IDEAS home Printed from https://ideas.repec.org/a/cuc/eforum/v12y2022i3p62-71.html

Assesment of the efficiency of risk management of the logistics system of the enterprise using the method of discriminant anakysis

Author

Listed:
  • Igor Kryvovyazyuk
  • Yulia Kulyk

Abstract

This article solves the problem of finding an adequate technology of mathematical statistics that will increase the efficiency of risk management in the supply chain. The main goal of research is to improve the model for assessing efficiency of risk management of the logistics system, which will ensure a high level of accuracy in the classification of the population of the researched enterprises according to the levels of risk management efficiency. A critical analysis of scientific sources on solving researched problem indicates the wide use of methods for evaluating trade-offs between logistics risk and efficiency of logistics system and the feasibility of using discriminant analysis as the most acceptable of them. The relevance of solving this scientific problem lies in the fact that the timely improvement of the risk management of logistics system of enterprises by evaluating the efficiency of its implementation based on previously justified methods of analysis and modeling of risk management processes provides comprehensive countermeasures for various risks of internal and external environment of influence, preventing disruption of integration of logistics links and occurrence of material losses. The theoretical-methodical and practical basis of the research was made by the following methods: abstract-logical and generalization – while reserching scientific provisions of the theory of risk management for logistics systems of modern enterprises and methods of analysis and assessment of the efficiency of risk management; abstraction and formalization – while revealing the methodology of implementing discriminant analysis for evaluating the efficiency of risk management of logistics system; mathematical and statistical – while calculating and building a discriminatory model for assessing the efficiency of risk management of the logistics system of engeneering enterprises; generalization – while summarizing conclusions and recommendations based on research results. The object of research is risk management of logistics system of engineering enterprises. Research results established that the speed of rotation in the supply chain has the greatest influence on efficiency of risk management of logistics systems of engineering enterprises and the speed of turnover and the degree of customer service are less important. The carried out grouping of enterprises according to the level of efficiency of risk management of the logistics system determined five classification groups regarding their distribution: 40.74% of the analyzed enterprises are characterized by high, 15.93% – medium, 20.11% – sufficient, 17.14% – low and 6.08% is characterized by the critical level of risk management efficiency of the logistics system. A set of software products is recommended for each of the groups, which will ensure optimization and improvement of logistics processes, which has practical value.

Suggested Citation

  • Igor Kryvovyazyuk & Yulia Kulyk, 2022. "Assesment of the efficiency of risk management of the logistics system of the enterprise using the method of discriminant anakysis," E-Forum Working Papers, Economic Forum, vol. 12(3), pages 62-71, August.
  • Handle: RePEc:cuc:eforum:v:12:y:2022:i:3:p:62-71
    DOI: https://doi.org/10.36910/6775-2308-8559-2022-3-8
    as

    Download full text from publisher

    File URL: https://e-forum.com.ua/web/uploads/pdf/Economic_Forum_Vol_12_No_3-62-71.pdf
    Download Restriction: no

