In this paper, we address two main issues. First, we determine, through a simulation model, the optimal size and distribution of the safety stocks in a supply network. The “optimal” size of safety stocks results from the minimization of the total logistics cost of the supply network, as a function of the safety stock coefficient (k). In particular, we define the “optimal” size of the safety stocks as the k value which minimizes the total logistics cost of the network. Second, the optimized values of k are used to run the same simulation model under different operating conditions of the network, which are obtained by introducing demand stochasticity, demand seasonality and lead time stochasticity. More precisely, once the optimal size of safety stocks has been set, we carry out further simulations, according to the design of experiments (DOE) procedure, and perform statistical analyses of the resulting outputs, to provide some insights about the design of the supply network under optimal safety stock level. The study is supported by a discrete-event simulation model, developed ad hoc and reproducing 4 different configurations of a fast moving consumer goods (FMCG) network. To run the model, real data related to the FMCG context were used. Results of this study can be useful to supply chain managers, to identify the optimal service level the network should deliver to customers, as well as to understand the behavior of supply networks under optimal safety stock level.
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