Title: Analysis and optimisation of inventory management policies for perishable food products: a simulation study

Authors: Eleonora Bottani; Gino Ferretti; Roberto Montanari; Marta Rinaldi

Addresses: Department of Industrial Engineering, University of Parma, viale G.P. Usberti 181/A – 43124 Parma, Italy ' Department of Industrial Engineering, University of Parma, viale G.P. Usberti 181/A – 43124 Parma, Italy ' Department of Industrial Engineering, University of Parma, viale G.P. Usberti 181/A – 43124 Parma, Italy ' Department of Industrial Engineering, University of Parma, viale G.P. Usberti 181/A – 43124 Parma, Italy

Abstract: In this paper, we analyse three traditional reorder policies, namely economic order interval (EOI), economic order quantity (EOQ) and (S, s), applied to five food products with different shelf-life characteristics; three fresh products with limited shelf-life are considered. An ad hoc simulation model, reproducing a real two-echelon supply chain, was developed under Microsoft ExcelTM to simulate the product flow along the supply chain, according to the three policies. From the simulation, the minimum cost setting is first derived for all policies. Then, additional performance parameters (e.g., the throughput time of items) are computed and compared with the products constraints (e.g., the shelf-life), to assess the real suitability of implementing each policy to the products considered. Because both the supply chain modelled and the products data are derived from a real scenario, our outcomes should be of practical usefulness to inventory managers, to optimise inventory management of perishable products.

Keywords: inventory management; inventory policies; perishable products; simulation; modelling; optimisation; supply chain management; SCM; perishable food products; reorder policies; economic order interval; EOI; economic order quantity; EOQ; fresh products; limited shelf-life; two-echelon supply chains; product flow.

DOI: 10.1504/IJSPM.2014.061429

International Journal of Simulation and Process Modelling, 2014 Vol.9 No.1/2, pp.16 - 32

Received: 22 Jan 2013
Accepted: 04 Sep 2013

Published online: 26 May 2014 *

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