2025, Vol. 8, Issue 1

author(s)
Doan Nam Hai

abstract
Dynamic pricing models have become an essential tool in logistics product management, enabling businesses to balance cost efficiency with shifting market demands. These models utilize real-time data, advanced algorithms, and predictive analytics to adjust pricing dynamically, ensuring logistics providers remain competitive while optimizing resource utilization. Key factors influencing dynamic pricing include transportation costs, demand volatility, inventory levels, and competitive strategies. The integration of machine learning further enhances pricing decisions, allowing for rapid adaptation to market fluctuations. This study examines the role of dynamic pricing in improving operational performance, addressing both opportunities and challenges associated with its implementation in logistics. Additionally, it highlights potential research directions to refine these models and enhance their practical applications in an increasingly dynamic supply chain environment.