In the last decade, papers have provided issues for modeling and quantifying bullwhip effect and its solutions. Moreover, investigations on the role of forecasting method, ordering policy, information sharing, and lot sizing rules are conducted in different statues. Chen et al. (2000a, 2000b) quantified bullwhip effect in a single product supply chain and provided a measure for bullwhip
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Int. J. Production Economics
http://dx.doi.org/10.1016/j.ijpe.2015.07.012 0925-5273/& 2015 Elsevier B.V. All rights reserved.
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Int. J. Production Economics 169 (2015) 44–54
ratio in case of moving average and exponential smoothing forecasting. Dejonckheere et al. (2003) suggested a control theory approach for measuring bullwhip effect and proposed a new common replenishment rule that can decrease bullwhip effect. Disney and Towill (2003) introduced an ordering policy that results in taming bullwhip effect. Due to the importance of forecasting methods on the bullwhip effect, many research works have been conducted on the influence of it and developing suitable forecasting methods for bullwhip effect reduction. Zhang (2004) considered several forecasting methods for an inventory control system. The results showed that forecasting methods affect the bullwhip effect. He also presented three measures for bullwhip effect based on each forecasting system. Kim et al. (2006) investigated stochastic lead-time and studied the role of information sharing on the bullwhip effect reduction. Chandra and Grabis (2005) measured bullwhip effect when order size is calculated according to multiple step forecasts using autoregressive models. Luong (2007) investigated effects of leadtime and demand process factors on bullwhip effect when MMSE forecasting method is used. Luong and Phien (2007) research was based on the order of autoregressive demand pattern. They got an interesting result and found that bullwhip effect is not always a straightly function of lead-time. They showed that in some instances, bullwhip effect could be reduced when lead-time increases. Gaalman and Disney (2009) investigated the behavior of the order up to policy for ARMA(2,2) demand process with arbitrary lead-time. Jaksic and Rusjan (2008) demonstrated that using some replenishment rules we could decrease the bullwhip effect. Zhang and Burke (2011) perused compound causes for the bullwhip effect based on an inventory system with multiple pricesensitive demands. Their research was conducted on two bullwhip effect measures: demand stream individually and aggregated demand.