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

•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 ampl...

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Veröffentlicht in:	Expert systems with applications Jg. 41; H. 5; S. 2395 - 2408
Hauptverfasser:	Jaipuria, Sanjita, Mahapatra, S.S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Ltd 01.04.2014 Elsevier
Schlagworte:	Amplification Applications Applied sciences Artificial intelligence Artificial neural networks Autoregressive Integrated Moving Average (ARIMA) Bullwhip effect Computer science; control theory; systems Connectionism. Neural networks Demand Discrete wavelet transforms Exact sciences and technology Forecasting Inference from stochastic processes; time series analysis Insurance, economics, finance Learning theory Logistics Mathematical models Mathematics Neural networks Operational research and scientific management Operational research. Management science Policies Probability and statistics Sciences and techniques of general use Statistics Supply chain management Supply chain uncertainty Supply chains Supply chain uncertainty Bullwhip effect Supply chain management Discrete wavelet transforms Autoregressive Integrated Moving Average (ARIMA) Artificial neural networks Backorder Costs Demand forecasting Modeling Mean square error Autoregressive integrated moving average model Uncertain system Shortage Estimated worth Open archives Inventory control Reduced order systems Time series Market survey Neural network Complex variable method Wavelet transformation Data models Reduced order model Open data Data gathering Logistics
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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