PERFORMANCE ASSESSMENT AS A BASIS FOR PREDICTIVE MANAGEMENT IN CUSTOMS AUTHORITIES
Abstract
This paper positions performance assessment of customs authorities as a cornerstone for building a predictive management framework. The authors propose an integrated methodology that combines a balanced scorecard with fuzzy logic inference to compute integral estimates Ei, which reflect the extent to which strategic objectives are met across each hierarchical level. A vertical consistency criterion is introduced, requiring E0≤E1≤E2≤E3; its violation serves as an early warning of an “implementation gap.” Building on these diagnostics, the study outlines a closed-loop governance cycle – “monitoring – benchmarking – intervention” – that enables a shift from reactive measures to proactive, predictive management. Applied to the Federal Customs Service of Russia data spanning 2013–2024, the methodology reveals a persistent “adaptive paradox”: performance steadily improves even amid exogenous shocks. The estimation procedure has been validated against expert judgments in the domain of economic security, yielding strong agreement (ICC = 0.86, MAE < 0.05, and 92 % concordance in qualitative classification). The paradox is further illustrated through the reorientation of trade flows to the Far East during 2022–2024. The findings are intended to inform the design of decision support systems that emphasize effectiveness diagnostics and forward-looking governance under uncertainty