A Comparison of Bullwhip Effect under Various Forecasting Techniques in Supply Chains with Two Retailers

Junhai Ma,Xiaogang Ma

doi:10.1155/2013/796384

Abstract

We examine the impact of three forecasting methods on the bullwhip effect in a two-stage supply chain with one supplier and two retailers. A first order mixed autoregressive-moving average model (ARMA(1, 1)) performs the demand forecast and an order-up-to inventory policy characterizes the inventory decision. The bullwhip effect is measured, respectively, under the minimum mean-squared error (MMSE), moving average (MA), and exponential smoothing (ES) forecasting techniques. The effect of parameters on the bullwhip effect under three forecasting methods is analyzed and the bullwhip effect under three forecasting methods is compared. Conclusions indicate that different forecasting methods lead to different bullwhip effects caused by lead time, underlying parameters of the demand process, market competition, and the consistency of demand volatility between two retailers. Moreover, some suggestions are present to help managers to select the forecasting method that yields the lowest bullwhip effect.

Highlights

In a supply chain, as moving backward from a downstream member to an upstream member, the variance of order quantities of orders placed by the downstream member to its upstream member tends to be amplified
The bullwhip effect was measured under the MMSE, MA, and ES forecasting methods respectively
For three bullwhip effect expressions, we investigated the effect of lead-times, autoregressive coefficients, moving average parameters, and the correlation coefficients between two retailers on the bullwhip effect

Summary

Introduction

As moving backward from a downstream member to an upstream member, the variance of order quantities of orders placed by the downstream member to its (immediate) upstream member tends to be amplified. Using a first-order autoregressive (AR(1)) demand process, Chen et al [6, 7] investigated the impact of the MA and ES forecasting methods on the bullwhip effect for a simple, two-stage supply chain with one supplier and one retailer. Luong [9] measured the bullwhip effect for a simple two-stage supply chain that includes only one retailer and one supplier in the environment where the retailer employs the order-up-to inventory policy for their inventory and demand forecast is performed through the AR(1) model, and the effect of autoregressive coefficient and lead time on this measure was investigated. For the same two-stage supply chain, Duc et al [10] investigated effects of the autoregressive coefficient, the moving average parameter, and the lead time on the bullwhip effect when the retailer performed through the ARMA(1, 1) model.

Supply Chain Model

The Measure of the Bullwhip Effect under Various Forecasting Techniques

Analysis and Comparison for Various Forecasting Methods

Conclusions