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http://elartu.tntu.edu.ua/handle/lib/54076| Titel: | Аналіз транспортних витрат для оптимізації ланцюга поставок |
| Övriga titlar: | Analysis of transportation costs for the supply chain optimisation |
| Författare: | Зьолковський, Ярослав Гуцайлюк, Володимир Антонов, Любомир Мартиняк, Ірина Олександрівна Ziółkowski, Jarosław Hutsaylyuk, Volodymyr Antonov, Lyubomir Martyniak, Iryna |
| Affiliation: | Військовий технологічний університет, Варшава, Польща Вільний університет Варни, Варна, Болгарія Тернопільський національний технічний університет імені Івана Пулюя, Тернопіль, Україна Military University of Technology, Warsaw, Poland Varna Free University, Varna, Bulgaria Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine |
| Bibliographic description (Ukraine): | Аналіз транспортних витрат для оптимізації ланцюга поставок / Зьолковський Ярослав, Гуцайлюк Володимир, Антонов Любомир, Мартиняк Ірина Олександрівна // ГЕВ. — Т. : ТНТУ, 2026. — Том 100. — № 3. — С. 14–28. — (Економіка та міжнародні економічні відносини). |
| Bibliographic reference (2015): | Аналіз транспортних витрат для оптимізації ланцюга поставок / Зьолковський Я. та ін. // ГЕВ, Тернопіль. 2026. Том 100. № 3. С. 14–28. |
| Bibliographic citation (APA): | Ziółkowski, J., Hutsaylyuk, V., Antonov, L., & Martyniak, I. (2026). Analiz transportnykh vytrat dlia optymizatsii lantsiuha postavok [Analysis of transportation costs for the supply chain optimisation]. Galician economic journal, 100(3), 14-28. TNTU. [in Ukrainian]. |
| Bibliographic citation (CHICAGO): | Ziółkowski J., Hutsaylyuk V., Antonov L., Martyniak I. (2026) Analiz transportnykh vytrat dlia optymizatsii lantsiuha postavok [Analysis of transportation costs for the supply chain optimisation]. Galician economic journal (Tern.), vol. 100, no 3, pp. 14-28 [in Ukrainian]. |
| Is part of: | Галицький економічний вісник, 3 (100), 2026 Galician economic journal, 3 (100), 2026 |
| Journal/Collection: | Галицький економічний вісник |
| Issue: | 3 |
| Volume: | 100 |
| Utgivningsdatum: | 26-maj-2026 |
| Submitted date: | 7-mar-2026 |
| Date of entry: | 8-sep-2026 |
| Utgivare: | ТНТУ TNTU |
| Place of the edition/event: | Тернопіль Ternopil |
| DOI: | https://doi.org/10.33108/galicianvisnyk_tntu2026.03.014 |
| UDC: | 656.13 658.7 338.47 |
| Nyckelord: | автомобільний транспорт ланцюг поставок аналіз витрат очікувана вартість точка беззбитковості road transport supply chain cost analysis expected value break-even point |
| Number of pages: | 15 |
| Page range: | 14-28 |
| Start page: | 14 |
| End page: | 28 |
| Sammanfattning: | Оптимізація ланцюга поставок є одним із визначальних факторів діяльності торговельних
фірм. Залежно від особливостей цільового ринку, фірми можуть оптимізувати власні витрати на основі
аналізу різних факторів, включаючи сезонність, маршрутизацію поставок, вибір виду транспортного засобу,
використання власного автопарку чи послуг логістичних посередників. Важливим фактором є вибір виду
транспортування продукції від постачальника до торговельних посередників. Вибір виду транспорту
залежить від кількох факторів, таких як тип вантажу, що перевозиться, його розмір, відстань
транспортування, час доставки та витрати. Економічні розрахунки завжди превалюють та повинні бути
ретельно оцінені, оскільки транспортні витрати складають основну та найбільшу частку витрат
дистриб’юторської компанії. Проаналізовано три варіанти доставки товарів автомобільним транспортом.
