Bullwhip Effect In Supply Chain

Uncertainty can be defined as the deviation from the deterministic knowledge of any system considered. This can be attributed to the information asymmetry that exists in real scenarios. With respect to supply chain the uncertainty occurs when there is a mismatch between demand and supply / capacity available which is the result of inability to forecast the actual manner. Supply chains tend to increase in complexity and the involvement of numerous suppliers, service providers, and end consumers in a network of relationships causes risks and vulnerability for everyone (Pfohl et al. 2010). Therefore, any organization is likely to have only limited visibility of the supply chain networks, which makes it difficult to forecast accurately the reason

The bullwhip effects tend to bring in complexities in the upstream of the supply chain rather than the downstream. The continuous evolution of the supply chain system has posed many challenges to the business environment as it challenges the bottom-line of the company. Moreover, the advent of e-commerce and globalization has made the business environment focus more on supply chain networks. The measure that is primarily used to counteract the bullwhip effect is effective information sharing across the various companies in the supply chain. There are unique characteristics that are necessary for information systems which supports supply chain management. First, they should be able to support distributed collaboration among companies. Second, a single company cannot govern unidirectional collaborations in supply, but an autonomous involvement of companies is needed. Third, they need a high level of intelligence for planning, scheduling, and change management. Listed below are some of the literature that has already been discussed in the past which deals with various aspects of the supply

1997, Metters 1997, Lee and Whang 1999, Lee and Tang 2000). Lee (2002) portrayed the uncertainties in supply and demand processes by providing information sharing and coordination as measures for reduction. Sahin and Robinson (2002) proposed a systematic framework for correlating the dimensions of supply chain on a single plane to integrate at the operational level. One extreme is represented by no information sharing and no physical flow coordination between supply chain members. In this scenario, each body operates in self-interest using available internal information. The other side of the spectrum is a highly interlinked , decision-making and physical flow control approach, in which all information and decisions are used together to attain global system objectives. This research on studying the impact of different combinations of information sharing and coordination mechanisms on the performance of complex real-life supply chains under real demand data including multiple products is well-justified and addresses an important gap in the