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ORIGINAL RESEARCH ARTICLE
Management of logistics processes in road freight transport under conditions of sectoral digitalisation
Zhanna Yelesheva 1, A-B,D,F
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1
Department of Business Technologies, Al-Farabi Kazakh National University, Kazakhstan
 
2
Department of Transport Services and Business, Academy of Logistics and Transport, Kazakhstan
 
3
School of Economics and Management, Yanshan University, China
 
4
Department of Traffic Organization, Transport Management and Logistics, International Transport and Humanitarian University, Kazakhstan
 
 
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
 
 
Submission date: 2026-02-23
 
 
Final revision date: 2026-04-03
 
 
Acceptance date: 2026-06-24
 
 
Publication date: 2026-06-26
 
 
Corresponding author
Zhanna Yelesheva   

Department of Business Technologies, Al-Farabi Kazakh National University, 71 Al-Farabi Ave, 050040, Almaty, Kazakhstan
 
 
SLW 2026;64(1):125-140
 
KEYWORDS
TOPICS
ABSTRACT
Modern road freight transport faces increasing demands for speed and economic efficiency, rendering traditional planning methods insufficient. Concurrently, growing transit flows in Kazakhstan necessitate an urgent review of infrastructure and logistics strategies. The research niche of this article is the integration of digital platforms into existing logistics systems, specifically focusing on the interaction between different participants under various management models in road freight transport. The primary aim of the study was to justify the effectiveness of digital tools for optimising the management of logistics processes. The central research question evaluated the effectiveness of centralised, decentralised, and hybrid logistics flow management models under the implementation of digital solutions. The methodology relied on transport flow modelling, big data analysis, and the simulation of logistics processes. Data spanning 2020-2025 from official transport portals and meteorological resources in Kazakhstan were utilised to recreate authentic logistics structures. The quantitative simulation results demonstrated that adopting a hybrid management model integrated with geographic information systems and IoT reduced average vehicle downtime by 18% and improved peak-hour delivery reliability to 94%. Furthermore, pilot scenarios using a decentralised strategy reduced peak-hour delivery times by 12% and unplanned stops by 17%. Concurrently, based on the synthesis of secondary literature, the adoption of digital solutions within such models can potentially reduce transportation costs by 15-25% and improve demand forecasting accuracy by 45%. In conclusion, within the modelled Kazakhstan-specific road, climatic, and infrastructure conditions, the results suggest that hybrid digital logistics management may improve selected operational indicators compared with centralised and decentralised models, providing actionable directions for logistics policy specifically for the analysed routes.
REFERENCES (52)
1.
Aittoniemi, E., 2022. Evidence on impacts of automated vehicles on traffic flow efficiency and emissions: Systematic review, IET Intelligent Transport Systems, 16 (10), 1306-1327.
 
2.
Albalawneh, D.A. and Afendee Mohamed, M., 2022. Evaluation of using genetic algorithm and arcgis for determining the optimal-time path in the optimization of vehicle routing applications, Mathematical Problems in Engineering, 2022 (1), 7769951.
 
3.
Association of National Freight Forwarders of Kazakhstan, 2021. Analytical review and conceptual proposals for the formation of a comprehensive plan for the development of the transport and logistics complex of Kazakhstan until 2030 [online]. Available from: https://kazlogistics.kz/upload... [Accessed: 12 August 2025].
 
4.
Babar, M. and Arif, F., 2019. Real-time data processing scheme using big data analytics in internet of things based smart transportation environment, Journal of Ambient Intelligence and Humanized Computing, 10, 4167-4177.
 
5.
Bansal, V.K., 2020. Use of GIS to consider spatial aspects in construction planning process, International Journal of Construction Management, 20 (3), 207-222.
 
6.
Bisenovna, K.A., Ashatuly, S.A., Beibutovna, L.Z., Yesilbayuly, K.S., Zagievna, A.A., Galymbekovna, M.Z. and Oralkhanuly, O.B., 2024. Improving the efficiency of food supplies for a trading company based on an artificial neural network, International Journal of Electrical and Computer Engineering, 14 (4), 4407–4417.
 
7.
Bulgakov, V., Pascuzzi, S., Ivanovs, S., Nadykto, V. and Nowak, J., 2020. Kinematic discrepancy between driving wheels evaluated for a modular traction device, Biosystems Engineering, 196, 88–96.
 
8.
Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan, 2023a. Industry statistics: Transport [online]. Available from: https://stat.gov.kz/ru/industr... [Accessed: 12 August 2025].
 
9.
Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan, 2023b. Transport spreadsheets [online]. Available from: https://stat.gov.kz/en/industr... [Accessed: 12 August 2025].
 
10.
Centobelli, P., Cerchione, R., Esposito, E. and Shashi., 2020. Evaluating environmental sustainability strategies in freight transport and logistics industry, Business Strategy and the Environment, 29 (3), 1563-1574.
 
