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Meta-Prediction Models for Bullwhip Effect Prediction of a Supply Chain Using Regression Analysis

Meta-Prediction Models for Bullwhip Effect Prediction of a Supply Chain Using Regression Analysis

Navee Chiadamrong, Nont Sarnrak
Copyright: © 2021 |Volume: 14 |Issue: 4 |Pages: 36
ISSN: 1935-5726|EISSN: 1935-5734|EISBN13: 9781799859932|DOI: 10.4018/IJISSCM.2021100103
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MLA

Chiadamrong, Navee, and Nont Sarnrak. "Meta-Prediction Models for Bullwhip Effect Prediction of a Supply Chain Using Regression Analysis." IJISSCM vol.14, no.4 2021: pp.36-71. http://doi.org/10.4018/IJISSCM.2021100103

APA

Chiadamrong, N. & Sarnrak, N. (2021). Meta-Prediction Models for Bullwhip Effect Prediction of a Supply Chain Using Regression Analysis. International Journal of Information Systems and Supply Chain Management (IJISSCM), 14(4), 36-71. http://doi.org/10.4018/IJISSCM.2021100103

Chicago

Chiadamrong, Navee, and Nont Sarnrak. "Meta-Prediction Models for Bullwhip Effect Prediction of a Supply Chain Using Regression Analysis," International Journal of Information Systems and Supply Chain Management (IJISSCM) 14, no.4: 36-71. http://doi.org/10.4018/IJISSCM.2021100103

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Abstract

In this study, the main factors that can cause the bullwhip effect and stock amplification are investigated using a simulation-based optimization approach and regression analysis. A two-echelon supply chain with uncertain customer demand and delivery lead time operating with the periodic-review reorder cycle policy is studied. The parameters of smoothing inventory replenishment and forecasting methods are required. These parameters are optimized in terms of minimizing the Total Stage Variance Ratios (TSVRs) of both echelons. The results show that even though all factors of interest have an impact on the bullwhip effect, using smoothing proportional controllers can reduce TSVRs (the sum of the order varaince ratio and net stock amplification). The meta-prediction models can effectively help predict the amount of the bullwhip effect of a chain under various situations with an average MAPE of less than 11%. The results can assist decision makers in the management of a supply chain to realize, benchmark with the optimal results, and reduce the TSVRs under an uncertain environment.

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