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.