A port's overall container throughput (CT) data can be arranged into diverse hierarchical dimensions (cross-sectional or temporal). Wherein the forecasts are generated using the “horses-for-courses” approach to achieve maximum accuracy at the base level. Generally, these base forecasts do not add up across temporal or cross-sectional hierarchies and give rise to the issue of forecast “incoherence”. Accordingly, the present study extends the idea of utilising optimal forecast reconciliation techniques in CT forecasting. However, the frameworks of these techniques do not allow for their interaction across different dimensions of hierarchy present in CT data. Hence, the paper addresses this problem by proposing a cross-temporal forecast reconciliation framework. Further, monthly CT data from the Port of Los Angeles is used for empirical analysis. It is observed that the proposed framework, with exponential smoothing as the base forecasting method, can provide coherent forecasts at all levels and hierarchical dimensions while simultaneously improving the accuracy of the forecasts. Further, the study also devises a mechanism to evaluate the efficacy of the proposed framework in providing decision support for various port operations (the requirement for terminal ground slots and gantry cranes).