CREATION AND DEVELOPMENT OF A RATE MODEL TOOL FOR RETROFIT & EXPANSION INITIATIVES IN AMAZON

This thesis investigates the development and implementation of a rate model tool aimed at improving decision-making in retrofitting and expanding Amazon's EU logistics network. A comprehensive overview of Amazon’s corporate evolution, fulfilment infrastructure, and logistics strategy establishes the contextual groundwork. Particular emphasis is placed on process flows within ARS fulfilment centers, including inbound, stowing, picking, and outbound operations. These flows are critical to understanding the variables that influence retrofit decisions and rate modelling. The core of the work lies in creating a multi-layered, Excel-based rate modelling tool. This tool allows analysts to simulate baseline layouts and perform sensitivity analyses on inbound and outbound operations. The front end of the tool is designed for usability by nontechnical stakeholders, and the back end supports detailed input-output logic and scenario modelling. This tool is valuable for quickly assessing the financial feasibility of proposed expansions and retrofits, eliminating the need for full-scale architectural redesigns. Validation is achieved through practical use cases, demonstrating the model’s capacity to inform investment decisions and project scoping with minimal upfront data requirements. Ultimately, the rate model enhances Amazon’s ability to align physical infrastructure changes with strategic growth by offering a scalable framework that balances automation complexity and throughput goals.