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. 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.
    2. 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.
    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. Emilia Vann Yaroson & Soumyadeb Chowdhury & Sachin Kumar Mangla & Prasanta Kumar Dey, 2024. "Unearthing the interplay between organisational resources, knowledge and industry 4.0 analytical decision support tools to achieve sustainability and supply chain wellbeing," Annals of Operations Research, Springer, vol. 342(2), pages 1321-1368, November.
    2. Wang, Jiaxin & Zhao, Mu & Huang, Xiang & Song, Zilong & Sun, Di, 2024. "Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing," International Review of Financial Analysis, Elsevier, vol. 93(C).
    3. Lu Han & Hanping Hou & Z. M. Bi & Jianliang Yang & Xiaoxiao Zheng, 2024. "Functional Requirements and Supply Chain Digitalization in Industry 4.0," Information Systems Frontiers, Springer, vol. 26(6), pages 2273-2285, December.
    4. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    5. Blessing Takawira & David Pooe, 2024. "Challenges and opportunities for pharmaceutical SMEs from South Africa in embedding into global supply chains a systematic literature review," International Journal of Business Ecosystem & Strategy (2687-2293), Bussecon International Academy, vol. 6(3), pages 01-22, July.
    6. 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).
    7. 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).
    8. Wang, Shaofeng & Zhang, Hao, 2025. "Enhancing environmental, social, and governance performance through artificial intelligence supply chains in the energy industry: Roles of innovation, collaboration, and proactive sustainability strategy," Renewable Energy, Elsevier, vol. 245(C).
    9. Hajar Fatorachian, 2024. "Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review," GATR Journals gjbssr655, Global Academy of Training and Research (GATR) Enterprise.
    10. Amine Belhadi & Venkatesh Mani & Sachin S. Kamble & Syed Abdul Rehman Khan & Surabhi Verma, 2024. "Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation," Annals of Operations Research, Springer, vol. 333(2), pages 627-652, February.
    11. 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).
    12. 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.
    13. 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.
    14. 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.
    15. Wang, Liangcheng & Chen, Yizheng, 2025. "Artificial intelligence and corporate investment efficiency: Evidence from China," Emerging Markets Review, Elsevier, vol. 68(C).
    16. Manu Sharma & Deepak Kaushal & Sudhanshu Joshi, 2023. "Strategic measures for enhancing resiliency in knowledge base supply chains: an emerging economy perspective," Operations Management Research, Springer, vol. 16(3), pages 1185-1205, September.
    17. 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.
    18. 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.
    19. 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).
    20. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.

    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.