Investigating Maritime Ports’ Operational Performance Determinants: An Empirical Study of Operational Delays and Moroccan Container Terminal Productivity

Authors

  • Zakaria ELKHARMALI National School of Business and Management of tangier, Abdelmalek Essaâdi University, Morocco
  • Ouail EL KHARRAZ National School of Business and Management of tangier, Abdelmalek Essaâdi University, Morocco

Keywords:

Operational delays, Gross Crane Productivity (GCP), Automated container terminals, Integrated scheduling, Smart port performance

Abstract

This study investigates how operational delays influence container terminal productivity by quantifying their effect on Gross Crane Productivity (GCP) at Moroccan container terminals. Based on six months of calls data (April–September 2025), we used a simple linear regression based on a sample of 422 observations.The distribution of GCP shows a slight right skew, with a stable operating range of 28–35 moves per crane-hour, whereas delays are heavily right-skewed, mostly under 10 minutes with occasional long outliers. Ordinary Least Squares results reveal a significant negative association between delays and GCP (β = −0.46, p < 0.001), indicating that each additional minute of delay lowers productivity by about 0.46 moves per hour, delays account for roughly 20% of the variance in GCP (R² = 0.20). LOWESS analysis highlights a tolerance threshold near 10 minutes, after which productivity drops sharply due to congestion propagation and loss of synchronization between quay and yard operations. Over the study period, cumulative delays decreased by nearly half while average GCP improved by approximately 25%, suggesting learning effects, better planning, and enhanced coordination. Overall, the findings show how micro-level time losses affect terminal productivity and connect directly to the smart-port agenda, emphasizing that automation and digital connectivity yield the greatest benefits when supported by integrated scheduling and strong organizational readiness. The study recommends the development of predictive delay analytics (e.g., AIS combined with machine learning) and real-time, integrated equipment scheduling as key levers to sustain both operational efficiency and environmental performance.

JEL Classification : R41, L91, C21, C51

Type du papier : Empirical Research

Downloads

Published

2025-12-18

Issue

Section

Articles