Abstract
ABSTRACTIn this article, we analyze the models quantifying the bullwhip effect in supply chains with stochastic lead times and find advantages and disadvantages of their approaches to the bullwhip problem. Moreover, using computer simulation, we find interesting insights into the bullwhip behavior for a particular instance of a multi-echelon supply chain with constant customer demands and random lead times. We confirm the recent finding of Michna and Nielsen that under certain circumstances lead time signal processing is by itself a fundamental cause of bullwhip effect just like demand-signal processing is. The simulation also shows that in this supply chain the delay parameter of demand forecasting smooths the bullwhip effect at the manufacturer level much faster than the delay parameter of lead time forecasting. Additionally, in the supply chain with random demands, the reverse behavior is observed, that is, the delay parameter of lead time forecasting smooths bullwhip effect at the retailer stage much faster than the delay parameter of demand forecasting. At the manufacturer level, the delay parameter of demand forecasting and the delay parameter of lead time forecasting dampen the effect with a similar strength.
Highlights
In this paper, we investigate and review the problem of stochastic lead times in supply chains
We investigate the work of Duc, Luong, and Kim (2008), where stochastic lead times are considered without forecasting and a recent model of Michna and Nielsen (2013) where the bullwhip effect is quantified in the presence of lead time forecasting
It is easy to notice that the delay parameter n can diminish the bullwhip effect if it increases, which practically means that the more accurate forecasting, the smaller the bullwhip effect (which is completely analog to the findings in Chen et al (2000a) on demand forecasting)
Summary
We investigate and review the problem of stochastic lead times in supply chains. We analyze a model (Michna, Nielsen, & Nielsen, 2013) (a slight modification of Kim, Chatfield, Harrison, & Hayya, 2006) where stochastic lead times and lead time demand forecasting are considered. In this model, the analytical expression for the bullwhip effect. We investigate the work of Duc, Luong, and Kim (2008), where stochastic lead times are considered without forecasting and a recent model of Michna and Nielsen (2013) where the bullwhip effect is quantified in the presence of lead time forecasting.
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