An improved demand forecasting method to reduce bullwhip effect in supply chains

作者：

Highlights：

• An integrated approach of DWT and ANN is proposed to improve the forecasting accuracy.

• The proposed model is validated with real-life data and compared with ARIMA model.

• The proposed model invariably produces less forecasting error.

• The model leads to reduction in bullwhip effect and net stock amplification in supply chains.

摘要

•An integrated approach of DWT and ANN is proposed to improve the forecasting accuracy.•The proposed model is validated with real-life data and compared with ARIMA model.•The proposed model invariably produces less forecasting error.•The model leads to reduction in bullwhip effect and net stock amplification in supply chains.

论文关键词：Supply chain management,Supply chain uncertainty,Bullwhip effect,Autoregressive Integrated Moving Average (ARIMA),Discrete wavelet transforms,Artificial neural networks

论文评审过程：Available online 2 October 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.09.038