Founded in 2009, Rent the Runway (RTR) is disrupting the trillion-dollar fashion industry and changing the way we get dressed through the Closet in the Cloud, the world’s first and largest shared designer closet. Through RTR, customers can subscribe, rent items a-la-carte, and shop resale from 800+ designer brands: from formal wear and accessories to ready-to-wear, workwear, loungewear, maternity, and more.

Managing this inventory is a complex process, involving a great deal of effort to identify individual items and route them through the warehouse and the cleaning process. That’s why the company invested in robotic arms to process orders faster and more efficiently, while also extending the lifecycle of each garment.

RTR provides its customers with access to millions of designer pieces available to rent at a fraction of their retail price. Two fulfillment centers in the United States are responsible for processing incoming garments, cleaning, and dispatching them to the next customer, as quickly as possible.

When it comes to cleaning the garments, there are dozens of methods to choose from depending on the color of the item, fabric type, solvent needed, etc. Each garment must also be checked for forgotten items, such as lipstick or ticket stubs in a pocket. Traditionally, these tasks were performed by hand, however, it was time-consuming and tedious. When COVID-19 struck and warehouse volume was lighter, RTR seized the opportunity to reimagine its existing processes and invest in automation to increase efficiency in its operations.

“Our goal is to automate the movement of goods to streamline our processes, which is why we invested in robotic arms and X-ray machines,” explained Mike Liberant, Director of Engineering, Rent The Runway. “The X-ray machines detect items that may have been left behind in the clothing, while the robotic arms ensure each item is sorted into the correct bin for cleaning.”

RTR wanted to immediately take advantage of the vast amounts of data being generated, which required a robust, flexible, and intuitive database platform.

“So the engineering team was set on a race against time to make sure our software was ready before the automation hardware was custom built and installed,” said Liberant. “We had two months to stand up a new data-driven service that would integrate testing and analytics.”

Larry Steinberg, CTO of Rent the Runway, had worked with MongoDB in a previous role and knew the Atlas platform offers best-in-class automation and proven practices that guarantee availability, scalability, and compliance with the most demanding data security and privacy standards.

RTR’s engineering team, working closely with MongoDB support, built six dedicated clusters for different services across the warehouse and fulfillment center. "We could move quickly and make changes as we needed to. We could seamlessly extract data for our data scientist to analyze, allowing us to fine tune our processes,” said Steinberg.

Now, incoming garments are X-Rayed, and then sorted by the robotic arms, which detects RFID tags in each item and places it in the appropriate bin. The bins themselves alert when they are full or if there is a problem, such as a forgotten lipstick in a pocket. All these data events automatically execute application and database logic.

“Everything data-related gets copied to our warehousing system, giving us real time analytics, unit per hour performance and visibility of any issues – we can tell whether a zipper will break, or a button will need replacing before it happens,” continues Steinberg. “We collect the data points and measure performance, to create, update, and delete as required.”

As the garments and their features evolve, with literally millions of attributes, RTR no longer has to worry about adding a new column as Atlas provides a dynamic schema, which allows RTR to quickly iterate the solution as it learns the nuances of garment attributes and cleaning solvents while ingesting analytics data and integrating the automation hardware.

The result is a 67% decrease in processing time, compared to manually checking each garment, meaning that garments are cleaned and put back on rack faster, and therefore are more readily available to customers. And system availability has improved too: RTR was able to migrate from one cloud to another seamlessly without disrupting the business and with 100% uptime. At the same time, the MongoDB platform can scale to handle growth of the company.

“Scalability is a big factor in this project’s success as is the ability to bring new technology and features to market in half the time than would otherwise have been possible,” says Liberant. “We also get enormous benefits from the code-first, dynamic data modeling approach which MongoDB makes possible. It reduces the cognitive load on our engineers and means we can evolve the schema more quickly.”

RTR also enjoys the reliability offered by MongoDB Atlas – since deployment, there has been zero downtime and zero maintenance requirements. This means the company does not have to spend valuable resources on database administrators. Instead, it has a simple, predictable, affordable monthly bill. At the same time, the introduction of the robotic arms and MongoDB Atlas has also enabled RTR to expand its cleaning techniques from six to 26. This level of detailed care and attention means clothes have a much longer lifecycle, making RTR - and its customers – more sustainable and environmentally friendly.

Larry Steinberg, CTO, Rent the Runway

Mike Liberant, Director of Engineering, Rent The Runway