Case Study - Technology transformation using microservices
A leading apparel retailer grows new personalized services line 5X in three years
A leading apparel retailer, successfully grew their new Custom Clothing line 5.5X in three years, with SkillNet’s reengineering of their legacy retail systems for higher speed, selection and service. Now client’s Expert Style and Wardrobe consultants use an instore app on a handheld device to design and order custom tailored clothes.
About the client
The client is a leading specialty retailer, who deliver personalized products and services through an expansive omnichannel network that includes over 1,400 locations in the U.S. and Canada as well as branded e-commerce websites.
Challenge | Major technology transformation required to compete on speed, selection and service
The client offers ready-to-wear clothing across their multiple specialty stores brands. In 2016, client saw an opportunity in offering Custom Clothing at the stores.
Expert wardrobe consultants and master tailors used an in-store app not only to order custom apparel but also advise on customizations available in styles, colors, fabric choices and for an optimal fit.
However, the interaction offered a very narrow range of options for custom details like working buttonholes, pick stitching, lining, thread color and more. In order to enhance customer experience and compete better in Custom Clothing, client needed to focus on three things: speed, selection and service.
This was now a technology challenge for the legacy systems. Clients heavily customized retail systems, still using flat files, required a three-month cycle to add every single custom option. As a further challenge, each of the nine sub-brands had their own customized legacy technology.
Over the years, as no commercially available system could match the unique requirements, the legacy retail systems had been built with heavy customizations to match the business needs of client’s nine brands. This introduced technical debt and a limited ability to introduce new features without high risk of operational impact in multiple areas.
The client realized the need to re-engineer their existing monolithic solution to enable further changes to their systems. In order to do this redesign from within they needed a partner with deep experience and capability on retail solutions that could work both with the latest technologies and the deployed legacy. They engaged SkillNet.
Solution | Technology transformation using microservices, implemented incrementally
SkillNet proposed a microservices approach for the legacy POS replacement. Their POS and stores expertise allowed them to identify the microservices to be extracted and used across multiple channels, providing omnichannel capability from the ground up. The creation of these services also meant the removal and simplification of existing retail systems.
The first step was to re-architecture their data model. SkillNet created catalog, store, inventory schemas and built the micro-services on top of these schemas.
The Store Service updates the data pertaining to a store and also manages the events and product labels available for the store within the company. Catalog service focuses on information pertaining to products, fabrics, models etc. It also holds the key information of a factory, company and labels available. When customers browse online, this application provides the information which is displayed. The Inventory service provides information on any given fabric, based on the factory where its available.
In order to make the changes in the monolith system, the team not only needed a different architecture and tools but also introduced a different way of working, The DevOps enablement and agile approach represents complete transformation from previous practices for software development and project execution.
While the transformation is ongoing, the initial phase has allowed introduction of new features in stores. To introduce these changes, SkillNet proposed a “Strangler Pattern” for incremental transformation of the overall solution. This allows for new features to be available early and avoids the need for a big program with a cliff hanger at the end of the implementation.
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