Analysis of recovery and optimization strategies for SaaS solutions on shipment tracking efficiency in logistics systems
DOI:
https://doi.org/10.26661/2522-1566/2025-3/33-06Keywords:
SaaS optimization, shipment tracking, logistics management, system recovery, operational efficiency, warehouse logistics, supply chain management, UkraineAbstract
The increasing complexity of modern logistics systems, particularly in warehouse logistics and international transportation, demands robust and efficient shipment tracking solutions. This study examines the impact of recovery and optimization strategies for Software-as-a-Service (SaaS) solutions on shipment tracking efficiency within Ukrainian logistics systems. Purpose: To analyze how different recovery and optimization strategies for SaaS platforms influence the operational efficiency of shipment tracking in warehouse and international logistics operations, with focus on comprehensive management approaches. Methodology: The research employs a mixed-methods approach, combining quantitative analysis of performance metrics from 47 Ukrainian logistics companies with qualitative assessment of management strategies. Data collection included system performance monitoring over 18 months (2022-2024), semi-structured interviews with logistics managers, and comparative analysis of pre- and post-optimization metrics. Statistical analysis was performed using regression models, Data Envelopment Analysis (DEA), and cluster analysis. Findings: Implementation of comprehensive recovery and optimization strategies resulted in 34% improvement in shipment tracking accuracy, 42% reduction in system downtime, and 28% increase in overall operational efficiency. The study identifies critical success factors including proactive monitoring, redundancy planning, adaptive load distribution, and organizational readiness. Ukrainian logistics companies implementing integrated optimization strategies demonstrated significantly better performance metrics compared to those using isolated approaches. Research limitations/practical implications: The study provides actionable frameworks for logistics managers to enhance SaaS performance through strategic optimization. The developed five-level maturity model enables companies to assess their current state and identify development priorities. The findings contribute to understanding the relationship between technical infrastructure management and operational logistics efficiency, offering practical guidelines for implementation in emerging markets.
JEL Classification: M15, L86, O32, R41
References
1. Bondarenko S., Ivanov D., Koval V. Risk management strategies for SaaS implementation in unstable business environments: Evidence from Ukrainian logistics sector. International Journal of Risk Assessment and Management. 2024. 27(2), 156–174.
2. Brown A., Davis M. Predictive analytics for load balancing in cloud-based logistics platforms: A performance optimization study. Journal of Cloud Computing. 2023. 12(3). Art. 47.
3. Chen L., Wang Y., Zhang H. Digital transformation in supply chain management: The role of SaaS platforms in operational efficiency. Supply Chain Management: An International Journal. 2023. 28(4), 721–738.
4. García R., Wilson P. Integrated management models for SaaS solutions in logistics: A systems approach. Information Systems Management. 2023. 40(2), 142–159.
5. Іваненко О. П., Петров М. С. Цифрова трансформація логістичних систем в умовах невизначеності. Економіка і суспільство. 2023. Вип. 47, С. 112–128.
6. Johnson K., Martinez C. Taxonomy of recovery strategies for mission-critical logistics systems: A comprehensive framework. IEEE Transactions on Services Computing. 2023. 16(3), 1842–1855.
7. Коваленко В. М. Стратегії цифровізації логістичних компаній: український контекст. Вісник економічної науки України. 2023. № 1, С. 78–92.
8. Kovalenko O., Petrov S. Barriers to cloud technology adoption in Ukrainian logistics companies: An empirical investigation. Eastern European Business Review. 2023. 11(2), 89–106.
9. Kumar S., Singh R. SaaS adoption in logistics: Drivers, challenges, and performance outcomes. International Journal of Production Economics. 2022. 245. Art. 108397.
10. Lee J., Park S., Kim H. Integration of recovery strategies with operational processes in logistics systems. Computers & Industrial Engineering. 2022. 174. Art. 108792.
11. Мельник Т. А., Григоренко С. В. Управління ризиками в логістичних системах: методологічні аспекти. Проблеми економіки. 2024. № 2, С. 234–248.
