exponential, logarithmic, and integral functions to formalize the dynamics of resource consumption and the accumulation of time deviations is substantiated. It is demonstrated that the use of multifactor models enables the assessment not only of absolute changes in indicators but also of their sensitivity to external and internal disturbances.

Special attention is given to the functional and analytical interaction of digital platforms. BIM is considered as the spatial and informational basis of monitoring, ERP as the system of resource and financial coordination, and IoT as the sensor layer ensuring data reliability. Their integration forms an adaptive management architecture capable of reconfiguration depending on changes in project parameters.

Within the framework of the research, approaches to forecasting deviations based on probabilistic, stochastic, simulation, and neural network models are systematized. The application of Monte Carlo methods, agent-based modeling, and LSTM architectures enables scenario analysis, assessment of the probability of exceeding critical thresholds, and the generation of early warning signals. This approach transforms monitoring from a reactive function into a proactive instrument of strategic management.

It is concluded that the effectiveness of digital monitoring is determined by the structural coherence of mathematical models, software platforms, and organizational procedures. The integration of analytical mechanisms into a unified coordination system ensures improved resource balance, minimization of time losses, and enhanced project resilience under conditions of uncertainty.