LOGISTICS SERVICE STRATEGIES IN SUPPLY CHAINS
Abstract
The article investigates strategic approaches to developing logistics service systems within supply chains, with particular attention to the management of low-turnover goods characterized by unstable and irregular demand. In modern competitive and volatile markets, logistics service plays a key role in ensuring supply chain efficiency, reliability, and customer satisfaction. The study emphasizes that the traditional Lean and Just-in-Time concepts, though effective for stable product flows, are insufficient for managing slow-moving or unpredictable inventory categories. To address this challenge, the research proposes adaptive and demand-driven strategies that integrate analytical and digital tools for service differentiation and process optimization. A central focus of the study is the integration of the ABC/XYZ classification with the Long Tail concept to design differentiated logistics service strategies based on turnover rates and demand variability. This approach enables enterprises to balance inventory costs with service levels by identifying product segments requiring flexible, customer-oriented models such as Vendor Managed Inventory (VMI), on-demand fulfillment, or 3PL collaboration. The article also presents a strategic matrix of logistics service models that aligns service priorities with the structure of product assortment and supply chain responsiveness. Particular attention is devoted to forecasting low-turnover items through a hybrid demand forecasting model combining Bayesian, cluster, and behavioral analytics. This model enhances prediction accuracy under conditions of incomplete or irregular data and supports proactive decision-making in inventory management. The research highlights practical implementations by international companies such as Amazon, Decathlon, and Zalando, which demonstrate the value of combining predictive analytics with digital tools to strengthen logistics performance and customer experience. The study further examines the potential of digital twins as simulation instruments for testing logistics service strategies in real time. Their application allows companies to evaluate alternative scenarios, identify bottlenecks, and develop data-driven recommendations to improve resilience and adaptability in supply chains. The proposed framework contributes to the theoretical and methodological development of logistics service management by combining process analytics, forecasting methods, and digital modeling. It offers practical recommendations for enterprises seeking to enhance the flexibility and efficiency of logistics operations while maintaining optimal service levels in a dynamic global environment.
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17. Anderson, E., Morrice, D., & Lundeen, G. (2020). The role of service levels in supply chain design. Operations Research, 68(2), 445–462. https://doi.org/10.1287/opre.2019.1932
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