Перший варіант передбачає використання послуг транспортної компанії, яка бере на себе відповідальність за
своєчасну та безпечну доставку вантажу в межах ланцюга поставок. Другий варіант базується на угоді, за
якою лізингова компанія надає транспортний засіб в обмін на регулярні лізингові платежі від клієнта. Третій
варіант – це угода, що передбачає використання транспортного засобу протягом тривалішого (договірного)
періоду, але без необхідності його придбання, тобто довготривала оренда. Період дослідження становив один
календарний рік, протягом якого експлуатаційні витрати оцінювалися для кожного варіанту. Через
сезонність ці витрати для кожного варіанту детально представлені помісячно (Таблиця 2, Таблиця 3 та
Таблиця 4). Далі оцінено середню змінну вартість доставки одиниці товару для кожного варіанту, а також
постійні витрати, які, вочевидь, були опущені для варіанту аутсорсингу. Розраховано очікувані значення,
проведений аналіз точки беззбитковості та розроблено остаточні висновки. Використання ефективного,
чіткого і зрозумілого інструментарію математичного аналізу дозволить фірмам максимально оптимізувати
витрати, що стає особливо актуальним в період нестабільності світових цін на паливно-мастильні
матеріали. Також він є підгрунтям для прийняття стратегічних рішень, що забезпечить фірмам розвиток на принципах сталості. Supply chain optimisation is a key factor in the operations of trading companies. Depending on the characteristics of the target market, companies can optimise costs by analysing factors such as seasonality, supply routing, vehicle type, and whether to use their own fleet or logistics intermediaries. An important factor is the choice of transportation mode for products from the supplier to the trading intermediaries. The choice of transport mode depends on several factors, such as the type and size of cargo, the distance to be travelled, delivery time, and costs. Economic considerations are always important and should be carefully assessed, as transport costs constitute the primary and largest share of a distribution companyʼs expenses. This paper analyses three options for delivering goods by road. The first option involves using a transport company, which is responsible for the timely and safe delivery of cargo within the supply chain. The second option is based on an agreement in which the leasing company provides the vehicle in exchange for regular leasing payments from the customer. The third option is an agreement to use the vehicle for a longer (contractual) period, without the need to purchase it, known as long-term rental. The study period was one calendar year, for which operating costs were estimated for each option. Due to seasonality, these costs for each option are presented in detail monthly (Table 2, Table 3, and Table 4). Next, the average variable unit delivery cost for each option was estimated, along with the fixed costs, which were understandably omitted for the outsourcing option. Expected values were then calculated, a break-even analysis was performed, and conclusions were developed. The use of effective, clear, and understandable mathematical analysis tools allows companies to optimise costs as much as possible, which is especially relevant amid instability in global fuel and lubricant prices. It also serves as a basis for strategic decisions that ensure the sustainable development of companies. |
| URI: | http://elartu.tntu.edu.ua/handle/lib/54076 |
| ISSN: | 2409-8892 |
| Copyright owner: | © Ternopil Ivan Puluj National Technical University, 2025 |
