The Bullwhip Effect in Water Demand Management: Taming It Through an Artificial Neural Networks-Based System

Abstract

The Bullwhip Effect refers to the amplification of the variance of orders and inventories along the supply chain as they move away from the customer. This is considered as the main cause of inefficiencies in the management of a traditional supply chain. However, the Bullwhip Effect is not relevant in the classic system of water distribution, based on long-term supply management. Nevertheless, current circumstances have drawn a new context, which has introduced the concept of Water Demand Management (WDM), in which efficiency and sustainability are of great importance. Then, the time horizon of management has decreased enormously and the supply time takes on an important role. Therefore, the Bullwhip Effect must be considered, as it significantly raises the costs of management. On the one hand, this paper brings evidence that Bullwhip Effect appears in a system of real-time management of water demand. On the other hand, it proposes the application of Artificial Intelligence techniques for its reduction. More specifically, an advanced forecasting system based on Artificial Neural Networks (ANNs) has been used. The Bullwhip Effect is heavily damped.