USING ECONOMETRIC MODELS FOR MANAGING DISTRIBUTION PROCESSES

Authors

DOI:

https://doi.org/10.29038/2786-4618-2024-02-134-144

Keywords:

econometric models, distribution of goods, supply chain management, optimization of distribution processes

Abstract

Introduction. The constant increase in competition and rapid changes in market conditions compel various business entities to seek more efficient ways to manage the distribution of goods. In this context, econometric models become an indispensable tool for analysis and forecasting, enabling informed decision-making, optimization of logistics processes, and cost minimization.

Objective. The aim of this article is to substantiate effective econometric models for managing the distribution of goods to optimize supply processes, sales, and maximize profits for manufacturers, retailers, and other participants in distribution channels.

Methods. The theoretical and methodological basis of the research consists of works by domestic and foreign scientists on the implementation of econometric models for managing business processes, including distribution. The informational basis includes scientific, economic, and reference literature, works of leading domestic and foreign scientists, methodological materials, information portals, and periodicals of Ukraine. A set of methods and approaches were used: system analysis – for studying the role and significance of econometric models for goods distribution and identifying their features; methods of analysis and synthesis – for analyzing and systematizing econometric models and determining the advantages and limitations of their application for goods distribution.

Results. The study investigates the role and significance of econometric models for goods distribution. It examines how these models can become a key tool in solving optimization tasks, adapting to changing market conditions, and ensuring competitiveness in a world where distribution strategies turn into strategic advantages. The advantages and limitations of applying such models are identified: simple linear or nonlinear demand models; time series autoregressive model ARIMA; exponentially smoothed model ETS; transportation problem model; Vehicle Routing Problem (VRP); Traveling Salesman Problem (TSP); orienteering problem; conjoint analysis.

Conclusions. Based on a comparative analysis of the key aspects of using econometric models for goods distribution, their specific aspects depending on the implementation of particular tasks and needs of the business entity are detailed. The main advantages of applying econometric models in distribution are highlighted, primarily providing the ability to effectively consider a large number of factors affecting distribution processes, along with certain limitations of their use. Among the main benefits are more accurate demand forecasting, which allows for optimizing inventories and supply chain management costs, as well as strategies for warehouse placement, route selection for delivery, etc.

References

Balabanova L.V., Hermanchuk A.M. Lohistyka. Donetsk: DonNUET, 2012. 458 s. [in Ukrainian].

Bilovodska O.A. Kvalimetrychnyi pidkhid otsiniuvannia stratehichnoi diialnosti upravlinnia dystrybutsiieiu innovatsiinykh produktiv u marketynhovii lohistytsi. Ekonomichnyi chasopys Volynskoho natsionalnoho universytetu imeni Lesi Ukrainky. 2021. № 1 (25). S. 175-183. https://doi.org/10.29038/2786-4618-2021-01-175-183. [in Ukrainian].

Holovina O. Suchasni tekhnolohii v upravlinni transportnoiu lohistykoiu. International Science Journal of Management, Economics & Finance. 2023. Vol. 2 (3). S. 35-42. https://doi.org/10.46299/j.isjmef.20230203.04. [in Ukrainian].

Kozmenko O. V., Kuzmenko O. V. Ekonomiko-matematychni metody ta modeli (ekonometryka) : navch. posib. Sumy : Universytetska knyha. 2014. 406 s. [in Ukrainian].

Krykavskyi Ye. Lohistyka dlia ekonomistiv. Lviv. : Vyd-vo: Lvivska politekhnika. 2004. S. 265–303. [in Ukrainian].

Luhinin O. Ye. Ekonometriia: navch. posib. 2-e vydannia, pererob. ta dop. Kyiv : Tsentr uchbovoi literatury, 2008. 278 s. [in Ukrainian].

Malchyk M. V., Tolchanova Z. O. Lohistychna diialnist promyslovoho pidpryiemstva v yoho marketynhovii politytsi. Naukovi zapysky Natsionalnoho universytetu "Ostrozka akademiia". Ser. : Ekonomika. 2013. Vyp. 21. S. 68-70. [in Ukrainian].

Mishchuk I. P. Sutnist ta kharakterystyka systemy lohistyky pidpryiemstva. Torhivlia, komertsiia, pidpryiemnytstvo. 2015. Vyp. 19. S. 72-76. [in Ukrainian].

