BUSINESS INFORM JOURNAL.

УКР

ENG

Search:

REGISTRATION CERTIFICATE

KV #19905-9705 PR dated 02.04.2013.

FOUNDERS

RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE)

According to the decision No. 802 of the National Council of Television and Radio Broadcasting of Ukraine dated 14.03.2024, is registered as a subject in the field of print media.
ID R30-03156

PUBLISHER

Liburkina L. M.

SITE SECTIONS

Analyzing the Relationship Between Country Risk Factors and Logistics Efficiency Using Bayesian Networks
Dmytryshyn L. I., Petryshak P. V.

Dmytryshyn, Lesia I., and Petryshak, Pavlo V. (2025) “Analyzing the Relationship Between Country Risk Factors and Logistics Efficiency Using Bayesian Networks.” Business Inform 1:160–171.
https://doi.org/10.32983/2222-4459-2025-1-160-171

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
Downloads/views: 0

Download article (pdf) -

UDC 332.1+656

Abstract:
The aim of the article is to quantitatively investigate the relationships between country risk factors and the Logistics Performance Index (LPI) indicators. The article analyzes studies that examine the connection between logistics efficiency and socioeconomic factors at the country level, including country risks. It is defined that it remains unexplored how different country risk factors may affect various logistics performance indicators. The LPI assessment is presented as a weighted average based on six indicators: customs, infrastructure, international shipments, logistics competence and quality, timeliness, and tracking. The following factors have been chosen to represent country risk: investment, economic, financial, political, environmental, and corruption risks. The conducted correlation analysis has made it possible to determine that all country risk factors may not be equally important concerning different LPI indicators. To determine the relative importance of country risk factors concerning various LPI indicators, it is proposed to use Bayesian Belief Networks (BBN). This choice of method is due to the fact that other methods are unable to effectively model and analyze various «what-if» scenarios within a probabilistic network. BBN surpasses them due to its ability to analyze direct and reverse conditional relationships. The structure of the Bayesian network has been defined based on the built-in algorithms of the Hugin software environment. The structure learning has been carried out using various algorithms: PC, NPC, Greedy search-and-score, Chow-Liu tree, Naive Bayes, and others. It is found that this dataset describes the Bayes algorithm with an augmented tree (TAN) with the highest accuracy. According to this algorithm, the probability distributions of individual risks affecting LPI and critically important LPI indicators have been determined. The constructed Bayesian network was analyzed for low and high performance of individual LPI indicators to gain insights into the risk profile of countries. In particular, the reverse propagation of the impact of individual risks on LPI indicators in the case of risk reduction was examined, as well as the reverse propagation of the influence of LPI and logistical competence and quality on specific types of country risks. The results of this study may be useful in allocating limited resources to critical risk factors based on the specific risk profile of the country.

Keywords: logistics, logistics network, country risk, risk factors, Bayesian network, Hugin software environment.

Fig.: 8. Tabl.: 3. Bibl.: 14.

Dmytryshyn Lesia I. – Doctor of Sciences (Economics), Professor, Head of the Department, Department of Economic Cybernetics, Vasyl Stefanyk Precarpathian National University (57 Shevchenka Str., Ivano-Frankіvsk, 76018, Ukraine)
Email: [email protected]
Petryshak Pavlo V. – Postgraduate Student, Department of Economic Cybernetics, Vasyl Stefanyk Precarpathian National University (57 Shevchenka Str., Ivano-Frankіvsk, 76018, Ukraine)
Email: [email protected]

List of references in article

Dmytryshyn, L. I., and Petryshak, P. V. “Doslidzhennia vzaiemozviazku mizh faktoramy ryzyku krainy ta efektyvnistiu lohistychnykh merezh“ [Research on the Relationship Between Country Risk Factors and the Efficiency of Logistics Networks]. Current scientific goals, approaches and challenges. Riga, Latvian Republic: SCIENTIA, 2025. 37-40.
Volosnikova, N. M. “Upravlinnia efektyvnistiu funktsionuvannia lohistychnoi systemy pidpryiemstva na mikro- ta makroekonomichnomu rivni“ [Managing the Efficiency of the Enterprise's Logistics System at the Micro- and Macroeconomic Levels]. Doslidzhennia ta optymizatsiia ekonomichnykh protsesiv «Optymum-2020». https://repository.kpi.kharkov.ua/server/api/core/bitstreams/977f4c89-9529-4c7d-87e3-435855e3c8c2/content
Melnykova, K. V. “Efektyvnist diialnosti lohistychnykh system“ [Efficiency of Logistic Systems]. Biznes Inform, no. 12 (2021): 283-287. DOI: https://doi.org/10.32983/2222-4459-2021-12-283-287
Zubrov, S. M., and Molchanov, O. V. “Efektyvnyi lohistychnyi menedzhment v umovakh hlobalnykh ryzykiv ta transformatsii dlia Ukrainy“ [Effective Logistics Management in the Context of Global Risks and Transformations for Ukraine]. Ekonomika: realii chasu, no. 3 (2024): 104-112. DOI: 10.15276/ETR.03.2024.10
Pavlova, H. Ye., Babii, I. V., and Volovyk, D. V. “Stanovlennia lohistyky na rivni mizhnarodnykh ekonomichnykh vidnosyn“ [Establishment of Logistics at the Level of International Economic Relations]. Innovation and Sustainability, no. 2 (2022): 139-146. DOI: https://doi.org/10.31649/ins.2022.2.139.146
Qazi, A., Simsekler, M. C. E., and Formaneck, S. “Impact assessment of country risk on logistics performance using a Bayesian belief network model“. Kybernetes, vol. 52, no. 5 (2022): 1620-1642. DOI: https://doi.org/10.1108/K-08-2021-0773
Gocer, A., Ozpeynirci, O., and Semiz, M. “Logistics performance index-driven policy development: An application to Turkey“. Transport Policy, vol. 124 (2022): 20-32. DOI: https://doi.org/10.1016/j.tranpol.2021.03.007
“Logistics Performance Index (LPI)“. https://lpi.worldbank.org/
“Country Risk Information“. AM Best. https://www3.ambest.com/ratings/cr/crisk.aspx
“A Global View: Interactive ESG Index Risk Map“. Global Risk Profile Corruption. https://risk-indexes.com/esg-index/
“Interactive Global Corruption Index Risk Map“. Risk Watch Initiative. https://risk-indexes.com/global-corruption-index/
Spirtes, P., Glymour, C., and Scheines, R. Causation, Prediction, and Search. MIT Press. Adaptive Computation and Machine Learning, 2000.
“NPC Algorithm“. HUGIN GUI. https://download.hugin.com/webdocs/manuals/9.2/htmlhelp/pages/Manual/Algorithms/NPC_Algorithm.html
Chow, K., and Liu, C. N. “Approximating Discrete Probability Distributions with Dependence Trees“. IEEE Transsctions on Information Theory, vol. 14, no. 3 (1968): 462-467. DOI: 10.1109/TIT.1968.1054142

FOR AUTHORS

License Contract

Conditions of Publication

Article Requirements

Regulations on Peer-Reviewing

Publication Contract

Current Issue

Frequently asked questions

INFORMATION

The Plan of Scientific Conferences

OUR PARTNERS

Journal «The Problems of Economy»

The site and its metadata are licensed under CC BY-SA.

Write to webmaster