A simulation study of artificial neural networks for nonlinear time-series forecasting
Computers and Operations Research
Evolution of ARMA Demand in Supply Chains
Manufacturing & Service Operations Management
Management Science
Improved supply chain management based on hybrid demand forecasts
Applied Soft Computing
Supply chain modeling in uncertain environment with bi-objective approach
Computers and Industrial Engineering
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
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Accurate forecasting of demand under uncertain environment is one of the vital tasks for improving supply chain activities because order amplification or bullwhip effect (BWE) and net stock amplification (NSAmp) are directly related to the way the demand is forecasted. Improper demand forecasting results in increase in total supply chain cost including shortage cost and backorder cost. However, these issues can be resolved to some extent through a proper demand forecasting mechanism. In this study, an integrated approach of Discrete wavelet transforms (DWT) analysis and artificial neural network (ANN) denoted as DWT-ANN is proposed for demand forecasting. Initially, the proposed model is tested and validated by conducting a comparative study between Autoregressive Integrated Moving Average (ARIMA) and proposed DWT-ANN model using a data set from open literature. Further, the model is tested with demand data collected from three different manufacturing firms. The analysis indicates that the mean square error (MSE) of DWT-ANN is comparatively less than that of the ARIMA model. A better forecasting model generally results in reduction of BWE. Therefore, BWE and NSAmp values are estimated using a base-stock inventory control policy for both DWT-ANN and ARIMA models. It is observed that these parameters are comparatively less in case of DWT-ANN model.