EXPRESS DIAGNOSTICS AND FUNDAMENTAL DIAGNOSTICS IN CRISIS MANAGEMENT

Abstract

This article examines the complementary roles of express diagnostics and fundamental diagnostics within enterprise anti-crisis management, with a focus on metallurgical firms that operate under high capital intensity and volatile commodity cycles. The study clarifies the notion of express diagnostics as a rapid, low-cost screening of liquidity, solvency, profitability, cash flow, and debt service indicators that enables early detection of destabilizing trends and triggers timely managerial responses. It contrasts this with fundamental diagnostics, defined as an in-depth, hypothesis-driven investigation that combines factor decomposition, scenario building, stress testing, and multi-criteria scoring to uncover root causes, quantify risk transmission channels, and forecast crisis trajectories. Methodologically, the paper proposes a two-level diagnostic architecture: (1) an always-on early-warning layer based on threshold KPIs and simple composite flags embedded in BI dashboards; and (2) a periodic deep-dive layer that integrates cost-curve analysis, working-capital mapping, covenant headroom assessment, and sensitivity models to prices, exchange rates, energy inputs, and logistics constraints. The results are presented as a practical framework that specifies trigger thresholds, escalation paths, data cadence, and governance roles, thereby shortening reaction time, improving forecast reliability, and aligning diagnostic outputs with concrete turnaround levers (cash containment, portfolio re-mix, operational efficiency, debt renegotiation, and optional capacity mothballing). For metallurgical enterprises, the framework emphasizes coupling market-side signals (benchmark prices, spreads, demand indices) with plant-level metrics (yield, utilization, scrap ratio, energy intensity) to distinguish reversible shocks from structural impairments. The paper’s value lies in operational guidance for designing an integrated diagnostic loop that converts weak signals into actionable decisions and links monitoring to remedial programs and post-implementation control. Limitations and avenues for further development include enriching the framework with real-time data feeds, probabilistic forecasting, and domain-specific stress scenarios for supply chain and energy shocks.