11.
Chen, Y.T., Sun, E.W., Chang, M.F. and Lin, Y.B., 2021. Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for logistics 4.0, International Journal of Production Economics, 238, 108157.
 
12.
Cullen, D.A., Neyerlin, K.C., Ahluwalia, R.K., Mukundan, R., More, K.L., Borup, R.L. and Kusoglu, A., 2021. New roads and challenges for fuel cells in heavy-duty transportation, Nature Energy, 6 (5), 462-474.
 
13.
Dikshit, S., Atiq, A., Shahid, M., Dwivedi, V. and Thusu, A., 2023. The use of artificial intelligence to optimize the routing of vehicles and reduce traffic congestion in urban areas, EAI Endorsed Transactions on Energy Web, 10, 1-13.
 
14.
Dolzhenko, N., Assilbekova, I., Konakbay, Z., Garmash, O. and Muratbekova, G., 2025. Organization of transport services and transport process safety, Periodica Polytechnica Transportation Engineering, 53 (3), 277–291. DOI: https://doi.org/10.3311/PPtr.3....
 
15.
Dolzhenko, N., Mailyanova, E., Assilbekova, I. and Konakbay, Z., 2021. Analysis of meteorological conditions significant for small aviation and training flights at the airfield “Balkhash” for planning and flight safety purposes, News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences, 2 (446), 62–67.
 
16.
Droj, G., Droj, L. and Badea, A.C., 2021. GIS-based survey over the public transport strategy: An instrument for economic and sustainable urban traffic planning, ISPRS International Journal of Geo-Information, 11 (1), 16.
 
17.
Duggal, A.S., Singh, R., Gehlot, A., Gupta, L.R., Akram, S.V., Prakash, C., Singht, S. and Kumar, R., 2021. Infrastructure, mobility and safety 4.0: Modernization in road transportation, Technology in Society, 67, 101791.
 
18.
Ethereum Foundation, 2023. Ethereum development documentation [online]. Available from: https://ethereum.org/en/develo... [Accessed: 12 August 2025].
 
19.
Farahpoor, M., Esparza, O. and Soriano, M., 2023. Comprehensive IoT-driven fleet management system for industrial vehicles, IEEE Access, 11, 148044-148057.
 
20.
Gan, Q., 2022. A logistics distribution route optimization model based on hybrid intelligent algorithm and its application, Annals of Operations Research, 1-13.
 
21.
Gao, X., Ci, Y., Yuen, K.F., Wu, L. and Li, R., 2025. Hybrid traffic flow prediction model for emergency scenarios with scarce historical data, Engineering Applications of Artificial Intelligence, 145, 110219.
 
22.
Gorman, M.F., Clarke, J.P., de Koster, R., Hewitt, M., Roy, D. and Zhang, M., 2023. Emerging practices and research issues for big data analytics in freight transportation, Maritime Economics and Logistics, 25, 28-60.
 
23.
Government of the Republic of Kazakhstan, 2022. On approval of the Concept for the development of transport and logistics potential of the Republic of Kazakhstan until 2030 [online]. Available from: https://adilet.zan.kz/rus/docs... [Accessed: 12 August 2025].
 
24.
He, J., Zhang, Z., Tan, Z. and Zheng, S., 2024. Analyzing the transportation infrastructure-rural industry integration relationship in China, Chinese Journal of Population, Resources and Environment, 22 (2), 157-166.
 
25.
Herold, D.M., Ćwiklicki, M., Pilch, K. and Mikl, J., 2021. The emergence and adoption of digitalization in the logistics and supply chain industry: an institutional perspective, Journal of Enterprise Information Management, 34 (6), 1917-1938.
 
26.
Jakubik, P., Kerimkhulle, S. and Teleuova, S., 2017. How to anticipate recession via transport indices, Ekonomický časopis, 6 5(10), 972–990.
 
27.
Kairatkyzy, G., Karsybayev, Y.Y., Abzhapbarova, A.Z., Deryugin, O.V. and Bas, I.K., 2022. Improving the reliability of trucking in the conditions of a mining enterprise, Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 3, 125–130.
 
28.
Khayesi, M., 2020. Vulnerable road users or vulnerable transport planning? Frontiers in Sustainable Cities, 2, 25.
 
29.
Layaoen, H.D.Z., Abareshi, A., Abdulrahman, M.D.A. and Abbasi, B., 2023. Sustainability of transport and logistics companies: An empirical evidence from a developing country, International Journal of Operations and Production Management, 43 (7), 1040-1067.
 
30.
Li, W., Batty, M. and Goodchild, M.F., 2020. Real-time GIS for smart cities, International Journal of Geographical Information Science, 34 (2), 311-324.
 
31.
Lumenspei, 2023. How a logistics company digitally transformed their business – Case study [online]. Available from: https://lumenspei.com/digital-... [Accessed: 12 August 2025].
 
32.
Lyu, Z., Pons, D., Zhang, Y. and Ji, Z., 2021. Freight operations modelling for urban delivery and pickup with flexible routing: Cluster transport modelling incorporating discrete-event simulation and GIS, Infrastructures, 6 (12), 180.
 
