Stochastic characterization of upstream demand processes in a supply chain

By Layth C. Alwan, John J. Liu, Dong-qing Yao. 2003

112 citations recorded by the source corpus.

In the supply chain management area, there has much recent attention to a phenomenon known as the bullwhip effect. The bullwhip effect represents the situation where demand variability increases as one moves up the supply chain. In this paper, we study this effect in an order-up-to supply-chain system when minimum Mean Square Error (MSE) optimal forecasting is employed as opposed to some commonly used simplistic forecasting schemes. We find that depending on the correlative structure of the demand process it is possible to reduce, or even eliminate (i.e., "de-whip"), the bullwhip effect in such a system by using an MSE-optimal forecasting scheme. Beyond the bullwhip effect, we also determine the exact time-series nature of the upstream demand processes.

Subfields: Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/19a4282c7990c88588da25825ba4ac0344fd84ef

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In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

A measure of the bullwhip effect in supply chains with stochastic lead time (2008, methodology): “For pre-specified demand processes such as AR and ARMA, it should be noted that the simple forecasting methods mentioned above can led to specification error [ 24 , 25], and thus the bullwhip effect may be amplified further.”
The bullwhip effect: Progress, trends and directions (2016, methodology): “racy for the specified demand process, and often used as a benchmark (Alwan et al., 2003; Hosoda & Disney, 2006a; Zhang, 2004a).”