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مدیریت
An improved demand forecasting method to reduce bullwhip effect in
supply chains
]چکیده:
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
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