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In this note we derive the famous formula of F. Chen, Z. Drezner, J.K. Ryan and D. Simchi-Levi [2000a] for the bullwhip effect measure in a simple two-stage supply chain under the assumption that demands constitute autoregressive structure of order 1. Our approach is a little different than in Chen et. al [2000a] and therefore we obtain the formula as an equality unlike Chen et. al [2000a], where they have it as a lower bound. Moreover, we analyze the bullwhip effect measure formula and in some cases we have different conclusions than in Chen et. al [2000a].
EN
In this article we investigate the classical risk process. We derive a formula for the ruin probability on a finite time horizon for zero initial capital that is Cramer's formula and for an arbitrary initial capital that is Seal's formula. Applying these formulas and the approximation of a gamma process by compound Poisson processes we obtain a formula for the supremum distribution of a gamma process with a linear drift.
EN
This article aims to present the applications of Lévy processes for the stochastic modeling of storage resources. Two cases were considered. In the first one, the volume of supplies to the storehouse is described by a random process (Lévy process), while issuing the products is described by a deterministic and linear function. The second case is reversed: the delivery to the storehouse is described by a linear function (variable: time), while issuing the goods is described by a Lévy process. For both cases the form of the stock level process and examples of its trajectories, when the net supply is a Lévy process, are given. We investigated the following net supply processes: gamma process, α-stable Lévy process with α = 0.5, Cauchy process, Wiener process.
XX
In this article we consider a simple two stage supply chain. We quantify the variance amplification of orders – the bullwhip effect in a model with stochastic lead times. Employing the moving average forecasting method for lead time demands we obtain an exact value of the bullwhip effect measure. We analyze the formula using numerical examples.
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