E-commerce is rising in demand and sales are forecasted to reach 7.4 trillion dollars by 2025. Many products are shipped to consumers from warehouses directly rather than being displayed in a physical retail store. Smooth and effective warehousing operations are more crucial with the increased dependency on warehouses nowadays. More than 130 key performance indicators (KPIs) exist for warehouses. Often a summarized and meaningful metric is desired to give an accurate evaluation of the overall warehouse performance (OWP). The most common method of measuring OWP used in the literature is done using data envelopment analysis (DEA). However, it is difficult to find empirical evidence that DEA has significantly improved performance evaluation and benchmarking in actual non-production conditions. A new method for measuring OWP is explored using a widely accepted overall metric in the manufacturing industry, namely overall equipment effectiveness (OEE). The use of OEE as a performance measure for warehouses or in logistics generally has not been researched extensively yet. The research problem lies in evaluating the overall performance of warehouses using a modified OEE framework. Therefore, a challenge to identify the components of a modified OEE for warehousing was present. Additionally, a modified OEE formula for the automated warehouse was made based on the major losses that occur in such warehouse. The nature of the items processed at a warehouse and the warehouse utilization stage impacts the KPIs to be considered by a modified OEE model. A framework for a modified OEE for warehouses was developed using these inputs. It was agreed that one of the main uses of an OWP measure lies in reporting performance to different stakeholders, mainly the warehouse contractor. Additionally, using OEE can give insight to areas of improvement which gives a science-based ground for continuous improvement. The research was done using a single case study in the inbound part of an automated warehouse for online clothing retail with an industry leader. Expanding this research into other warehouse areas is suggested, and a final OWP using a modified OEE model may be achieved after numerous iterations and case studies. Further quantitative analysis of the performance of OEE as an OWP can validate the results more. It is argued that a universal indicator of some sort using OEE can be achieved in the future, this can then be used for benchmarking alike warehouses.