Impact of Lumpy Demand Forecasting System on Bullwhip Effect in Supply Chain: A Comparative Approach

An important phenomenon in supply chain management, known as bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper, we investigate the influence of advanced and traditional methods to forecasting lumpy demand on the occurrence of bullwhip effect. Knowing the fact that neural networks can better model and forecast the nonlinear and chaotic behavior of lumpy demands in comparison with traditional and usual methods such as exponential smoothing, this paper efficiently employs neural networks as the main core in the proposed forecasting system. Further with the help of a numerical example it is shown that utilization of neural networks was more beneficial and successful in comparison to other forecasting techniques in terms of reduction in bullwhip phenomenon.