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A simulation approach to manage finished goods inventory replenishment economically in a mixed push/pull environment

A simulation approach to manage finished goods inventory replenishment economically in a mixed... In this paper we introduce a simulative model, designed and developed to optimally manage an integrated finished goods inventory system, and suitable for adoption in a wide range of make to stock manufacturing firms. The goal of the model is to manage optimally finished goods inventory levels, in order to minimize costs deriving from holding inventory and from adjustment to the master production schedule (MPS) to prevent stock-outs. A trade off is sought between these costs components. The input variables of the model are: safety stock levels, assessed through coefficients k1, k2 and k3, and the stochastic distribution functions of products demand. The model performances are assessed considering the annual total cost for the inventory management. The model is then tested by a numerical application. The case considered refers to a major firm operating in the zootechnical feeds production industry. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Logistics Information Management Emerald Publishing

A simulation approach to manage finished goods inventory replenishment economically in a mixed push/pull environment

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Publisher
Emerald Publishing
Copyright
Copyright © 2002 MCB UP Ltd. All rights reserved.
ISSN
0957-6053
DOI
10.1108/09576050210436129
Publisher site
See Article on Publisher Site

Abstract

In this paper we introduce a simulative model, designed and developed to optimally manage an integrated finished goods inventory system, and suitable for adoption in a wide range of make to stock manufacturing firms. The goal of the model is to manage optimally finished goods inventory levels, in order to minimize costs deriving from holding inventory and from adjustment to the master production schedule (MPS) to prevent stock-outs. A trade off is sought between these costs components. The input variables of the model are: safety stock levels, assessed through coefficients k1, k2 and k3, and the stochastic distribution functions of products demand. The model performances are assessed considering the annual total cost for the inventory management. The model is then tested by a numerical application. The case considered refers to a major firm operating in the zootechnical feeds production industry.

Journal

Logistics Information ManagementEmerald Publishing

Published: Oct 1, 2002

Keywords: Management; Stock control; Optimization; Flexibility; Simulation

References

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