12. Miller T., Anderson J., Roberts K. Performance optimization factors in real-time tracking systems: A comprehensive analysis. Transportation Research Part E. 2022. 168. Art. 102943.
13. Nakamura H., Tanaka Y. Dynamic evaluation models for SaaS optimization strategies using machine learning approaches. Expert Systems with Applications. 2023. 216. Art. 119471.
14. Park J., Kim D. The role of organizational culture in successful implementation of optimization strategies: Evidence from logistics sector. Journal of Business Research. 2022. 149, 832–845.
15. Petrenko A., Kovalenko N. Reliability challenges in Ukrainian logistics tracking systems: Current state and improvement strategies. Logistics Research. 2023. 16(1). Art. 23.
16. Roberts M., Taylor S. Comprehensive methodology for evaluating recovery and optimization strategies effectiveness in logistics. International Journal of Operations & Production Management. 2024. 44(1), 178–201.
17. Rodriguez F., Chen X., Kumar A. Adaptive resource management in SaaS platforms for logistics operations. Future Generation Computer Systems. 2024. 141, 234–248.
18. Шевченко Л. С., Іваненко В. О. Вплив воєнного стану на розвиток логістичної інфраструктури України. Економічний вісник. 2023. № 3, С. 156–171.
19. Thompson R., Anderson L. Geographic data redundancy models for disaster recovery in logistics systems. Reliability Engineering & System Safety. 2024. 233. Art. 109124.
20. Wang Q., Liu J., Chen K. Three-dimensional efficiency framework for SaaS platforms in logistics: Reliability, scalability, and adaptability. Decision Support Systems. 2023. 167. Art. 113923.
21. Zhang W., Li M., Sun Y. Adaptive backup strategies based on data criticality and system load in logistics platforms. Information Sciences. 2023. 622, 892–910.
REFERENCES (TRANSLATED AND TRANSLITERATED)
Bondarenko, S., Ivanov, D., & Koval, V. (2024). Risk management strategies for SaaS implementation in unstable business environments: Evidence from Ukrainian logistics sector. International Journal of Risk Assessment and Management, 27(2), 156-174. https://doi.org/10.1504/IJRAM.2024.135891
Brown, A., & Davis, M. (2023). Predictive analytics for load balancing in cloud-based logistics platforms: A performance optimization study. Journal of Cloud Computing, 12(3), Article 47. https://doi.org/10.1186/s13677-023-00421-5
Chen, L., Wang, Y., & Zhang, H. (2023). Digital transformation in supply chain management: The role of SaaS platforms in operational efficiency. Supply Chain Management: An International Journal, 28(4), 721-738. https://doi.org/10.1108/SCM-09-2022-0358
García, R., & Wilson, P. (2023). Integrated management models for SaaS solutions in logistics: A systems approach. Information Systems Management, 40(2), 142-159. https://doi.org/10.1080/10580530.2023.2196745
Ivancnko, O. P., & Petrov, M. S. (2023). Tsyfrova transformatsiia lohistychnykh system v umovakh nevyznachenosti [Digital transformation of logistics systems under uncertainty]. Ekonomika i suspilstvo, (47), 112-128. https://doi.org/10.32782/2524-0072/2023-47-15
Johnson, K., & Martinez, C. (2023). Taxonomy of recovery strategies for mission-critical logistics systems: A comprehensive framework. IEEE Transactions on Services Computing, 16(3), 1842-1855. https://doi.org/10.1109/TSC.2023.3241567
Kovalenko, O., & Petrov, S. (2023). Barriers to cloud technology adoption in Ukrainian logistics companies: An empirical investigation. Eastern European Business Review, 11(2), 89-106. https://doi.org/10.1007/s40821-023-00247-2
Kovalenko, V. M. (2023). Stratehii tsyfrovizatsii lohistychnykh kompanii: ukrainskyi kontekst [Digitalization strategies of logistics companies: Ukrainian context]. Visnyk ekonomichnoi nauky Ukrainy, (1), 78-92.
