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A Measure of SCM Bullwhip Effect Under Mixed Autoregressive-Moving Average with Errors Heteroscedasticity (ARMA(1,1)–GARCH(1,1)) Model

A Measure of SCM Bullwhip Effect Under Mixed Autoregressive-Moving Average with Errors... Measuring the bullwhip effect, a phenomenon in which demand variability increases as one moves up the supply chain, is a major issue in supply chain management. In this paper, we quantify the impact of the bullwhip effect on a simple two-stage supply chain consisting of one supplier and one retailer, where the retailer employed a base-stock policy to replenish their inventory. The demand forecast was performed via a mixed autoregressive moving average model, ARMA(1,1), in which ARMA model errors have the GARCH process and the model’s variance changes with time i.e. the model has conditional heteroscedasticity in order to simulate the bullwhip effect which has a non-linear behavior. The definition of bullwhip effect has been expanded to “over time bullwhip effect” (conditional bullwhip effect). We use the minimum mean-square error forecasting technique and also investigate the effects of the autoregressive coefficient, the moving average parameter and the lead time on the bullwhip effect. Moreover, bullwhip effect has been compared in linear demand ARMA and none linear demand ARMA–GARCH process. The results show that the bullwhip effect can be decreased by choosing correct coefficients in demand process through none linear demand process. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

A Measure of SCM Bullwhip Effect Under Mixed Autoregressive-Moving Average with Errors Heteroscedasticity (ARMA(1,1)–GARCH(1,1)) Model

Hadizadeh, Reza; Shojaie, Amir
Annals of Data Science , Volume 4 (1) – Jan 6, 2017
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22 pages

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Business and Management; Business and Management, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Computing Methodologies
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-016-0097-5
Publisher site
See Article on Publisher Site

Abstract

Measuring the bullwhip effect, a phenomenon in which demand variability increases as one moves up the supply chain, is a major issue in supply chain management. In this paper, we quantify the impact of the bullwhip effect on a simple two-stage supply chain consisting of one supplier and one retailer, where the retailer employed a base-stock policy to replenish their inventory. The demand forecast was performed via a mixed autoregressive moving average model, ARMA(1,1), in which ARMA model errors have the GARCH process and the model’s variance changes with time i.e. the model has conditional heteroscedasticity in order to simulate the bullwhip effect which has a non-linear behavior. The definition of bullwhip effect has been expanded to “over time bullwhip effect” (conditional bullwhip effect). We use the minimum mean-square error forecasting technique and also investigate the effects of the autoregressive coefficient, the moving average parameter and the lead time on the bullwhip effect. Moreover, bullwhip effect has been compared in linear demand ARMA and none linear demand ARMA–GARCH process. The results show that the bullwhip effect can be decreased by choosing correct coefficients in demand process through none linear demand process.

Journal

Annals of Data Science – Springer Journals

Published: Jan 6, 2017

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