Proposed Improvement of Forecasting Using Time Series Forecasting of Fast Moving Consumer Goods

The company discussed in this paper is a national distributor firm that distributes FMCG products. The PPIC division in the company is responsible for forecasting the demand using the combination of the moving average method and intuition according to the interest of the company. However, the PPIC staff never measures the accuracy of their forecasting method. This research paper aims to evaluate the forecasting methods used to predict the demands of 12 classes of A SKU. Four-time series forecasting methods are particularly implemented, i.e., ARIMA, moving average (MA), double exponential smoothing (DES), and linear regression (RL). Forecasting using the ARIMA method is carried out by considering the stationarity of the average and variance of the historical data points. Forecasting using DES is carried out by using the optimal alpha and gamma values of the ARIMA method. The results show that the performance of each forecasting method varies, depending on which demands of class A SKU are predicted. Based on these results, the current forecasting method utilized by the company should be improved using the time series forecasting methods leading to the smallest error values for each class of A SKU.