33.
Millonig, A. and Haustein, S., 2020. Human factors of digitalized mobility forms and services, European Transport Research Review, 12, 46.
 
34.
Modernization of the country’s key road facilities is nearing completion [online], 2024. Available from: https://ortcom.kz/ru/novosti/1... [Accessed: 12 August 2025].
 
35.
Nikitas, A., Michalakopoulou, K., Njoya, E.T. and Karampatzakis, D., 2020. Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era, Sustainability, 12 (7), 2789.
 
36.
Novytska, M., 2022. Comparative analysis of definitions of the term “digital economy”, Economics and Management Organization, 2 (46) 183-189.
 
37.
Pavard, A., Dony, A. and Bordin, P., 2023. Road modelling for infrastructure management - The efficient use of geographic information systems, Journal of Information Technology in Construction, 28, 438-457.
 
38.
Raevneva, O., Aksyonova, I. and Brovko, O., 2021. Comparative rating analysis of the state and trends of digitalization of Ukrainian society and economy, Economic Problems, 4, 56-66. Available from: http://repository.hneu.edu.ua/... [Accessed: 12 August 2025].
 
39.
RSE “Kazhydromet”, 2023. Annual bulletin of monitoring the state and change of climate in Kazakhstan [online]. Available from: https://www.kazhydromet.kz/ru/... [Accessed: 12 August 2025].
 
40.
Smailov, N., Nussupov, Y., Taissariyeva, K., Kuttybayev, A., Baigulbayeva, M., Turumbetov, M., Hryhoriev, Y. and Lutsenko, S., 2025a. Identification of dangerous situations in the road infrastructure using unmanned aerial vehicles, Technology Audit and Production Reserves, 6 (2), 97–102.
 
41.
Smailov, N., Tsyporenko, V., Ualiyev, Z., Issova, A., Dosbayev, Z., Tashtay, Y., Zhekambayeva, M., Alimbekov, T., Kadyrova, R. and Sabibolda, A., 2025b.
 
42.
Improving accuracy of the spectral-correlation direction finding and delay estimation using machine learning, Eastern European Journal of Enterprise Technologies, 2 (5(134)), 15–24.
 
43.
Soori, M., Arezoo, B. and Dastres, R., 2023. Artificial intelligence, machine learning and deep learning in advanced robotics, a review, Cognitive Robotics, 3, 54-70.
 
44.
The Steppe, 2023. How the growth of the logistics industry allows Kazakhstan and Kazakhstanis to earn money [online]. Available from: https://the-steppe.com/razviti... [Accessed: 12 August 2025].
 
45.
Tkachenko, I. and Hlushchenko, Y., 2020. Comparative analysis of the current state of digitalization of the economy of Ukraine and the EU, Black Sea Economic Studies, 50, 106-111.
 
46.
Verma, S.K., Verma, R., Singh, B.K. and Sinha, R.S., 2024. Management of intelligent transportation systems and advanced technology. In: R.K. Upadhyay, S.K. Sharma, V. Kumar (eds.), Intelligent Transportation System and Advanced Technology, 159-175. Singapore: Springer.
 
47.
Voloshina, A., Panchenko, A., Panchenko, I., Titova, O. and Zasiadko, A., 2019. Improving the output characteristics of planetary hydraulic machines, IOP Conference Series: Materials Science and Engineering, 708 (1), 012038. DOI: https://doi.org/10.1088/1757-8....
 
48.
Wang, Y. and Sarkis, J., 2021. Emerging digitalisation technologies in freight transport and logistics: Current trends and future directions, Transportation Research Part E: Logistics and Transportation Review, 148, 102291.
 
49.
Wójcik, W., Kalizhanova, A., Kulyk, Y.A., Knysh, B.P., Kvyetnyy, R.N., Kulyk, A.I., Sichko, T.V., Dumenko, V.P., Bezstmertna, O.V., Adikhanova, S., Zhassandykyzy, M., Junisbekov, M., Smailov, N. and Yussupova, G., 2022. The method of time distribution for environment monitoring using unmanned aerial vehicles according to an inverse priority, Journal of Ecological Engineering, 23 (11), 179–187.
 
50.
Xia, W.H., Zhou, D., Xia, Q.Y. and Zhang, L.R., 2020. Design and implementation path of intelligent transportation information system based on artificial intelligence technology, The Journal of Engineering, 2020(13), 482-485.
 
51.
Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H. and Yin, B., 2021. Deep learning on traffic prediction: Methods, analysis, and future directions, IEEE Transactions on Intelligent Transportation Systems, 23 (6), 4927-4943.
 
52.
Yusupova, G., Dootaliev, A., Belek uulu, E. and Melis uulu, D., 2024. Infrastructure. of national automobile transport systems as a factor in the development of international supply chains. Bulletin of the Kyrgyz National Agrarian University, 22 (6), 285-291.
 
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