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Economic Analysis: Theory and Practice
 

A three-product model to manage inventory with random demand

Vol. 16, Iss. 3, MARCH 2017

PDF  Article PDF Version

Received: 26 August 2016

Received in revised form: 27 December 2016

Accepted: 17 January 2017

Available online: 29 March 2017

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: C61

Pages: 561-572

https://doi.org/10.24891/ea.16.3.561

Mitsel' A.A. National Research Tomsk Polytechnic University, Tomsk, Russian Federation
maa@asu.tusur.ru

Stavchuk L.G. National Research Tomsk Polytechnic University, Tomsk, Russian Federation
lyusbk93@gmail.com

Importance The article addresses the inventory management system and offers a stochastic model, which implementation implies significant cost saving when building inventory, as required resources are purchased in the amount of their deficit. This enables to reduce costs for storing the unused resources.
Objectives The aim of the study is to develop a three-product inventory management model with random demand and equal frequency of deliveries with minimum working capital.
Methods While building the model, it is assumed that at the initial stage the first product is purchased in full, the rest products are purchased in part, and then they are additionally during the cycle. The volume of delivery is arbitrary; the frequency of delivery of all resources is the same, but demand for the product is a random variable.
Results We built a three-product model for inventory management with random demand and equal frequency of deliveries under the stipulation that resources are replenished in the amount of their deficit. The simulation rests on the data on materials and supplies arrival at TOO SP VG-Plast.
Conclusions The proposed methodology for building a model to manage inventory with random demand helps obtain an analytical model only on condition that no more than three resources are purchased during the cycle. The offered three-product model enables to save up to 40.8% of current assets.

Keywords: logistics, inventory management, stochastic models, random demand

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