In supply chains, variability in replenishment orders often increases as demand information travels up the supply chain, from the retailer to the factory and suppliers. This phenomenon has been recognized as the bullwhip effect (Lee, Padmanabhan, and Whang 1997) and observed in many industries such as Campbell Soup's (Fisher et al. 1997), HP and Proctor & Gamble (Lee, Padmanabhan, and Whang 1997), FMCG (Zotteri 2012) and automotive production (Klug 2013). Forrester (1958) was the first to study this problem through a series of simulation experiments using system dynamics and called it demand amplification. Forrester analyzed the behavior of a linear supply chain under various internal conditions, as well as its response to external changes and shocks, and found that the structure, policies, and interactions within a supply chain cause amplification the variability of demand. Other researchers have developed simulation games to illustrate the existence of the bullwhip effect and its negative impacts in supply chains (Sterman, 1989; Chen and Samroengraja, 2000; Jacobs, 2000; Machuca and Barajas, 1997). Towill, Zhou, and Disney (2007) indicated that the bullwhip effect could lead to stock-outs, large and costly swings in capacity utilization, lower quality products, and considerable production/transportation costs. Lee et al. (1997) identified five fundamental causes of the bullwhip effect: demand signal processing, non-zero lead time, order bundling, price fluctuations, rationing, and the shortage game. Of particular interest to us is demand signal processing where forecasting methods and replenishment rules (stock control policy) are jointly applied to identify when and how much to order. Burbi...... half of the paper ...... is shaped under the influence of three inventory policies: an available inventory policy, an installation inventory policy and a stagger inventory policy. They analytically demonstrated that the set-up stock and staggered stock policies are more stable than the on-hand stock policy. Chandra and Grabis (2005) adopted a simulation modeling approach to study the impact of a material requirements planning (MRP) inventory management approach on the bullwhip effect and inventory performance for the manufacturing unit. supply chain further downstream. They argued that the MRP approach is comparable to the traditional order-up-to approach. Dejonckheere et al. (2003) demonstrated through a control theoretic approach that the bullwhip effect is guaranteed in the order-up-to model regardless of the forecasting method used and they state this without making any assumptions on the demand model.