Rostami-Tabar, B., Babai, M.Z., Ali, M. and Boylan, J.E. (2018) The Impact of Temporal Aggregation on Supply Chains with ARMA (1, 1) Demand Processes. European Journal of Environmental Science Operational Research, 273, 920-932.
https://doi.org/10.1016/j.ejor.2018.09.010

has been cited by the following article:

• 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.