Optimizing Warehouse Logistics and Inventory Management in a E-commerce Fulfillment Center

Thesis Abstract

The rapid growth of e-commerce has intensified the demand for efficient warehouse logistics and inventory management systems within fulfillment centers, underscoring a critical need for operational optimization to meet customer expectations for timely delivery and cost efficiency. This study aims to investigate strategies for optimizing warehouse logistics and inventory management in a leading e-commerce fulfillment center situated in a metropolitan logistics hub, with specific objectives to identify key logistical bottlenecks, evaluate current inventory control practices, develop an integrated optimization model, and assess the impact of technological automation on operational performance. The research employs a mixed-methods approach, integrating qualitative analysis through thematic content analysis of interviews with 25 warehouse managers and staff, alongside quantitative data analysis from a structured survey administered to 150 operational personnel and supply chain analysts. The primary sources of data include operational records, inventory tracking systems, and logistical process documentation, complemented by semi-structured interviews to gain nuanced insights. The quantitative data will be analyzed using multiple regression analysis to identify significant predictors of logistical delays, while thematic analysis will be used to interpret qualitative insights, with data triangulation ensuring robustness of findings. The study adopts the Theory of Constraints and the Technology Acceptance Model as guiding frameworks to examine process bottlenecks and technology adoption barriers, respectively, thereby providing a holistic understanding of systemic inefficiencies and facilitators. Expected findings suggest that key logistical bottlenecks are predominantly related to inadequate forecasting accuracy, suboptimal space utilization, and fragmented communication channels among warehouse sections. Additionally, the integration of automation technologies such as warehouse management systems (WMS) and automated guided vehicles (AGVs) is anticipated to significantly enhance inventory accuracy, reduce cycle times, and lower operational costs. The development of a customized optimization model employing linear programming techniques aims to provide actionable recommendations for inventory replenishment, stock location assignment, and layout redesign. This model is expected to demonstrate quantifiable improvements such as a 15-20% reduction in order processing time and a 10-15% decrease in inventory holding costs. The contribution to knowledge is substantial, as the research bridges the gap between theory and practice by providing a comprehensive framework for assessing and improving logistics and inventory practices specifically tailored to high-volume e-commerce environments. It also advances understanding of technological integration in logistics, emphasizing factors influencing successful implementation and staff acceptance. The main conclusion underscores the importance of synchronized process improvement and technological adoption in achieving operational excellence. Based on the findings, strategic recommendations include adopting dynamic inventory positioning algorithms, investing in staff training for advanced warehouse management systems, and fostering a culture of continuous improvement through data-driven decision-making. The study further advocates for future research to evaluate the long-term impacts of automation technologies in diverse e-commerce contexts, expanded to include environmental sustainability considerations. Overall, the findings aim to provide actionable insights for logistics managers and operational strategists, contributing both theoretical advancement and practical relevance to the fields of warehouse management and supply chain optimization in the digital era.