An improved demand forecasting method to reduce bullwhip effect in supply chains |
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| Affiliation: | 1. Sobey School of Business, Saint Mary’s University, Halifax, NS B3H 2W3, Canada;2. Charlton College of Business, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA;1. CMR Institute of Technology, AECS Layout, Bangalore, Karnataka 560037, India;2. Christ University, Hosur Road, Bangalore, Karnataka 560029, India;1. Department of Statistics, National Cheng Kung University, Tainan 70101, Taiwan, ROC;2. Department of Applied Mathematics, National Chiayi University, Chiayi 60004, Taiwan, ROC;1. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK;2. Dpto. de Lenguajes y Sistemas Informáticos, UNED, c/Juan del Rosal 16, 28040 Madrid, Spain;1. Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore, Singapore;1. DEI, University of Padua, viale Gradenigo 6, Padua, Italy;2. ELT, Tampere University of Technology, Korkeakoulunkatu 3, Tampere 33720, Finland;3. Computer Information Systems, Missouri State University, 901 S. National, Springfield, MO 65804, USA;4. BioMediTech, Biokatu 10, Tampere 33520, Finland |
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| Abstract: | 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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| Keywords: | Supply chain management Supply chain uncertainty Bullwhip effect Autoregressive Integrated Moving Average (ARIMA) Discrete wavelet transforms Artificial neural networks |
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