The impact of stochastic lead times on the bullwhip effect

TL;DR

This paper reviews how stochastic lead times influence the bullwhip effect in supply chains, providing models, real data validation, and an analytical expression to quantify the effect, highlighting key factors like lead time distribution and forecasting delay.

Contribution

It introduces an analytical model quantifying the bullwhip effect considering stochastic lead times and validates it with real data, enhancing understanding of key influencing factors.

Findings

Real lead times can be modeled as i.i.d. random variables.

The distribution of lead times and forecasting delay are main factors of the bullwhip effect.

The analytical expression helps quantify the impact of stochastic lead times.

Abstract

In this article we want to review the research state on the bullwhip effect in supply chains with stochastic lead times and give a contribution to quantifying the bullwhip effect. 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. Using real data we confirm that real lead times are stochastic and can be modeled by a sequence of independent identically distributed random variables. Moreover we modify a model where stochastic lead times and lead time demand forecasting are considered and give an analytical expression for the bullwhip effect measure which indicates that the distribution of a lead time and the delay parameter of the lead time demand prediction are the main factors of the bullwhip phenomenon. Moreover we analyze a recent paper of Michna and Nielsen adding simulation results.