Modelling inventory management: Separate issues for construction and application

Mykhailo Luchko, Iryna Lukanovska, Vadym Ratynsky
DOI: https://doi.org/10.4995/ijpme.2019.11435

Abstract

The article presents the model of inventory management for forestry enterprises. It allows us to make calculationsof the efficiency of the use of inventories, helps minimize the cost of transportation and storage and to avoid fines for lack of inventory in the presence of demand. The suggested model takes into account the dependence of the price of the received reserves, warehouse costs, the cost of holding stocks, the amount of storage costs and penalties for customer stocks that have been unloaded on time. The model will allow us to avoid delays in supply, determine the size and guarantee optimal inventory levels. It is proved that rational inventory management enables companies to calculate the optimal amount of reserveordering, and the time interval between such orders. Putting this model into practice will allow for managing of the logistical and warehouse costs, determining the risk of not receiving or reducing the company’s profits due to the excess inventory costs, and understanding of the efficiency of turnover of inventories. Purpose. The aim of the study is to construct an economic and mathematical model of inventory management of the enterprise, taking into account the dependence of the shipping costfrom the supplier to the warehouse, the cost of holding the stocks, the amount of storage costs and penalties for non-shipped products. Methodology. Approbation of theoretical developments was carried out in the application package Statistica and the use of the module MARSPline – an integral element of technology Data Mining Results. The theoretical contribution. The main dependencies influencing the formation of the value of stocks were discovered on the basis of this and a regression model ofthe optimal size of stocks was constructed with a fairly precise approximation; calculations were made of the efficiency of the use of inventories, minimizing the total costs associated with delivery, storage and fines for the absence of stocks at availability of demand for them. Practical implications. We have constructed, investigated and tested the economic and mathematical model of the optimal size of stocks, which takes into account the dependence of the shipping cost from the supplier to thewarehouse, the cost of holding the stocks, the amount of storage costs and penalties for non-shipped products.

Keywords

Modelling; Optimization; Spline surfaces; Data Mining Results; MARSPline; Statistica pack-age

Full Text:

PDF

References

A DAN DZO. (2015). Inventory Control. Available at: http://www.adandzo.com/consulting/82/ Accessed 22 August 2014.

Agarwal, A., Shankar, R., & Tiwari, M. K. (2006). Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach. European Journal of Operational Research, 173(1), 211-225. https://doi.org/10.1016/j.efor.2004.12.005

Agarwal, A., Shankar, R., & Tiwari, M. K. (2007). Modeling agility of supply chain. Industrial Marketing Manage-ment, 36(4), 443-457. https://doi.org/10.1016/j.indmarman.2005.12.004

Andrushkiv, B. M., Vovk, Y.Y., & Vovk, I. P. (2012). Resursonomika: Teoretychni ta prykladni aspekty [Resourcenomy: Theoretical and applied aspects]. Ternohraf, Ternopil, Ukraine.

Atamanchuk, Yu.S., & Pasenchenko, Yu.A. (2016). Modeling of enterprise's inventory management with allowance for uncertain demand. Actual Problems of Economics and Management, 10, 115-123.

Beale, E. M. (1955). On minimizing a convex function subject to linear inequalities. Journal of the Royal Statistical Society: Series B (Methodological), 17(2), 173-184. https://doi.org/10.1111/j.2517-6161.1955.tb00191.x

Beamon, B. M. (1998). Supply chain design and analysis:: Models and methods. International journal of production eco-nomics, 55(3), 281-294. https://doi.org/10.1016/S0925-5273(98)00079-6

Biswas, S., & Narahari, Y. (2004). Object oriented modeling and decision support for supply chains. European Journal of Operational Research, 153(3), 704-726. https://doi.org/10.1016/S0377-2217(02)00806-8

Bukan, D., & Kenіgsberg, E. (2007). Nauchnoe upravlenie zapasami [Scientific inventory management]. Moscow: Nauka, Russia.

