The role of forecasting on bullwhip effect for E-SCM applications

Abstract

The bullwhip effect represents the information distortion in customer demand between orders to supplier and sales to the buyer. Demand forecasting is one of the main causes of the bullwhip effect. The purpose of this study is to analyze the impact of exponential smoothing forecasts on the bullwhip effect for electronic supply chain management (E-SCM) applications. A simulation model is developed to experiment the different scenarios of selecting right parameters for the exponential smoothing forecasting technique. It is found that longer lead times and poor selection of forecasting model parameters lead to strong bullwhip effect in E-SCM. In contrast, increased seasonality helps to reduce the bullwhip effect. The most significant managerial implication of this study lies in the need to reduce lead times along the E-supply chain to mitigate the bullwhip effect. While high seasonality would reduce the forecast accuracy, it has a positive influence on the reduction of bullwhip effect. E-SCM managers are therefore strongly suggested to utilize exponential smoothing by selecting lower values for α and β and a mid-value for γ to keep the bullwhip ratio low, while at the same time to increase forecast accuracy.