OPTIMIZING SUPPLY CHAIN EFFICIENCY LOGISTICS PREDICTIVE ANALYTICS

Abstract

In the modern era, technology continues to advance rapidly, leading to innovative solutions that simplify human life. One significant development is the integration of predictive analytics into logistics, which enhances supply chain efficiency. According to Bienstock et al. (2008), logistics service quality can be evaluated by metrics such as tracking and tracing accuracy, service speed, location precision, and pricing effectiveness. This study focuses on how transportation and logistics services adapt to these standards by implementing advanced tracking and tracing systems. The goal is to explore the role of predictive analytics in optimizing logistics operations and ensuring high service quality. This study draws insights from the work of Martinez, J. A., & Martinez, E. G. (2021), which emphasizes the use of Agile methodologies—particularly the Scrum framework—in developing Transportation Management Systems (TMS). The research examines how Agile approaches can address the dynamic challenges in logistics by allowing flexible, iterative development cycles. Through this framework, the development process adapts efficiently to the evolving needs of the logistics sector. Due to the rapid growth in transportation demands, the proposed system offered an efficient solution for vehicle service logistics within the transportation network. With the application of predictive analytics, the system provided measurable improvements in supply chain performance. The results demonstrated that forecasting capabilities and real-time data analysis enabled better decision-making and resource allocation across logistics operations. The Agile Scrum process was crucial in the system’s development and implementation. The cycle began with sprint planning, where the team selected and scheduled specific tasks from the product backlog. Daily stand-up meetings facilitated communication and progress monitoring, while sprint reviews allowed for stakeholder feedback on completed work. Finally, sprint retrospectives provided opportunities for process evaluation and improvement. These iterative phases, as outlined by Asana (2024), enhanced team collaboration and ensured the system’s continuous refinement. The study concludes that predictive analytics combined with Agile development contributes significantly to logistics efficiency and supply chain optimization.