Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Blue Yonder retail solution
Tech Stack
- Excel
- Access databases
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Productivity Improvements
- Cost Savings
Technology Category
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
Use Cases
- Inventory Management
- Supply Chain Visibility
Services
- System Integration
About The Customer
DSW is a leading U.S.-based footwear specialty retailer. The company evolved from a close-out retailer into a large footwear specialty retailer in the mid-2000s. DSW's stores average over 20,000 square feet and feature approximately 24,000 pairs of shoes and accessories. The company's business model involves limited backrooms to keep excess merchandise, necessitating exacting assortment management for each market. DSW sought to improve its supply chain processes and technology tools to better manage its inventory and increase sales and margins.
The Challenge
DSW, a leading U.S.-based footwear specialty retailer, faced a strategic challenge due to the fixed capacity of its branded locations. The stores, which average over 20,000 square feet and feature approximately 24,000 pairs of shoes and accessories, had limited backrooms to keep excess merchandise. This necessitated exacting assortment management for each market. The existing supply chain processes and technology tools only focused on the highest levels of the business and overall metrics. The planning was done in dollars at the department level, which was not sufficient for the company's needs. DSW saw an opportunity to drop down to a deeper level planning solutions and realize significant benefits.
The Solution
To support both financial and merchandise planning, DSW implemented a state-of-the-art Blue Yonder retail solution. This solution was capable of aligning planning, assortment allocation, and pricing, enabling DSW to project the right inventory levels, keep stores optimally stocked, and purchase the right products, including the right mix of shoe sizes. The company transitioned from having a mosaic of Excel worksheets, Access databases, and numerous other independent pieces of technology to relying on a unified set of solutions that were customized to their unique business needs.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Improving Production Line Efficiency with Ethernet Micro RTU Controller
Moxa was asked to provide a connectivity solution for one of the world's leading cosmetics companies. This multinational corporation, with retail presence in 130 countries, 23 global braches, and over 66,000 employees, sought to improve the efficiency of their production process by migrating from manual monitoring to an automatic productivity monitoring system. The production line was being monitored by ABB Real-TPI, a factory information system that offers data collection and analysis to improve plant efficiency. Due to software limitations, the customer needed an OPC server and a corresponding I/O solution to collect data from additional sensor devices for the Real-TPI system. The goal is to enable the factory information system to more thoroughly collect data from every corner of the production line. This will improve its ability to measure Overall Equipment Effectiveness (OEE) and translate into increased production efficiencies. System Requirements • Instant status updates while still consuming minimal bandwidth to relieve strain on limited factory networks • Interoperable with ABB Real-TPI • Small form factor appropriate for deployment where space is scarce • Remote software management and configuration to simplify operations
Case Study
Digital Retail Security Solutions
Sennco wanted to help its retail customers increase sales and profits by developing an innovative alarm system as opposed to conventional connected alarms that are permanently tethered to display products. These traditional security systems were cumbersome and intrusive to the customer shopping experience. Additionally, they provided no useful data or analytics.
Case Study
How Sirqul’s IoT Platform is Crafting Carrefour’s New In-Store Experiences
Carrefour Taiwan’s goal is to be completely digital by end of 2018. Out-dated manual methods for analysis and assumptions limited Carrefour’s ability to change the customer experience and were void of real-time decision-making capabilities. Rather than relying solely on sales data, assumptions, and disparate systems, Carrefour Taiwan’s CEO led an initiative to find a connected IoT solution that could give the team the ability to make real-time changes and more informed decisions. Prior to implementing, Carrefour struggled to address their conversion rates and did not have the proper insights into the customer decision-making process nor how to make an immediate impact without losing customer confidence.
Case Study
Ensures Cold Milk in Your Supermarket
As of 2014, AK-Centralen has over 1,500 Danish supermarkets equipped, and utilizes 16 operators, and is open 24 hours a day, 365 days a year. AK-Centralen needed the ability to monitor the cooling alarms from around the country, 24 hours a day, 365 days a year. Each and every time the door to a milk cooler or a freezer does not close properly, an alarm goes off on a computer screen in a control building in southwestern Odense. This type of alarm will go off approximately 140,000 times per year, equating to roughly 400 alarms in a 24-hour period. Should an alarm go off, then there is only a limited amount of time to act before dairy products or frozen pizza must be disposed of, and this type of waste can quickly start to cost a supermarket a great deal of money.
Case Study
Supermarket Energy Savings
The client had previously deployed a one-meter-per-store monitoring program. Given the manner in which energy consumption changes with external temperature, hour of the day, day of week and month of year, a single meter solution lacked the ability to detect the difference between a true problem and a changing store environment. Most importantly, a single meter solution could never identify root cause of energy consumption changes. This approach never reduced the number of truck-rolls or man-hours required to find and resolve issues.