Kumar, S., & Singh, R. (2022). SaaS adoption in logistics: Drivers, challenges, and performance outcomes. International Journal of Production Economics, 245, Article 108397. https://doi.org/10.1016/j.ijpe.2022.108397
Lee, J., Park, S., & Kim, H. (2022). Integration of recovery strategies with operational processes in logistics systems. Computers & Industrial Engineering, 174, Article 108792. https://doi.org/10.1016/j.cie.2022.108792
Melnyk, T. A., & Hryhorenko, S. V. (2024). Upravlinnia ryzykamy v lohistychnykh systemakh: metodolohichni aspekty [Risk management in logistics systems: methodological aspects]. Problemy ekonomiky, (2), 234-248.
Miller, T., Anderson, J., & Roberts, K. (2022). Performance optimization factors in real-time tracking systems: A comprehensive analysis. Transportation Research Part E, 168, Article 102943. https://doi.org/10.1016/j.tre.2022.102943
Nakamura, H., & Tanaka, Y. (2023). Dynamic evaluation models for SaaS optimization strategies using machine learning approaches. Expert Systems with Applications, 216, Article 119471. https://doi.org/10.1016/j.eswa.2022.119471
Park, J., & Kim, D. (2022). The role of organizational culture in successful implementation of optimization strategies: Evidence from logistics sector. Journal of Business Research, 149, 832-845. https://doi.org/10.1016/j.jbusres.2022.05.068
Petrenko, A., & Kovalenko, N. (2023). Reliability challenges in Ukrainian logistics tracking systems: Current state and improvement strategies. Logistics Research, 16(1), Article 23. https://doi.org/10.1007/s12159-023-00523-7
Roberts, M., & Taylor, S. (2024). Comprehensive methodology for evaluating recovery and optimization strategies effectiveness in logistics. International Journal of Operations & Production Management, 44(1), 178-201. https://doi.org/10.1108/IJOPM-08-2023-0621
Rodriguez, F., Chen, X., & Kumar, A. (2024). Adaptive resource management in SaaS platforms for logistics operations. Future Generation Computer Systems, 141, 234-248. https://doi.org/10.1016/j.future.2023.11.028
Shevchenko, L. S., & Ivanenko, V. O. (2023). Vplyv voiennoho stanu na rozvytok lohistychnoi infrastruktury Ukrainy [Impact of martial law on the development of logistics infrastructure in Ukraine]. Ekonomichnyi visnyk, (3), 156-171.
Thompson, R., & Anderson, L. (2024). Geographic data redundancy models for disaster recovery in logistics systems. Reliability Engineering & System Safety, 233, Article 109124. https://doi.org/10.1016/j.ress.2023.109124
Wang, Q., Liu, J., & Chen, K. (2023). Three-dimensional efficiency framework for SaaS platforms in logistics: Reliability, scalability, and adaptability. Decision Support Systems, 167, Article 113923. https://doi.org/10.1y016/j.dss.2023.113923
Zhang, W., Li, M., & Sun, Y. (2023). Adaptive backup strategies based on data criticality and system load in logistics platforms. Information Sciences, 622, 892-910. https://doi.org/10.1016/j.ins.2022.12.001
2. Brown A., Davis M. Predictive analytics for load balancing in cloud-based logistics platforms: A performance optimization study. Journal of Cloud Computing. 2023. 12(3). Art. 47.
3. Chen L., Wang Y., Zhang H. Digital transformation in supply chain management: The role of SaaS platforms in operational efficiency. Supply Chain Management: An International Journal. 2023. 28(4), 721–738.
4. García R., Wilson P. Integrated management models for SaaS solutions in logistics: A systems approach. Information Systems Management. 2023. 40(2), 142–159.
5. Іваненко О. П., Петров М. С. Цифрова трансформація логістичних систем в умовах невизначеності. Економіка і суспільство. 2023. Вип. 47, С. 112–128.
6. Johnson K., Martinez C. Taxonomy of recovery strategies for mission-critical logistics systems: A comprehensive framework. IEEE Transactions on Services Computing. 2023. 16(3), 1842–1855.
7. Коваленко В. М. Стратегії цифровізації логістичних компаній: український контекст. Вісник економічної науки України. 2023. № 1, С. 78–92.
