This study proposes an integrated artificial intelligence-based framework for modeling and predicting quay crane productivity and operational delays in conventional container terminals, addressing key limitations in the existing port analytics literature. The research introduces a novel dual-mode machine learning architecture that explicitly separates retrospective prediction (forecast mode) from pre-operational decision support (decision mode), addressing a critical gap in existing literature where predictive models are rarely aligned with real-world informational constraints. The framework is applied to a high-resolution, real-world dataset comprising ship-level operations over a three-year period (2023–2025), incorporating a structured representation of 27 delay types and multiple resource allocation variables. A multi-indicator modeling strategy is employed, simultaneously analyzing four productivity metrics (RQCP, GMPH, WBMPH and NMPH), thus allowing for a systematic comparison of their structural sensitivities to delays, congestion, and equipment utilization. The results reveal a clear hierarchy of predictability and operational behavior: structurally driven indicators such as RQCP and GMPH exhibit high predictive stability, while delay-sensitive indicators such as NMPH display greater variability, reflecting real-time operational disruptions. The consistent model performance in forecasting and decision-making indicates significant predictive value in pre-operational variables, endorsing its utility for uncertain decision-making. Sensitivity analysis reveals a critical nonlinear congestion threshold affecting predictive accuracy under extreme operational strain. Employing a combination of multi-indicator productivity modeling, structured delay classification, and ensemble learning within an integrated analytical framework, this research enhances both methodological and practical insights into port operations, aiding in merging predictive analytics with operational decision-making in container terminals to enhance resource allocation, delay handling, and container terminal efficiency.
Partene et al. (Sun,) studied this question.