This study develops a multi-item multi-supplier probabilistic inventory policy for an SME household-goods retailer by considering partial backorders and storage capacity constraints. The case covers 60 fast-moving SKUs supplied by eight fixed suppliers. Two policies are evaluated: a Supplier-Based Policy (SBP) with different ordering intervals across suppliers and an All-Supplier Policy (ASP) with one common interval per month. For historically stockout SKUs, the observed unmet-demand gap of 966 units is reduced to a modeled cumulative expected shortage of six units over seven months. Across all SKUs, both policies maintain monthly demand fulfillment above 95%. Replenishment quantities are adjusted to SKU-level demand and remaining inventory position, reducing unnecessary orders and excess-inventory risk. SBP yields the lowest calculated cost, while ASP offers a simpler schedule. Sensitivity analysis identifies storage capacity as the most influential parameter on the selected interval and total inventory cost.