This study focuses on the examination of time series data concerning thermal paper consumption at different sales points, motivated by the logistical challenges of a company facing unexpected shortages and the subsequent need for expensive, unscheduled shipments. The research aims to develop a predictive model using a bespoke algorithm to forecast future demand accurately, thereby optimizing shipment volumes and reducing the frequency and cost of unplanned deliveries. Two forecasting algorithms, Prophet and DeepAR, were applied to model the data. Prophet is utilized for its flexibility and ability to adapt to the unique seasonal and trend patterns of individual or grouped time series.