| URL for reference material: | https://doi.org/10.1007/978-3-032-04774-8_102 https://doi.org/10.1016/j.cstp.2025.101600 https://doi.org/10.3390/en18092291 https://doi.org/10.1007/978-3-031-93327-1_29 https://doi.org/10.17531/ein/206048 https://doi.org/10.1007/978-3-031-89553-1_17 https://doi.org/10.17531/ein/204539 http://doi.org/10.17531/ein/210312 https://doi.org/10.3390/logistics8020046 https://doi.org/10.1016/j.eswa.2025.129402 https://doi.org/10.1016/j.ress.2025.111895 https://doi.org/10.1016/j.omega.2025.103461 https://doi.org/10.1016/j.tranpol.2025.103868 https://doi.org/10.3390/su17104707 https://doi.org/10.1016/j.multra.2025.100233 https://doi.org/10.1111/itor.70062 https://doi.org/10.17531/ein/176375 https://doi.org/10.17531/ein/192165 https://doi.org/10.1080/03088839.2024.2407381 https://doi.org/10.1016/j.cstp.2025.101664 https://doi.org/10.1016/j.jlp.2025.105864 https://doi.org/10.1016/j.cstp.2025.101660 https://doi.org/10.3390/en14082131 https://doi.org/10.22049/cco.2024.29435.1993 https://doi.org/10.1016/j.ejor.2025.08.037 https://doi.org/10.1016/j.eswa.2025.129143 https://doi.org/10.3390/en15145198 https://doi.org/10.20858/tp.2025.20.1.16 https://doi.org/10.1201/9781351174664-393 |
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| References (International): | 1. Alves R., Hora J., Galvão T. (2026). Optimizing Transit Connectivity: A Synchronization Model Applied to Porto Campanhã, w Lecture Notes in Mobility. Springer, pp. 708–714. DOI: https://doi.org/10.1007/978-3-032-04774-8_102 2. Anussornnitisarn P. et al. (2025) Urban freight Electrification: Total cost of ownership comparison of medium-duty BEVs and ICEVs in Thailand», Case Studies on Transport Policy, 22. DOI: https://doi.org/10.1016/j.cstp.2025.101600 3. Borucka A., Sobczuk S. (2025) Analysis of the Relationship Between Energy Consumption in Transport, Carbon Dioxide Emissions and State Revenues: The Case of Poland, Energies, 18 (9). DOI: https://doi.org/10.3390/en18092291 4. Chandiramani H., Soni G., Mittal M. L. (2026). Advances in Intelligent Urban Transportation: New Approaches to Routing, Scheduling, and Network Design, w Mech. Mach. Sci. Mechanisms and Machine Science, Springer Science and Business Media B. V., рр. 503–515. DOI: https://doi.org/10.1007/978-3-031-93327-1_29 5. Damaziak K. et al. (2025). Multi-objective optimization of the deck structure of the lightweight micro- vehicle for improved reliability and desired comfort and stability while driving, Eksploatacja i Niezawodnosc, vol. 27 (4). DOI: https://doi.org/10.17531/ein/206048 6. Ferro C. G., Leopoldo L., Maggiore P. (2026). Cost and Sustainability Comparison of Airship vs. Long- Haul Trucking for Cold Chain Vegetable Logistics in Europe, w Sustainable Aviation. Springer Nature, рр. 247–258. DOI: https://doi.org/10.1007/978-3-031-89553-1_17 7. Folęga P. et al. (2025) Application of the life cycle assessment method in public bus transport, Eksploatacja i Niezawodnosc, vol. 27 (3). DOI: https://doi.org/10.17531/ein/204539 8. Gładysz P. et al. (2026). Reliability of Unmanned Aerial Vehicles in the Context of Selected Factors, Eksploatacja i Niezawodnosc, vol. 28 (1). DOI: http://doi.org/10.17531/ein/210312 9. Grzelak M. et al. (2024). Application of Logistic Regression to Analyze The Economic Efficiency of Vehicle Operation in Terms of the Financial Security of Enterprises, Logistics, vol. 8 (2). DOI: https://doi.org/10.3390/logistics8020046 10. Guo Y., Zhang M. (2026). Joint optimization of electric bus infrastructure planning, fleet composition, and charging schedule with multiple charging modes, Expert Systems with Applications, 297 p. DOI: https://doi.org/10.1016/j.eswa.2025.129402 11. Jung Y., Lee U., Lee I. (2026). An integrated framework for reliability analysis and design optimization using input, simulation, and experimental data: Confidence-based design optimization under aleatory and epistemic uncertainty, Reliability Engineering and System Safety, 267 p. DOI: https://doi.org/10.1016/j.ress.2025.111895 12. Lu S. et al. (2026). Multi-driver transportation scheduling for improving supply chain resilience, Omega (United Kingdom), 140 p. DOI: https://doi.org/10.1016/j.omega.2025.103461 13. Mallidis I. et al. (2026). Optimizing fleet size for on-demand taxi and ridehailing services: application to the case of Madrid, Transport Policy, 175 p. DOI: https://doi.org/10.1016/j.tranpol.2025.103868 14. Mohamed Alshabibi N., Matar A.