Forecasts made based on information from the member down the stream lead to amplification of demand. Dependence on these downstream pieces of information to plan for inventory often misleads. As such, many upstream members end up having a greater variability of demand (Lee et al. Another cause is the frequent change in prices in the market. Sometimes the manufacturers reduce the prices of their products. This makes more suppliers in the downstream end to do ‘forward buying’. This is in a bid to minimize the purchasing costs. When the prices become normal, the consumers will stop purchasing goods as they used to do when the prices were down. The result is an increased demand at the upstream level. This creates the Bullwhip Effect. It then becomes clear that the consumption patterns are the quantities being bought vary greatly from each other (Lee, Padmanabhan & Whang, 2015).Yao and Zhu,’s 2012 article ‘Information Systems Research. An Empirical Analysis of the US manufacturing chains’ expounds more on the causes of the Bullwhip Effect. I selected this article because it gives a very comprehensive explanation on each cause of the phenomenon. The causes that are tackled in the article include the following: the operational causes and the behavioral causes. Order batching, price fluctuation, lead time, inventory policy, replenishment policy, lack of transparency, demand forecasting and rationing and shortage gaming are in the category of operational causes of the Bullwhip Effect. The article also outlines the behavioral causes which include the following: lack of proper training, fear of going out of stock and neglect or little attention to time delays in making orders. The article explains in detail how the various causes lead to the Bullwhip Effect. Forward buying makes the distributors to make more orders when the manufacturers offer attractive prices for their products. A consequence of this is price fluctuations. When the prices become normal, the consumers stop buying the products until the inventories are depleted. The end result is a big variation between the consumer buying patterns and the amount of product being bought. This is the Bullwhip Effect.Almost all the companies in the supply chain make their demand forecast based on the history of orders. The implication of this is an exaggerated amount demand upstream resulting from
Bhattacharya, R., & Bandyopadhyay, S. (2011). A review of the causes of bullwhip effect in a supply chain. The International Journal of Advanced Manufacturing Technology, 54(9-12), 1245-1261.
Chen, L., & Lee, H. L. (2012). Bullwhip effect measurement and its implications. Operations Research, 60(4), 771-784.
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains1. Sloan management review, 38(3), 93-102.
Lee, S. Y., Klassen, R. D., Furlan, A., & Vinelli, A. (2014). The green bullwhip effect: Transferring environmental requirements along a supply chain. International Journal of Production Economics, 156, 39-51.
Yao, Y., & Zhu, K. X. (2012). Research Note-Do Electronic Linkages Reduce the Bullwhip Effect? An Empirical Analysis of the US Manufacturing Supply Chains. Information Systems Research, 23(3-part-2), 1042-1055.
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