<![CDATA[This study addresses an operational procurement problem in university admissions, where alma mater jackets and sports T-shirts for incoming students must be ordered several months before complete size information becomes available. In the case of ITERA admissions, procurement decisions are typically made in March or April, whereas actual student size data are only gradually collected during the re-registration period from April to July. To support earlier and more reliable procurement planning, this study formulates the problem as a size-demand forecasting task covering six categories: S, M, L, XL, XXL, and XXXL. Historical data from 2015 to 2025 were analyzed, with reliable size records concentrated in the 2019–2025 period. The main novelty of this study lies in formulating freshman uniform procurement as a staged forecasting problem that follows the actual admissions workflow. Specifically, the study proposes a hybrid framework that combines: (i) a time-weighted Bayesian Dirichlet–Multinomial model for early-stage aggregate forecasting when current-year size data are not yet available, and (ii) a CatBoostClassifier-based multiclass machine learning model for prediction updates when student attributes become available. Model performance was evaluated using an expanding-window rolling/forward chaining scheme with a one-year forecasting horizon. In addition to conventional historical baselines, the study also included Simple Exponential Smoothing (SES) as a time-series benchmark. Performance was assessed using cross-entropy for size-distribution accuracy, MAE/size and WAPE for quantity prediction, and stockout/overstock for operational impact. The results show that the previous-year proportion remains a strong baseline, while the Bayesian model provides competitive performance and yields posterior uncertainty estimates that are useful for determining safety-oriented order quantities. The statistical analysis further confirms that gender is the most influential predictor of size, while study program, admission track, and province provide complementary but weaker signals. The findings indicate that the proposed framework can support more adaptive and evidence-based procurement planning, reduce the risk of size shortages and excess inventory, and provide a transferable forecasting workflow that may be adapted to other institutions after local recalibration.]]>
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