-H. H., Abdelati M. (2025). Multi-Objective Mixed-Integer Linear Programming for Dynamic Fleet Scheduling, Multi-Modal Transport Optimization, and Risk-Aware Logistics, Sustainability (Switzerland), vol. 17 (10). DOI: https://doi.org/10.3390/su17104707 15. Purshamsian R. et al. (2026) Optimizing maritime transport schedule recovery strategies: a novel approach for speeding up and port-skipping in case of disruption, Multimodal Transportation, vol. 5 (1). DOI: https://doi.org/10.1016/j.multra.2025.100233 16. Rönnqvist M. et al. (2026) An enhanced pricing model for truck transportation: a case study in Swedish forestry, International Transactions in Operational Research, vol. 33 (2), pp. 775–797. DOI: https://doi.org/10.1111/itor.70062 17. Rosiński A. et al. (2024) Method for Assessing Reliability of the Power Supply System for Electronic Security Systems of Intelligent Buildings Taking Into Account External Natural Interference, Eksploatacja i Niezawodnosc, vol. 26 (1). DOI: https://doi.org/10.17531/ein/176375 18. Sang T. et al. (2025) An uncertain programming model for fixed charge transportation problem with item sampling rates, Eksploatacja i Niezawodnosc, vol. 27 (1). DOI: https://doi.org/10.17531/ein/192165 19. Topaloglu Yildiz S., Doymuş M. (2025) Multi-objective intermodal transportation planning with real-life application, Maritime Policy and Management, vol. 52 (5), pp. 745–763. DOI: https://doi.org/10.1080/03088839.2024.2407381 20. Wang F. et al. (2026) Multi-dimensional integrated development strategy for urban rail transit optimized via a carbon emission model driven by new quality productive forces, Case Studies on Transport Policy, vol. 23. DOI: https://doi.org/10.1016/j.cstp.2025.101664 21. Wang Z. et al. (2026) Dynamic optimization of hazardous materials vehicle transportation routes based on real-time risk, Journal of Loss Prevention in the Process Industries, vol. 100. DOI: https://doi.org/10.1016/j.jlp.2025.105864 22. Waygood E. O. D. et al. (2026) Transport emissions and climate change: Which actions are the hardest?, Case Studies on Transport Policy, vol. 23. DOI: https://doi.org/10.1016/j.cstp.2025.101660 23. Wróblewski P. et al. (2021) Total cost of ownership and its potential consequences for the development of the hydrogen fuel cell powered vehicle market in poland, Energies, vol. 14 (8). DOI: https://doi.org/10.3390/en14082131 24. Youness E.-Y., Lahoussine L. (2026) Optimization problems with nonconvex multiobjective generalized Nash equilibrium problem constraints, Communications in Combinatorics and Optimization, vol. 11 (1), pp. 93–116. DOI: https://doi.org/10.22049/cco.2024.29435.1993 25. Zhou T. et al. (2026). Integrated recovery of air cargo transportation under various abnormal scenarios, European Journal of Operational Research, vol. 329 (3), pp. 864–877. DOI: https://doi.org/10.1016/j.ejor.2025.08.037 26. Zhou X. et al. (2026) A collaborative evolution algorithm for unmanned equipment project distributed scheduling optimization with grouping and due window constraints, Expert Systems with Applications, 296. DOI: https://doi.org/10.1016/j.eswa.2025.129143 27. Ziółkowski J. et al. (2022) Optimization of the Delivery Time within the Distribution Network, Taking into Account Fuel Consumption and the Level of Carbon Dioxide Emissions into the Atmosphere, Energies, vol. 15 (14) DOI: https://doi.org/10.3390/en15145198 28. Ziółkowski J. et al. (2025). Optimization Of Road Transport Within The Supply Network – A Case Study From Poland, Transport Problems, vol. 20 (1), pp. 193–206. DOI: https://doi.org/10.20858/tp.2025.20.1.16 29. Żurek J., Zieja M., Ziółkowski J. (2018). Reliability of supplies in a manufacturing enterprise, w Saf. Reliab. – Safe Soc. Chang. World – Proc. Int. Eur. Saf. Reliab. Conf. Safety and Reliability – Safe Societies in a Changing World – Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018, CRC Press/Balkema, pp. 3143–3148. DOI: https://doi.org/10.1201/9781351174664-393 |
| Content type: | Article |
| Samling: | Галицький економічний вісник, 2026, № 3 (100) |
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