Predicting Vessel Time at Berth for Smart Berth Planning: A Machine-Learning Approach on a Governed Data Lakehouse at the Port of Sines

Background: Accurate prediction of vessel time at berth is essential for smart berth planning, yet berth-allocation research typically treats processing time as a given input rather than a learned variable. This study develops a governed data lakehouse blueprint for port operational data and uses berth-time prediction at the Port of Sines, Portugal, to establish what routinely collected administrative records support. Methods: Following a design science research approach, a Bronze–Silver–Gold medallion pipeline was realised twice from one design, on a managed cloud platform and on single-node open-source components, demonstrating portability rather than vendor dependence. Four years of administrative data (October 2020 to September 2024) yielded 8148 berth visits. Results: Under rolling-origin temporal validation across five cut-offs, a Random Forest regressor attained a mean R2 of 0.260 and a mean absolute error of 10.1 h, against 13.5 h for the median-stay heuristic, predicting 52.6% of visits within six hours. SHapley Additive exPlanations identified the vessel’s own previous stay, the berth and vessel size as the strongest predictors. Pipeline measurement shows near-linear scaling and a hundredfold query speed-up over raw-source recomputation. Conclusions: Predictive accuracy remains bounded by the absence of exogenous operational variables; the contribution is the governed data foundation that berth planning requires.