Nazarenko O. M. Osnovy ekonometryky: pidruchnyk. Vyd. 2-he, pererob. Kyiv : Tsentr navchalnoi literatury. 392 s[in Ukrainian].

Chebanova O.P., Volokhov V.A.. Vykorystannia tekhnolohii mashynnoho navchannia dlia optymizatsii lohistyky. Visnyk ekonomiky transportu i promyslovosti. 2023. № 83. S. 278-283. URL: http://btie.kart.edu.ua/issue/view/17822. [in Ukrainian].

Shcho take dystrybutsiia: vid zakupivel do pidtrymky firmovoho styliu. URL: https://mc.today/uk/shho-take-distributsiya/A revisited branch-and-cut algorithm for large-scale orienteering problems. URL: https://arxiv.org/abs/2011.02743. [in Ukrainian].

Andrews D. Tests for parameter stability and structural change with unknown change point. Econometrica. 1993. Vol. 59. P. 817–858. [In English].

Conjoint Analysis Helps Apple Win $1B in Lawsuit. URL: https://verstaresearch.com/blog/conjoint-analysis-helps-apple-win-1b-in-lawsuit/ [In English].

Deroussi L., Grangeon N., Norre S. Optimization of logistics systems using metaheuristic based hybridization techniques, Metaheuristics. 2016. URL: https://hal.science/hal-02023692/document. [In English].

How does conjoint analysis work? URL: https://conjointly.com/guides/what-is-conjoint-analysis/#howitworks. [In English].

Keuzenkamp H. A., Magnus J. R. On tests and significance in econometrics. Journal of Econometrics. 1995. Vol. 67. P. 5–24. [In English].

Kim S.T., Lee H.-H., Hwang T. Logistics integration in the supply chain: a resource dependence theory perspective. International Journal of Quality Innovation. 2020. Vol. 6 (5). https://doi.org/10.1186/s40887-020-00039-w.

Linear Programming. URL: https://byjus.com/maths/linear-programming/ (дата звернення: 29.11.2023). [In English].

MacKie-Mason J. K. Econometric software: A user’s view. Journal of Economic Perspectives. 1992. Vol. 6 (4). P. 165–187. [In English].

Mrithula V., Devi N., Haran H. Future Sales Forecasting Using ARIMA Model. International Journal of Research and Reviews. 2023. Vol 4, no. 5. P. 210–2015. URL: https://ijrpr.com/uploads/V4ISSUE5/IJRPR12640.pdf. [In English].

Nature of Econometrics. URL: https://theintactone.com/2023/08/15/meaning-nature-and-scope-of-econometrics-economic-and-econometric-models-methodology-of-econometrics/[In English].

Thirteen Most Common Econometrics Models. URL: https://learneconometricsfast.com/13-econometrics-models-for-researchers-and-students/[In English].

Transportation Problem and Assignment problem. URL: https://www.acsce.edu.in/acsce/wp-content/uploads/2020/03/1585041316993_Module-4.pdf. [In English].

Travelling salesman problem. URL: https://en.m.wikipedia.org/wiki/Travelling_salesman_problem. [In English].

Vehicle Routing Problem. URL: https://developers.google.com/optimization/routing/vrp. [In English].

Wang Y., Guo Z., Zhang Y., Hu X., Xiao J. Iron Ore Price Prediction Based on Multiple Linear Regression Model. Sustainability. 2023. Vol. 15, 15864. https://doi.org/10.3390/ su152215864. [In English].

Wilson, J. Artificial Intelligence In Logistics: How Ai Can Make Your Processes More Efficient Jennifer Wilson Jennifer. 2020. URL: https://www.sage.com/engb/blog/artificial-intelligence-in-logisticsefficient-processes/ [In English].

Published

2024-06-18

Issue

Section

Entrepreneurship trade and exchange activities

How to Cite

[1]
2024. USING ECONOMETRIC MODELS FOR MANAGING DISTRIBUTION PROCESSES. Economic journal of Lesya Ukrainka Volyn National University. 2, 38 (Jun. 2024), 134–144. DOI:https://doi.org/10.29038/2786-4618-2024-02-134-144.