Credit is an economic category and is also a product of the commodity economy, which exists through many socio-economic forms to promote economic growth. Therefore, the objective of this paper is to analyst, compare and forecast domestic credit growth in Vietnam's and China's economy. Thus, the paper is applied by a method of an autoregressive integrated moving average (ARIMA) model. This model is fitted to time series data both to better understand the data and to forecast future points in the series. Hereby, the methodology is selected by Vietnam's bestfit model ARIMA (2,3,1) and China's best-fit model ARIMA (2,3,5). Analytical data are collected from 1996 to 2017, the sample fitted the model and is statistically significant. The result show the forecast result for next years. The Vietnamese and Chinese economy are the open economies and have domestic credit greatly contributed to economic growth. These results are the basis for policymakers to have a general view and define the impact of domestic credit growth on GDP between the two countries.

In this paper, various classical time series forecasting methods were compared to determine the forecasting method with the highest accuracy in predicting demand of the 50cl product of a bottled water supply chain. The classical time series forecasting methods compared are the moving average, weighted moving average, exponential smoothing, adjusted exponential smoothing, linear trend line, Holt’s model, and Winter’s model. These methods were evaluated to determine the method with the least Mean Absolute Deviation (MAD) value and hence the highest forecasting accuracy. From the results, the weighted moving average forecasting method had the lowest MAD value of 1,987, making it the forecasting method with the highest accuracy for predicting the 50cl bottled water demand. While the exponential smoothing forecasting method had the highest MAD value of 2,483, making it the forecasting method with the least accuracy for predicting the 50cl bottled water demand. This research provides a procedure for aiding supply chain analysts in implementing demand forecasting using classical time series forecasting models.