Quantifying the bullwhip effect in a supply chain with stochastic lead time

Jeon G. Kim, Dean Chatfield, Terry P. Harrison, Jack C. Hayya

Research output: Contribution to journalArticlepeer-review

131 Scopus citations


In a recent paper, Dejonckheere, Disney, Lambrecht, and Towill [European Journal of Operational Research 147 (2003) 567] used control systems engineering (transfer functions, frequency response, spectral analysis) to quantify the bullwhip effect. In the present paper, we, like Chen, Ryan, Drezner, and Simchi-Levi [Management Science 46 (2000) 436], use the statistical method. But our method extends Dejonckheere et al. and Chen et al. in that we include stochastic lead time and provide expressions for quantifying the bullwhip effect, both with information sharing and without information sharing. We use iid demands in a k-stage supply chain for both. By contrast, Chen et al. provide lower bounds using autoregressive demand for information sharing and for information not sharing (with zero safety factor for stocks). Dejonckheere et al. validate Chen et al.'s results for a 2-stage supply chain without information sharing, using both autoregressive and iid normally distributed demands. We estimate the mean and variance of lead-time demand (LTD) from historical LTD data, rather than from the component period demands and lead time. Nevertheless, we also calculate the variance amplification like Chen et al., but with gamma lead times. With constant lead times, which Chen et al. used, our method yields lower variance amplification. As for the effect of information, we find that the variance increases nearly linearly in echelon stage with information sharing but exponentially in echelon stage without information sharing.

Original languageEnglish (US)
Pages (from-to)617-636
Number of pages20
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - Sep 1 2006

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


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