Title: A modelling and simulation approach to assessment of a negative binomial approximation in a multi-echelon inventory system

Authors: Adriano O. Solis; Francesco Longo; Letizia Nicoletti; Pietro Caruso; Elisa Fazzari

Addresses: School of Administrative Studies, York University, Toronto, Ontario M3J 1P3, Canada ' Department of Mechanical, Energy, and Management Engineering, University of Calabria, 87036 Rende, Cosenza, Italy ' Department of Mechanical, Energy, and Management Engineering, University of Calabria, 87036 Rende, Cosenza, Italy ' Department of Mechanical, Energy, and Management Engineering, University of Calabria, 87036 Rende, Cosenza, Italy ' Department of Mechanical, Energy, and Management Engineering, University of Calabria, 87036 Rende, Cosenza, Italy

Abstract: Some studies in the multi-echelon inventory systems literature have used a negative binomial distribution to approximate a critical random variable arising in the model. Graves (1996) developed a multi-echelon inventory model with fixed replenishment intervals, where each site follows a base stock policy. He proposed in the one-warehouse, N-retailer case a negative binomial distribution to approximate a random variable he referred to as 'uncovered demand'. Computational evidence was provided to demonstrate the effectiveness of the approximation. Graves then suggested search procedures for approximately optimal base stock levels at the warehouse and N identical retailers under two customer service criteria: 1) probability of no stockout; 2) fill rate. A separate analytical evaluation of the negative binomial approximation has been preliminarily reported elsewhere. In the current study, we apply a modelling and simulation approach to assess whether the approximation-based search procedures, in fact, lead to optimal or near-optimal stock levels.

Keywords: multi-echelon inventory modelling; supply chain management; SCM; one-warehouse-N-retailer inventory systems; base stock policy; negative binomial approximation; modelling; simulation; optimal stock levels.

DOI: 10.1504/IJSPM.2014.064386

International Journal of Simulation and Process Modelling, 2014 Vol.9 No.3, pp.146 - 156

Available online: 20 Aug 2014 *

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