TITLE:
Forecasting Methods to Reduce Inventory Level in Supply Chain
AUTHORS:
Tiantian Cai, Xiaoshen Li
KEYWORDS:
Supply Chain, Forecasting Method, ARIMA(1, 1, 1) Model, Mean Square Error
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.10 No.2,
February
15,
2022
ABSTRACT: Based on the two-level supply chain composed of suppliers and retailers, we assume that market demand is subject to an ARIMA(1, 1, 1). The supplier uses the minimum mean square error method (MMSE), the simple moving average method (SMA) and the weighted moving average method (WMA) respectively to forecast the market demand. According to the statistical properties of stationary time series, we calculate the mean square error between supplier forecast demand and market demand. Through the simulation, we compare the forecasting effects of the three methods and analyse the influence of the lead-time L and the moving average parameter N on prediction. The results show that the forecasting effect of the MMSE method is the best, of the WMA method is the second, and of the SMA method is the last. The results also show that reducing the lead-time and increasing the moving average parameter improve the prediction accuracy and reduce the supplier inventory level.