Alternative Forecasting Techniques that Reduce the Bullwhip Effect in a Supply Chain: A Simulation Study

• Main Authors: Francisco Campuzano-Bolarín, Antonio Guillamón Frutos, Ma Del Carmen Ruiz Abellón, Andrej Lisec
• Format: Article
• Language: English
• Published: University of Zagreb, Faculty of Transport and Traffic Sciences 2013-04-01
• Series: Promet (Zagreb)
• Subjects: Bullwhip effect; supply chain; kernel regression; system dynamics model
• Online Access: http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/1294

The research of the Bullwhip effect has given rise to many papers, aimed at both analysing its causes and correcting it by means of various management strategies because it has been considered as one of the critical problems in a supply chain. This study is dealing with one of its principal causes, demand forecasting. Using different simulated demand patterns, alternative forecasting methods are proposed, that can reduce the Bullwhip effect in a supply chain in comparison to the traditional forecasting techniques (moving average, simple exponential smoothing, and ARMA processes). Our main findings show that kernel regression is a good alternative in order to improve important features in the supply chain, such as the Bullwhip, NSAmp, and FillRate.