This study analyzed time-series forecasting methods on material usage to predict future material usage at the warehouse level. An accurate forecast of material usage will help the material management and Procurement team to plan their procurement better and cost-effectively. These forecasting methods were evaluated with data collected from ERP backend tables. Various statistical analysis has been performed and machine learning algorithms have been applied. Some of the machine learning algorithms include average, exponential smoothing, autoregressive integrated moving average (ARIMA), Facebook inhouse python package – fbprophet, Seasonal Auto-Regressive Integrated Moving Average (SARIMA). To forecast accurately, different models are applied based on the frequency of material usage.