A Manager-Assisted Hybrid Decision Support System Using Advanced AI Models for Demand Forecasting and Inventory Risk Management in Fashion Manufacturing and Distribution

Fashion enterprises face demand uncertainty due to seasonality, short product life cycles, and dense color–size variation: excessive ordering enlarges aged-inventory risk, whereas insufficient ordering causes stockouts. This study develops and validates a manager-assisted hybrid decision support system (DSS) integrating demand forecasting, reorder-candidate identification, aged-inventory risk detection, and ranking-based prioritization. Operational data from a Korean fashion enterprise (4,130,603 sales and 5,065,027 order records) were reconstructed into a style-level monthly panel of 263,260 observations and split chronologically into training (2012–2014), validation (2015), and independent test (2016) periods. Three operational baselines and thirteen data-driven models were evaluated. Added comparisons with PatchTST, TimeFormer, MoFo, and CycleNet yielded 2016 test WAPE values of 0.6819, 0.7095, 0.7126, and 0.7072, respectively, whereas the Hybrid DSS achieved 0.6477. The DSS also attained a reorder F1-score of 0.7983, a Hit@50 of 47.04%, and an NDCG@50 of 75.12%. In policy sensitivity analysis, shortage growth varied from 39.5% to 62.5%, while aged-inventory reduction remained within 23.0–24.6%. The results show that forecasting accuracy alone is insufficient and that inventory-policy effects require explicit sensitivity and managerial review.