Bullwhip effect Prediction in a Single Echelon Supply Chain Using a Regression Analysis
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Abstract
One of the main problems in supply chain systems is the bullwhip effect that can generate a huge cost for the companies in a chain.In this study, the factors and their impacts that can cause the bullwhip effect (order variance and net stock amplification) are investigated by using a simulation-based optimization approach. The proposed meta-prediction model is built using regression analysis, to predict the Total Stage Variance Ratio (TSVR) of the system. A single-echelon supply chain with uncertain customer demand operating under the periodic-review reorder cycle policy is studied. The parameters of the smoothing inventory replenishment and forecasting methods are required to search for their optimality in reducing the TSVR by OptQuest, an optimization tool in ARENA simulation software. Our results can assist decision makers in the management of a supply chain, to realize, benchmark, and reduce the TSVR under an uncertain environment.
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