8. Kovalenko O., Petrov S. Barriers to cloud technology adoption in Ukrainian logistics companies: An empirical investigation. Eastern European Business Review. 2023. 11(2), 89–106.
9. Kumar S., Singh R. SaaS adoption in logistics: Drivers, challenges, and performance outcomes. International Journal of Production Economics. 2022. 245. Art. 108397.
10. Lee J., Park S., Kim H. Integration of recovery strategies with operational processes in logistics systems. Computers & Industrial Engineering. 2022. 174. Art. 108792.
11. Мельник Т. А., Григоренко С. В. Управління ризиками в логістичних системах: методологічні аспекти. Проблеми економіки. 2024. № 2, С. 234–248.
12. Miller T., Anderson J., Roberts K. Performance optimization factors in real-time tracking systems: A comprehensive analysis. Transportation Research Part E. 2022. 168. Art. 102943.
13. Nakamura H., Tanaka Y. Dynamic evaluation models for SaaS optimization strategies using machine learning approaches. Expert Systems with Applications. 2023. 216. Art. 119471.
14. Park J., Kim D. The role of organizational culture in successful implementation of optimization strategies: Evidence from logistics sector. Journal of Business Research. 2022. 149, 832–845.
15. Petrenko A., Kovalenko N. Reliability challenges in Ukrainian logistics tracking systems: Current state and improvement strategies. Logistics Research. 2023. 16(1). Art. 23.
16. Roberts M., Taylor S. Comprehensive methodology for evaluating recovery and optimization strategies effectiveness in logistics. International Journal of Operations & Production Management. 2024. 44(1), 178–201.
17. Rodriguez F., Chen X., Kumar A. Adaptive resource management in SaaS platforms for logistics operations. Future Generation Computer Systems. 2024. 141, 234–248.
18. Шевченко Л. С., Іваненко В. О. Вплив воєнного стану на розвиток логістичної інфраструктури України. Економічний вісник. 2023. № 3, С. 156–171.
19. Thompson R., Anderson L. Geographic data redundancy models for disaster recovery in logistics systems. Reliability Engineering & System Safety. 2024. 233. Art. 109124.
20. Wang Q., Liu J., Chen K. Three-dimensional efficiency framework for SaaS platforms in logistics: Reliability, scalability, and adaptability. Decision Support Systems. 2023. 167. Art. 113923.
21. Zhang W., Li M., Sun Y. Adaptive backup strategies based on data criticality and system load in logistics platforms. Information Sciences. 2023. 622, 892–910.
REFERENCES (TRANSLATED AND TRANSLITERATED)
Bondarenko, S., Ivanov, D., & Koval, V. (2024). Risk management strategies for SaaS implementation in unstable business environments: Evidence from Ukrainian logistics sector. International Journal of Risk Assessment and Management, 27(2), 156-174. https://doi.org/10.1504/IJRAM.2024.135891
Brown, A., & Davis, M. (2023). Predictive analytics for load balancing in cloud-based logistics platforms: A performance optimization study. Journal of Cloud Computing, 12(3), Article 47. https://doi.org/10.1186/s13677-023-00421-5
Chen, L., Wang, Y., & Zhang, H. (2023). Digital transformation in supply chain management: The role of SaaS platforms in operational efficiency. Supply Chain Management: An International Journal, 28(4), 721-738. https://doi.org/10.1108/SCM-09-2022-0358
García, R., & Wilson, P. (2023). Integrated management models for SaaS solutions in logistics: A systems approach. Information Systems Management, 40(2), 142-159. https://doi.org/10.1080/10580530.2023.2196745
Ivancnko, O. P., & Petrov, M. S. (2023). Tsyfrova transformatsiia lohistychnykh system v umovakh nevyznachenosti [Digital transformation of logistics systems under uncertainty]. Ekonomika i suspilstvo, (47), 112-128. https://doi.org/10.32782/2524-0072/2023-47-15
Johnson, K., & Martinez, C. (2023). Taxonomy of recovery strategies for mission-critical logistics systems: A comprehensive framework. IEEE Transactions on Services Computing, 16(3), 1842-1855. https://doi.org/10.1109/TSC.2023.3241567
Kovalenko, O., & Petrov, S. (2023). Barriers to cloud technology adoption in Ukrainian logistics companies: An empirical investigation. Eastern European Business Review, 11(2), 89-106. https://doi.org/10.1007/s40821-023-00247-2
Kovalenko, V. M. (2023). Stratehii tsyfrovizatsii lohistychnykh kompanii: ukrainskyi kontekst [Digitalization strategies of logistics companies: Ukrainian context]. Visnyk ekonomichnoi nauky Ukrainy, (1), 78-92.
