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Integration of statistical model for optimum inventory and Wilson EOQ model

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Category: Economy and Management

Last Updated on 22 May 2018

Published on 16 May 2018

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Authors:

I. M. Pistunov, Dr. Sc. (Tech.), Prof., orcid.org/0000-0002-9041-8368, National Mining University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.; elenachurika­no­This email address is being protected from spambots. You need JavaScript enabled to view it.

I. A. Bielkina, Cand. Sc.(Econ.), orcid.org/0000-0002-0789-2545, National Mining University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.; elenachurika­no­This email address is being protected from spambots. You need JavaScript enabled to view it.

O. Yu. Churikanova, Cand. Sc.(Econ.), orcid.org/0000-0001-5703-2271, National Mining University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.; elenachurika­no­This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract:

Purpose. Development of method for determining the optimal stock of products, which would allow combining several models of optimal order quantity estimation, excluding errors of each composing model.

Methodology. The analytical model that allows averaging the estimation results obtained by means of statistical and Wilson models is designed. Optimization approach to the problem ensures uniqueness of obtained results and provides proper and unambiguous solution.

Findings. It is statistically substantiated and experimentally confirmed that the statistical and Wilson models, designed to determine the optimal order quantity provide far different results. The capacity of combining the results of these calculations in a single complex is examined.

Originality. For the first time the difference between the data obtained by means of Wilson and statistical models is defined. A formula of combining the results into a single package assuring unification of both approaches is developed.

Practical value. The optimal order supply for each product type on the criteria minimum of unsold goods storage costs or maximum of sale profit or minimum amount of unit storage can be defined having the following consumption parameters: overall demand statistics, net cost plus additional storage costs per unit of product, delivery and placing goods costs, stock capacity, the amount of stock remains, deficit matrix for each commodity type and sales profit per product type. Moreover, consideration of warehouse capacity allows determining the product that ensures maximum of sale profit. The method had been tested on real business data.

References.

1. Andersson, H., Hoff, A., Christiansen, M., Gei Hasle, G. and Løkketangen, A., 2010. Industrial aspects and literature survey: Combined inventory management and routing. Computers and Operations Research [online], 37(9), pp. 1515‒1536. Available at: <http://www.sciencedirect.com/science/article/pii/S0305054809002962> [Accessed 21 April 2017].

2. Kot, S., Grondys, K. and Szopa, R., 2011. Theory of inventory management based on demand forecasting. Polish journal of management studies [pdf], 3, pp. 147‒155. Available at: <http://oaji.net/articles/2014/1384- 1415172623.pdf> [Accessed 11 July 2017].

3. Pistunov, I.M. and Bielkina, I.A., 2010. Forecast of seamles pipes export in 2010. Economika: problem teorii i praktyky [pdf], 261(2), pp. 414‒418. Available at: <http://pistunovi.inf.ua/Bel.pdf [Accessed 7 May 2017].

4. Dekker, R., Fleischmann, M., Inderfurth, K. and van Wassenhove, L.N., eds, 2013. Reverse logistics: quantitative models for closed-loop supply chains, Springer Science & Business Media.

5. Kim, M. and Chai, S., 2017. Implementing Environmental Practices for Accomplishing Sustainable Green Supply Chain Management. Sustainability, 9(7), p. 1192.

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