    File URL: https://libkey.io/https://doi.org/10.36910/6775-2308-8559-2022-3-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    2. Sebastian Tillmanns & Manfred Krafft, 2022. "Logistic Regression and Discriminant Analysis," Springer Books, in: Christian Homburg & Martin Klarmann & Arnd Vomberg (ed.), Handbook of Market Research, pages 329-367, Springer.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Samadhiya, Ashutosh & Yadav, Sanjeev & Kumar, Anil & Majumdar, Abhijit & Luthra, Sunil & Garza-Reyes, Jose Arturo & Upadhyay, Arvind, 2023. "The influence of artificial intelligence techniques on disruption management: Does supply chain dynamism matter?," Technology in Society, Elsevier, vol. 75(C).
    2. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    3. Li, Jian & Yi, Mei & Sun, Qing, 2025. "Artificial intelligence and supply chain risk: Mediating effects of supply chain efficiency and resilience," International Review of Financial Analysis, Elsevier, vol. 108(PA).
    4. Mohammadreza Akbari & John L. Hopkins, 2022. "Digital technologies as enablers of supply chain sustainability in an emerging economy," Operations Management Research, Springer, vol. 15(3), pages 689-710, December.
    5. Hind Aboussikine & Sonia Bendimerad & Thierry Sauvage & Mohamed Haouari, 2023. "Comment l’Intelligence Artificielle dompte la traçabilité des processus Supply Chain ? Application à NOZ France," Post-Print hal-04536092, HAL.
    6. Wang, Weizhong & Chen, Yu & Zhang, Tinglong & Deveci, Muhammet & Kadry, Seifedine, 2024. "The use of AI to uncover the supply chain dynamics of the primary sector: Building resilience in the food supply chain," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 544-566.
    7. Wang, Liangcheng & Chen, Yizheng, 2025. "Artificial intelligence and corporate investment efficiency: Evidence from China," Emerging Markets Review, Elsevier, vol. 68(C).
    8. Han, Peng & Huo, Yanfang & Liu, Weihua & Qi, Ershi & Cai, Helen, 2026. "The development strategy of supply chain intelligent technology considering technology development uncertainty," European Journal of Operational Research, Elsevier, vol. 328(2), pages 496-510.
    9. Anas Iftikhar & Imran Ali & Ahmad Arslan & Shlomo Tarba, 2024. "Digital Innovation, Data Analytics, and Supply Chain Resiliency: A Bibliometric-based Systematic Literature Review," Annals of Operations Research, Springer, vol. 333(2), pages 825-848, February.
    10. Gupta, Shivam & Modgil, Sachin & Choi, Tsan-Ming & Kumar, Ajay & Antony, Jiju, 2023. "Influences of artificial intelligence and blockchain technology on financial resilience of supply chains," International Journal of Production Economics, Elsevier, vol. 261(C).
    11. Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
    12. Hamed Jahani & Richa Jain & Dmitry Ivanov, 2026. "Data science and big data analytics: a systematic review of methodologies used in the supply chain and logistics research," Annals of Operations Research, Springer, vol. 359(2), pages 1297-1354, April.
    13. Tan-I Chen & Hung-Chang Chung & Shih-Kai Lin, 2023. "The Effect of Applying Language Picture Books in Reciprocal Teaching on Students’ Language Learning Motivations," SAGE Open, , vol. 13(4), pages 21582440231, December.
    14. Šimon Hána & Bart Lameijer, 2026. "AI-based systems adoption in business operations: barriers and performance effects," Operations Management Research, Springer, vol. 19(1), pages 1-18, March.
    15. Muhammad Khan & Gohar Saleem Parvaiz & Abbas Ali & Majid Jehangir & Noor Hassan & Junghan Bae, 2022. "A Model for Understanding the Mediating Association of Transparency between Emerging Technologies and Humanitarian Logistics Sustainability," Sustainability, MDPI, vol. 14(11), pages 1-23, June.
    16. Wang, Chang-song & Chen, Wei & Zheng, Yang & Dai, Qin, 2025. "Bridge the gap: nexus between artificial intelligence and urban energy resilience, evidence from low-carbon city in China," Energy Economics, Elsevier, vol. 152(C).
    17. Dimitris Zissis, 2023. "Information sharing through digitalisation in decentralised supply chains," Annals of Operations Research, Springer, vol. 327(2), pages 763-778, August.
    18. Ioannis Adamopoulos & Lester Allan Lasrado & Raghava Rao Mukkamala, 2026. "A Systematic Literature Review of Machine Learning and Artificial Intelligence Applications for Sustainable Logistics: Current Trends and Future Directions," Circular Economy and Sustainability, Springer, vol. 6(2), pages 1-48, April.
    19. Lo, Shirleen Lee Yuen & How, Bing Shen & Leong, Wei Dong & Teng, Sin Yong & Rhamdhani, Muhammad Akbar & Sunarso, Jaka, 2021. "Techno-economic analysis for biomass supply chain: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    20. Mengmeng Wang & Xiaoming Pan, 2022. "Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance: An Empirical Analysis on an Emerging Market," Sustainability, MDPI, vol. 14(24), pages 1-16, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cuc:eforum:v:12:y:2022:i:3:p:62-71. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Economic Forum (email available below). General contact details of provider: https://e-forum.com.ua/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.