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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Expert systems with applications Ročník 41; číslo 5; s. 2395 - 2408
Hlavní autori: Jaipuria, Sanjita, Mahapatra, S.S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier Ltd 01.04.2014
Elsevier
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•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. 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.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.09.038