Systematic aspects of strategic management of logistics provider organizations in a complex dynamic environment

published:
Number: Issue 31(2025)
Section: Economy. Management
The page spacing of the article: 22-32
Keywords: organization of logistics providers, strategic management, balanced scorecard, key performance indicator, dynamic environment.
How to quote an article: Tetyana Vorkut, Ludmila Volynets. Systematic aspects of strategic management of logistics provider organizations in a complex dynamic environment. Dorogi і mosti [Roads and bridges]. Kyiv, 2025. Issue 31. P. 22–32 [in Ukrainian].

Authors

National Transport University (NTU), Kyiv, Ukraine
https://orcid.org/0000-0002-5064-2349
National Transport University (NTU), Kyiv, Ukraine
https://orcid.org/0000-0003-0354-476X

Summary

Introduction. Today, the conditions of interaction between the stages of formation (formulation) and implementation of organizational strategy are considered in many scientific works in the field of management as a scientific discipline. At the same time, these works are of a general methodological nature, from the point of view of strategic management, such as, for example, the work [1], and focus on individual aspects of functional, in particular, logistical, strategic management, for example, work [2].

In the context of the above, we can recall the work [1], in which, within the framework of the systematization of scientific schools of strategic management, prescriptive and descriptive scientific schools are distinguished. One of the distinguishing features of prescriptive schools – the design school, the planning school, and the positioning school – is that their proponents believe that effective and efficient organizational strategy involves the separation (separation) of thinking and action. That is, the processes of formulating, in the terminology of prescriptive schools, and implementing organizational strategy. Regarding descriptive schools – the school of entrepreneurship, the cognitive school, the school of learning, the school of power, the school of the environment, the school of culture, we should note that their supporters hold a different opinion. They believe that an effective and efficient organizational strategy involves the combination of thinking and action. That is, the processes of formation, in the terminology of descriptive schools, and the implementation of organizational strategy.

Problem Statement. The scientific gap is identified in the lack of comprehensive, based on methodological approaches of strategic management and information technologies, methods and models of strategic management of a logistics provider organization, which, (the organization) operating in a complex dynamic environment, implements the approaches of descriptive scientific schools of strategic management.

Objective. The purpose of the study is to formulate the conceptual principles of strategic management, primarily in terms of monitoring and control, by a logistics provider organization that, operating in a complex dynamic environment, uses the approaches of descriptive scientific schools of strategic management.

Materials and Methods. When analyzing scientific works that consider the issues of forming a concept of strategic management of a logistics provider organization operating in a complex dynamic environment, the method of scientific identification was used; When developing a comprehensive model that, using the theory of strategic management and the theory of artificial intelligence, provides a mathematical description of the process of assessing the state of functioning of the logistics provider organization, which is considered as a sub-process within the monitoring and control process of the corresponding stage of strategic management, the approach of the theory of artificial intelligence was used, namely, a separate component of this theory, which is represented by fuzzy cognitive models.

Results. Conceptual principles of strategic management have been developed, primarily in terms of monitoring and control, by a logistics provider organization that, operating in a complex dynamic environment, uses the approaches of descriptive scientific schools of strategic management.

Conclusions. A concept of strategic management of a logistics provider organization has been formed, which, operating in a complex dynamic environment, uses the approaches of descriptive scientific schools of strategic management.

A comprehensive model is proposed, which, using the theory of strategic management and the theory of artificial intelligence, provides a mathematical description of the process of assessing the state of functioning of the logistics provider organization, which is considered as a sub-process within the process of monitoring and controlling the organizational strategy of the corresponding stage of strategic management. This model will increase the effectiveness and efficiency of strategic management in the organization of a logistics provider by increasing the efficiency of data acquisition for strategic decision-making in the context of overlapping stages of formation and implementation of organizational strategies, which distinguishes descriptive scientific schools of strategic management.

