Inherent Complexity Research on the Bullwhip Effect in Supply Chains with Two Retailers: The Impact of Three Forecasting Methods Considering Market Share

Junhai Ma,Binshuo Bao,Xiaogang Ma

doi:10.1155/2014/306907

Abstract

An important phenomenon in supply chain management which is known as the bullwhip effect suggests that demand variability increases as one moves up a supply chain. This paper contrasts the bullwhip effect for a two-stage supply chain consisting of one supplier and two retailers under three forecasting methods based on the market share. We can quantify the correlation coefficient between the two retailers clearly, in consideration of market share. The two retailers both employ the order-up-to inventory policy for replenishments. The bullwhip effect is measured, respectively, under the minimum mean squared error (MMSE), moving average (MA), and exponential smoothing (ES) forecasting methods. The effect of autoregressive coefficient, lead time, and the market share on a bullwhip effect measure is investigated by using algebraic analysis and numerical simulation. And the comparison of the bullwhip effect under three forecasting methods is conducted. The conclusion suggests that different forecasting methods and various parameters lead to different bullwhip effects. Hence, the corresponding forecasting method should be chosen by the managers under different parameters in practice.

Highlights

In the research of modern logistics and supply chain management, a significant phenomenon which is called the bullwhip effect has attracted the attention of researchers and practitioners alike
When φ1 is smaller than a certain value, the bullwhip effect becomes smaller with α becoming larger, when φ1 is larger than that certain value and smaller than another certain value, the bullwhip effect becomes larger with αbecoming larger, when φ1 is larger than another certain value, the bullwhip effect becomes smaller with αbecoming larger
This paper contrasts the bullwhip effect based on three different forecasting methods for a simple inventory system with an AR(1) demand

Summary

Introduction

In the research of modern logistics and supply chain management, a significant phenomenon which is called the bullwhip effect has attracted the attention of researchers and practitioners alike. Chen et al [13, 14] quantified the bullwhip effect for supply chains using moving average and exponential smoothing techniques for demand forecasts. In their works, it was assumed that members of the chain employ base stock policy for their inventory system. Zhang [16] investigated the impact of different forecasting methods on the bullwhip effect for a simple inventory system with a first-order autoregressive demand process.

A Supply Chain Model

The Measure of the Bullwhip Effect

The Analysis of Parameters under the MMSE Forecasting

The Analysis of Parameters under the MA Forecasting

Conclusions