We optimize inventory by leveraging machine learning techniques to the planning of sales, production, and inventory management. Our primary focus is on assessing the uncertainty associated with sales predictions, which directly impacts safety stock decisions across various inventory strategies. Conventional methods for uncertainty estimation often rely on state space models, widely used in time-series forecasting; however, these models have limitations regarding symmetric distribution assumptions and reduced data efficiency. In contrast, Sequential Predictive Conformal Inference (SPCI) addresses these challenges by non-parametrically estimating residual. We experimentally confirm that SPCI effectively lowers stock levels while minimizing the risk of stockouts.