Commercial Shift, Not Operational Decline: Explaining Falling Berth Productivity with Interpretable Machine Learning

Background: Gross Berth Productivity (GBP) is a primary terminal benchmark, yet it is routinely read as an efficiency signal, and vessel-call-level evidence is scarce. Methods: From 4463 vessel calls at a major East Asian container terminal (2021–2025), we model GBP with XGBoost and SHAP under a leakage-controlled ablation, adding per-crane, service-level and container-level yard and gate records. Results: Berth productivity falls 24.2%, a medium effect. Gang productivity, the crew-level rate berth productivity commonly read as measuring, falls only 4.2%, a small effect. From 2024 to 2025 berth productivity is statistically equivalent (δ=−0.007), so the decline has leveled off. SHAP identifies cargo volume, not crane speed, as the dominant correlate, stable in 16 of 20 sub-populations and all 200 bootstrap resamples; the model retains R2≈0.64 without the ratio’s components. An additive decomposition assigns 71% of the modeled decline to falling call size—fewer containers handled per vessel call, not fewer calls and not smaller ships—and 8% to crane speed; a shift-share decomposition traces 94% of that fall to portfolio turnover. Crane interference is bounded within ±0.10. Conclusions: Falling berth productivity can reflect a commercial shift to smaller calls rather than operational decline. Evidence is single-terminal.