By Thabane L.
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Extra resources for A closer look at the distribution of number needed to treat (NNT) a Bayesian approach (2003)(en)(6s)
This stands in contrast to the optimal replenishment policy of Scarf in the absence of emergency orders [Sca60]. Scarf describes the fundamental results of the well-known (s, S) base-stock policy which replenishes up to S when the stock level drops to s, see [Sca60]. He requires a known demand distribution which can change over time. Further, he assumes linear ordering, holding, and backlog costs and allows for fixed order costs. This standard (s, S) policy has been extended to dual-source (normal and emergency) replenishment scenarios.
The order is expedited if it does not arrive until T, some point Literature – Order splitting 29 of time after the depletion of the stock at t0 + Q/d. They derive conditions for the existence of a cost-minimal value for T. However, the joint optimization of R, Q, and T is very complex. In their examples, they use a numerical optimization approach and assume a Weibull distribution for the lead time L1 . They find that the additional use of T is superior to the regular ( R, Q) policy. The reviewed literature on expediting orders with two random lead times has shown several interesting points for our work.
All lead times are constant and both retailers are identical in terms of the same ( R, Q) replenishment policy and identical Poisson customer demand. In this context they compare a policy in which each retailer wi only orders from its assigned warehouse Wi with a policy which allows ordering from the non-assigned warehouse in case of a supply shortage, as well. The latter policy yields significantly more savings in their sensitivity analysis on the order quantities and the lead times. From these publications we see that strong assumptions are applied for combined multi-echelon and multi-supplier scenarios.
A closer look at the distribution of number needed to treat (NNT) a Bayesian approach (2003)(en)(6s) by Thabane L.