Kumar, S., & Singh, R. (2022). SaaS adoption in logistics: Drivers, challenges, and performance outcomes. International Journal of Production Economics, 245, Article 108397. https://doi.org/10.1016/j.ijpe.2022.108397
Lee, J., Park, S., & Kim, H. (2022). Integration of recovery strategies with operational processes in logistics systems. Computers & Industrial Engineering, 174, Article 108792. https://doi.org/10.1016/j.cie.2022.108792
Melnyk, T. A., & Hryhorenko, S. V. (2024). Upravlinnia ryzykamy v lohistychnykh systemakh: metodolohichni aspekty [Risk management in logistics systems: methodological aspects]. Problemy ekonomiky, (2), 234-248.
Miller, T., Anderson, J., & Roberts, K. (2022). Performance optimization factors in real-time tracking systems: A comprehensive analysis. Transportation Research Part E, 168, Article 102943. https://doi.org/10.1016/j.tre.2022.102943
Nakamura, H., & Tanaka, Y. (2023). Dynamic evaluation models for SaaS optimization strategies using machine learning approaches. Expert Systems with Applications, 216, Article 119471. https://doi.org/10.1016/j.eswa.2022.119471
Park, J., & Kim, D. (2022). The role of organizational culture in successful implementation of optimization strategies: Evidence from logistics sector. Journal of Business Research, 149, 832-845. https://doi.org/10.1016/j.jbusres.2022.05.068
Petrenko, A., & Kovalenko, N. (2023). Reliability challenges in Ukrainian logistics tracking systems: Current state and improvement strategies. Logistics Research, 16(1), Article 23. https://doi.org/10.1007/s12159-023-00523-7
Roberts, M., & Taylor, S. (2024). Comprehensive methodology for evaluating recovery and optimization strategies effectiveness in logistics. International Journal of Operations & Production Management, 44(1), 178-201. https://doi.org/10.1108/IJOPM-08-2023-0621
Rodriguez, F., Chen, X., & Kumar, A. (2024). Adaptive resource management in SaaS platforms for logistics operations. Future Generation Computer Systems, 141, 234-248. https://doi.org/10.1016/j.future.2023.11.028
Shevchenko, L. S., & Ivanenko, V. O. (2023). Vplyv voiennoho stanu na rozvytok lohistychnoi infrastruktury Ukrainy [Impact of martial law on the development of logistics infrastructure in Ukraine]. Ekonomichnyi visnyk, (3), 156-171.
Thompson, R., & Anderson, L. (2024). Geographic data redundancy models for disaster recovery in logistics systems. Reliability Engineering & System Safety, 233, Article 109124. https://doi.org/10.1016/j.ress.2023.109124
Wang, Q., Liu, J., & Chen, K. (2023). Three-dimensional efficiency framework for SaaS platforms in logistics: Reliability, scalability, and adaptability. Decision Support Systems, 167, Article 113923. https://doi.org/10.1y016/j.dss.2023.113923
Zhang, W., Li, M., & Sun, Y. (2023). Adaptive backup strategies based on data criticality and system load in logistics platforms. Information Sciences, 622, 892-910. https://doi.org/10.1016/j.ins.2022.12.001
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Published
2025-10-20
Issue
Section
Management
How to Cite
Mukha, T. and Popova, N. (2025) “Analysis of recovery and optimization strategies for SaaS solutions on shipment tracking efficiency in logistics systems”, Management and Entrepreneurship: Trends of Development, 3(33), pp. 73–83. doi:10.26661/2522-1566/2025-3/33-06.
