DIAGNOSIS OF "POINTS OF FRAGILITY" IN A DEVELOPER'S BUSINESS MODEL: BIG DATA ANALYSIS METHODOLOGY
Abstract
The article addresses the critical challenge of managing construction development stability amidst the unprecedented turbulence of the war and post-war economy in Ukraine. The study identifies a gap between the stochastic nature of modern projects and outdated, fragmented risk management methods that operate in isolation. It is substantiated that traditional risk management fails to timely identify systemic vulnerabilities – "points of fragility" – which arise at the intersection of financial, logistical, and human capital flows and possess the property of non-linearly amplifying external shocks. The aim of the research is to develop an applied methodology for diagnosing these vulnerabilities using Big Data analytics to enhance business adaptability. Within the framework of the research, an Integrated Fragility Diagnostics Model (IFDM) is proposed, which synthesizes heterogeneous data streams from ERP systems, IoT sensors, and the ProZorro public procurement platform. For financial diagnostics, the author moves beyond deterministic budgeting, introducing a stochastic Cash Flow at Risk (CFaR) approach using Gaussian Mixture Models (GMM) to handle irregular distributions, alongside a modified dynamic Altman Z-score for real-time liquidity monitoring. Logistical fragility is assessed using graph theory to calculate the Supply Chain Resilience Index (SCRI) and Dynamic Bayesian Network Approach (DBNA) to model the "ripple effect" of supplier disruptions. The methodology uniquely leverages open government data to benchmark material prices and verify counterparty reliability. Addressing the critical labor shortage, the study applies logistic regression to predict staff turnover probability by analyzing salary competitiveness, fatigue from overtime, and sentiment data. The article also proposes enhancing the use of the proposed methodology by visualizing diagnostics through "risk heat maps" overlaid on 5D BIM models (3D + time + cost), which allows for the identification of specific building elements at risk. The practical significance of this approach lies in establishing a foundation for scenario planning by development companies and ensuring the viability of construction projects under crisis conditions and during the recovering of Ukraine.
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