The Impact of Forecasting Methods Combination for Reducing Bullwhip Effect in a Four-level Supply Chain under Variable Demand

Bullwhip effect in a supply chain, makes inefficiencies such as excess inventory and overdue orders during the chain. These problems can be reduced by appropriate predictions. Forecasting must be done in all levels of a supply chain. This research addresses the problem of optimal combination of forecasting to reduce the bullwhip effect in a four-level supply chain when demand is variable. For this purpose, a four-level supply chain has been considered.  Moving average, exponential smoothing, linear regression and multilayer perceptron artificial neural network can be considered for predicting in each level. First, the desired supply chain is simulated for this means. The different combinations of aforementioned forecasting methods are calculated. Then a combination of forecasting methods which minimizes bullwhip effects is selected. Finally, the results are analyzed by variance analysis model. One combination has the lowest bullwhip effects. Moving average, neural networks, exponential smoothing and linear regression are determined for levels: retailer, wholesaler, manufacturer and supplier respectively. Other combinations have less utility