E-COMMERCE LOGISTIC MANAGEMENT SYSTEM: PROCUREMENT WITH AUTOMATED PERFORMANCE DASHBOARDS, FORECASTING AND ANALYTICS

Abstract

This study tackles critical inefficiencies prevalent in e-commerce logistics, where many businesses rely heavily on manual record-keeping tools—such as logbooks, spreadsheets, and simplistic dashboards—that lack real-time data integration and predictive analytics. Such outdated systems lead to frequent delivery delays, inventory discrepancies, and uninformed procurement decisions, all of which detrimentally impact customer satisfaction and competitive positioning. To address these challenges, the researchers propose a comprehensive automated procurement management system that seamlessly combines interactive performance dashboards, sophisticated forecasting models, and advanced analytics. By delivering actionable, predictive insights on demand variability, inventory optimization, and supplier reliability, the system empowers businesses to make data-driven decisions that enhance operational efficiency and drive sustained growth. The project was executed using the Agile Scrum framework, progressing through iterative sprint cycles that encompassed comprehensive phases of planning, daily stand-ups, development, rigorous testing, and review. Dedicated roles including Scrum Master, Product Owner, Business Analyst, Security Specialist, Programmer, and Documentation Specialist, were strategically assigned to uphold clear ownership and accountability from initial scope definition through to deployment. To ensure the system’s alignment with real-world needs, the researchers employed a mixed-methods approach, conducting in-depth stakeholder interviews alongside detailed observational analyses of existing e-commerce platforms to capture precise requirements and contextual insights. The implemented system effectively met its goals by automating procurement workflows, enabling real-time inventory visibility, and optimizing logistics coordination. Interactive dashboards deliver comprehensive visualizations of vital metrics such as order accuracy, supplier lead times, and stock levels, while advanced forecasting algorithms project next-month demand based on real-time data analytics. Rigorous integration testing confirmed flawless data synchronization between system modules and third-party delivery APIs, and load testing verified the system’s robustness and scalability under peak operational loads. The findings underscore the transformative impact of embedding automated analytics and forecasting into e-commerce logistics operations. Transitioning from reactive record-keeping to proactive, data-driven strategies enables businesses to accurately anticipate demand surges, optimize inventory levels, and proactively mitigate supply-chain disruptions. The Agile Scrum framework facilitated rapid iteration and strong stakeholder alignment, though integrating heterogeneous module architectures and ensuring real-time data integrity posed significant challenges, addressed through robust middleware solutions and rigorous testing. Additionally, user training emerged as a pivotal factor for successful adoption, highlighting the necessity for intuitive user interfaces coupled with comprehensive change-management support.