Bullwhip Effect Analysis in a Supply Chain

Bullwhip Effect Analysis in a Supply Chain

Mehdi Najafi (Sharif University of Technology, Iran) and Reza Zanjirani Farahani (Kingston University, UK)
DOI: 10.4018/978-1-4666-2625-6.ch038
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Abstract

In today’s world, all enterprises in a supply chain are attempting to increase both their and the supply chain’s efficiency and effectiveness. Therefore, identification and consideration of factors that prevent enterprises to attain their expected/desired levels of effectiveness are very important. Since bullwhip effect is one of these main factors, being aware of its reasons help enterprises decrease the severity of bullwhip effect by opting proper decisions. Now that forecasting method is one of the most important factors in increasing or decreasing the bullwhip effect, this chapter considers and compares the effects of various forecasting methods on the bullwhip effect. In fact, in this chapter, the effects of various forecasting methods, such as Moving Average, Exponential Smoothing, and Regression, in terms of their associated bullwhip effect, in a four echelon supply chain- including retailer, wholesaler, manufacturer, and supplier- are considered. Then, the bullwhip effect measure is utilized to compare the ineffectiveness of various forecasting methods. Owing to this, the authors generate two sets of demands in the two cases where the demand is constant (no trend) and has an increasing trend, respectively. Then, the chapter ranks the forecasting methods in these two cases and utilizes a statistical method to ascertain the significance of differences among the effects of various methods.
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2. Background

The root of bullwhip effect can be traced back to Forester (1961). Although Forester described the bullwhip effect in some cases and pointed out management policies and decisions can generate variation in supply chain, however, the bullwhip effect owes its renown to Sterman’s studies in inventory management. Sterman (1989) reported his observations from bullwhip effect as “Beer Distribution Game” or simply “Beer Game”. In general, the researches in this area can be divided into three groups. In the first group, the researches focus on analyzing the creators (causes) of the bullwhip effect (for instance: Lee et al. (1997b), Bourland (1996), Miragliotta (2006)) whereas more detailed and specialized subjects are surveyed in the two last groups. The second group includes researches which measure or consider the bullwhip effect generated from creators known in the previous researches and the third group includes the researches which compare the created bullwhip effect generated from various methods or causes. Since this chapter aims to consider the impact of various forecasting methods on the bullwhip effect, we focus on those papers of the second and the third groups which considered the impact of forecasting methods on the bullwhip effect. Generally, four methods of forecasting have been considered so far: Exponential Smoothing, Moving Average, Linear Regression and ARIMA. In addition, it is assumed in these papers that a supply chain consists of three-echelons with one product and the same forecasting method in each echelon. Then, the chain was modeled and the bullwhip effect indices were analyzed. Some of these papers are summarized in Table 1.

Table 1.
Some papers about impact of forecasting method on bullwhip effect
Ref.Studied MethodMethodologyNo. of Echelon
Lee et al. (1997)Exponential SmoothingAnalytical Model and Experimental DataThree
Lee et al. (2000) Exponential SmoothingMathematical Model to Choose Optimized AlphaTwo
Thonemann (2002) Exponential SmoothingMathematical Model to Improve PerformanceThree
Zhang (2003)Moving Average and Exponential SmoothingMathematical Model to Demonstrate Bullwhip EffectTwo
Gang et al. (2005) ARIMA(1)Simulation to Consider Impact of Information Transformation on Bullwhip EffectThree
Chandra et al. (2006)Proposed ModelSimulation to demonstrate Proposed Model Effect on Bullwhip EffectThree
Carbonneau et al. (2006)Proposed ModelSimulation to Comparison Proposed Model with Moving Average and Linear RegressionFour
Liang and Che (2006) Proposed Model By Genetic AlgorithmSimulation to Evaluate Bullwhip EffectMulti
Dhahri and Chabchoub (2007) ARIMAModeling by Goal Programming to Reduce or Eliminate Bullwhip EffectFour

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