The role of digital platforms in monitoring and managing material supply
DOI:
https://doi.org/10.32347/2707-501x.2026.57(1).122-135Keywords:
digital platforms, SCM, BIM integration, supply monitoring, logistics risks, adaptive algorithms, fractal resilience, shortage forecastingAbstract
The study reveals the role of digital platforms in monitoring and managing material supply in construction projects as a key element of modern logistics architecture. It is substantiated that digital platforms are evolving from tracking tools into multi-level adaptive systems capable of forecasting, self-learning, and autonomous response to deviations. Integrated models of predictive monitoring are examined, including concepts of multi-level reactive routing, vector-based redistribution of criticality, and context-sensitive coordination of material flows. The architecture of digital platforms such as “Smart SCM+” is analyzed, combining accounting modules, risk analytics, dynamic routing, and adaptive task prioritization. It is proven that the implementation of nonlinear risk functions, multi-factor weighting coefficients, and feedback mechanisms reduces delays and shortens system response time to changes in the logistics environment.
Particular attention is given to the integration of BIM models with SCM platforms through mechanisms of indicative material dependency and information balance. Such interoperability enables the forecasting of shortages before actual supply disruptions occur. The fractal architecture of digital platforms is investigated, providing autonomy of local control units and their capacity for self-recovery in the event of central control failure. It is established that the application of recursive adaptation models and stochastic route editing enhances the resilience of the logistics system under turbulent conditions. It is concluded that digital material supply platforms represent not merely automation tools but integrated analytical environments combining logistical, informational, and behavioral management components. Their implementation contributes to increased predictability, loss reduction, and the formation of a new paradigm of adaptive material flow management in construction.
Additionally, it is substantiated that the application of machine learning algorithms and predictive analytics expands the functional capabilities of such platforms, ensuring early detection of critical deviations, optimization of inventories, and enhanced coordination among participants in the logistics chain.
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