References

  1. Mintzberg, H., Lampel, J., Ahlstrand, B. (2001). Strategy Safari: A Guided Tour Through The Wilds of Strategic Mangament. Toronto: The Free Press, 416 [in English].
  2. Vorkut T. А., Lushchai Yu. V., Kharuta V. S., Cheshet A. M. (2021). Strategic management of logistics outsourcing projects. Monograph. K.: Millennium. 156 p. [in Ukrainian].
  3. Niven, P. R. (2006). Balanced scorecard step‐by‐step: Maximizing performance and maintaining results (2nd ed.). Wiley. 336 р. [in English].
  4. Kaplan, R. S., & Norton, D. P. (2000). The strategy-focused organization: How balanced scorecard companies thrive in the new business environment. Harvard Business School Press. 504 р. [in English].
  5. Kaplan, R. S., & Norton, D. P. (2006). Alignment: Using the balanced scorecard to create corporate synergies. Harvard Business School Press. 320 р. [in English].
  6. Vorkut, T. A., Bilonog, O. Ye., Dmytrichenko, A. M., Tretynychenko, Y. O. (2017). Supply Chain Management: Logistic Aspect. Kyiv: NTU. 288 р. [in Ukrainian].
  7. Vorkut T. A., Bilonoh O. Ie., Dmytrychenko A. M., Petunin A. V., Sribna N. V., Tretynychenko Yu.O. (2021) Portfelno-oriientovane upravlinnia v orhanizatsiinykh merezhakh. Monohrafiia. Kyiv: Milenium, – 227 р. [in Ukrainian].
  8. Ko Y.-C., Fujitа H. (2019). An Evidential Analytics for Buried Information in Big Data Samples: Case Study of Semiconductor Manufacturing. Information Sciences. Vol. 486. P. 190–203. DOI: https://doi.org/10.1016/j.ins.2019.01.079 [in English].
  9. Pérez-González C. J., Colebrook M., Roda-García J. L., Rosa-Remedios C. B. Developing a Data Analytics Platform to Support Decision Making in Emergency and Security Management. Expert Systems with Applications. 2019. Vol. 120. P. 167–184. DOI: https://doi.org/10.1016/j.eswa.2018.11.023 [in English].
  10. Chen H. (2018). Evaluation of Personalized Service Level for Library Information Management Based on Fuzzy Analytic Hierarchy Process. Procedia Computer Science, Vol. 131, P. 952–958. DOI: https://doi.org/10.1016/  j.procs.2018.04.233 [in English].
  11. Chan H. K., Sun X., Chung S.-H. (2019).When Should Fuzzy Analytic Hierarchy Process be Used Instead of Analytic Hierarchy Process? Decision Support Systems. Vol. 125, P. 113–114. DOI: https://doi.org/10.1016/j.dss.2019.113114 [in English].
  12. Osman A. M. S.  (2019). Novel Big Data Analytics Framework for Smart Cities. Future Generation Computer Systems. Vol. 91. P. 620–633. DOI: https://doi.org/ 10.1016/j.future.2018.06.046 [in English].
  13. Gödri I., Kardos C., Pfeiffer A., Váncza J. (2019). Data Analytics-Based Decision Support Workflow for High-Mix Low-Volume Production Systems. CIRP Annals, Vol. 68 (1), P. 471–474. DOI: https://doi.org/10.1016/j.cirp.2019.04.001 [in English].
  14. Harding J. L. (2013). Data Quality in the Integration and Analysis of Data from Multiple Sources: Some Research Challenges. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XL-2/W1, P. 59–63. DOI: https://doi.org/10.5194/isprsarchives-xl-2-w1-59-2013 [in English].
  15. Maccarone A. D., Brzorad J. N., and Stone H. M. (2008). Characteristics And Energetics Of Great Egret And Snowy Egret Foraging Flights. Waterbirds. Vol. 31, No. 4, P. 541–549. DOI: https://doi.org/ 10.1675/1524-4695-31.4.541 [in English].
  16. Tatiana Vorkut, Lyudmila Volynets. (2024). DEVISING A METHOD FOR ASSESSING THE EFFICIENCY IN MANAGING LOGISTICS OPERATIONS OF MOTOR TRANSPORT ENTERPRISES. Eastern-European Journal of Enterprise Technologies. Vol. 6 (3 (132), P. 17–24. DOI: https://doi.org/10.15587/17294061.2024.317567 [in English].