E-commerce is rapidly growing and is expected to encompass a quarter of all global sales by 2025. This growth pressures e-commerce warehouses to enhance efficiency. A promising innovation is the Robotic Mobile Fulfilment System (RMFS), which optimises warehouse operations by using robots to manage storage and retrieval tasks, thus significantly improving productivity, speed and accuracy. This research focuses on how inventory allocation (slotting) decisions with RMFS can optimise operational performance. In particular, how the slotting decision of Stock Keeping Unit (SKU) distribution across movable storage racks (pods) based on SKU turnover can maximise order throughput rates and optimise operational performance. The research question guiding this study is: What is the optimal demand-based slotting decision to maximise the order throughput rate in a Robotic Mobile Fulfilment System? This question aims to provide insights into how different slotting configurations impact the efficiency and performance of ecommerce warehouses. The research approach is twofold. A general analysis is conducted to understand the impact of turnover-based slotting decisions using synthesised demand profiles derived from literature. This is followed by a detailed case study for Gall&Gall using demand profiles derived from real-world data to find specific optimal slotting configurations and validate the synthesised demand results. The methodology involves three main steps: determining demand configurations, generating slotting configurations with a mathematical model, and simulating these configurations to evaluate performance. Each demand configuration results in multiple slotting configurations, which are evaluated with the simulation to gain insights into the effect of slotting decisions on performance. The different demand profiles consist of total SKU quantity, total item quantity, and SKU classification into three classes (A, B and C) based on their item turnover. The different slotting configurations consist of different distributions of the three classes over the pods. These slotting configurations are obtained with a mathematical model that prioritises class distribution based on given weights. The simulation tool RawSim-O assesses the slotting configurations on key performance indicators such as total order throughput rate and the number of items picked from a pod in one go (pile-on). Key findings provide that pile-on and travel distance significantly affect the order throughput rate, with performance variations of up to 40 orders handled in 30 minutes. High performance often arises with configurations aiming for an equal number of items per pod across classes and maximising the number of pods for SKUs in class A. While synthetic demand profiles show high performance with class A distributed over the maximum number of pods or equal items per pod for all classes, the Gall&Gall demand profiles perform better with class B distributed over slightly more pods, indicating variability in optimal slotting approaches based on specific demand characteristics. Overall, turnover-based slotting decisions significantly impact order throughput rates in RMFS, and tailoring slotting configurations to specific demand characteristics is crucial for optimal operational efficiency. In addition to general slotting insights, this research developed a method that allows warehouses to input their specific demand characteristics and receive insights on optimal slotting approaches. Furthermore, the method enables the readjustment of warehouse-specific details, such as a warehouse’s unique layout, for extra applicability and realism, and allows the integration of additional decision problems, such as order batching and routing, to broaden the method’s scope. This supports warehouses with the design of a tailored, robust and effective slotting strategy for operational performance improvement