Ermoliev, Y. M., & Norkin, V. I. (1997). On nonsmooth and discontinuous problems of stochastic systems optimiza-tion. European Journal of Operational Research, 101(2), 230-244. https://doi.org/10.1016/S0377-2217(96)00395-5

Franca, R. B., Jones, E. C., Richards, C. N., & Carlson, J. P. (2010). Multi-objective stochastic supply chain modeling to evaluate tradeoffs between profit and quality. International Journal of Production Economics, 127(2), 292-299. https://doi.org/10.1016/j.ijpe.2009.09.005

Isonin, I.V., & Lagotsky, T. Ya. (2012). The problem of stochastic modeling in the management of production reserves. Scientific Herald of NLTU of Ukraine, 22(5), 351-356.

Kaplan, R. S., & Norton, D. P. (2006). Alignment: Using the balanced scorecard to create corporate synergies. Harvard Business Press.

Kushnirenko O. S., & Ralle, N. V. (2015). Simulation of inventory management. Visnyk KNUTD, 2(85), 69-73.

Luchko, M.R. (2016). Contract theory: selected issues of accounting and analysis. Socio-Economic Problems and the State, 2(15), 71-77.

Luchko, M.R. (2018). Analytical procedures in the process of inventory management audit. In Proceedings III International conf. Accounting, Taxation and Control: Theory and Methodology [Ternopil, June 26, 2018]. Ternopil: TNEU, Ukraine, 95-97.

Melo, M. T., Nickel, S., & Da Gama, F. S. (2006). Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Computers & Operations Research, 33(1), 181-208. https://doi.org/10.1016/j.cor.2004.07.005

Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & Industrial Engineering, 43(1-2), 231-249. https://doi.org/10.1016/S0360-8352(02)00066-9

Mula, J., Poler, R., García-Sabater, J. P., & Lario, F. C. (2006). Models for production planning under uncertainty: A review. International journal of production economics, 103(1), 271-285. https://doi.org/10.1016/j.ijpe.2005.09.001

Pasenchenko, Yu.A, & Trubnikova, O.I. (2011). Modeling of commercial enterprise reserves management in the system of balanced indicators. Investments: Practice and Experience, 15, 38-41.

Perea, E., Grossmann, I., Ydstie, E., & Tahmassebi, T. (2000). Dynamic modeling and classical control theory for supply chain management. Computers & Chemical Engineering, 24(2-7), 1143-1149. https://doi.org/10.1016/S0098-1354(00)00495-6

Popovic, T., Kraslawski, A., Barbosa-Póvoa, A., Carvalho, A. (2017). Quantitative Indicators for Social Sustainability Assessment of Society and Product Responsibility Aspects in Supply Chains. Journal of International Studies, 10(4), 9-36. https://doi.org/10.14254/2071-8330.2017/10-4/1

Robert, S. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71-79.

Seuring, S. (2013). A review of modeling approaches for sustainable supply chain management. Decision support sys-tems, 54(4), 1513-1520. https://doi.org/10.1016/j.dss.2012.05.053

Shraybfeder, D. (2005). Efficient inventory control. Alpina Business Books, Moscow, Russia.

Shygun M. M. (2008). Economic modeling in dissertation research: Bibliometric analysis. Visnyk ZhDTU, 1(43), 115-127.

Swaminathan, J. M., Smith, S. F., & Sadeh, N. M. (1998). Modeling supply chain dynamics: A multiagent ap-proach. Decision Sciences, 29(3), 607-632. https://doi.org/10.1111/j.1540-5915.1998.tb01356.x

Takha, K.A. (2005). Introduction to Operations Research. Williams, Moscow, Russia.

Abstract Views

743
Metrics Loading ...

Metrics powered by PLOS ALM


Bookmark and Share


This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Universitat Politècnica de València

e-ISSN: 2340-4876     ISSN: 2340-5317   https://doi.org/10.4995/ijpme