Grocery Chain Keeps the Shelves Stocked During a Pandemic with Domo
Company Size
1,000+
Region
- America
Country
- United States
Product
- Domo
Tech Stack
- Data Analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Retail
Applicable Functions
- Procurement
- Sales & Marketing
Use Cases
- Inventory Management
- Real-Time Location System (RTLS)
Services
- Data Science Services
About The Customer
Harmons is a family-owned and operated supermarket chain that started as a fruit stand in 1932. Over the years, it has grown to 19 locations throughout Utah. The company has been using Domo for several years to make data-driven decisions about which products to stock in each store to meet the specific needs of its neighborhoods. During the COVID-19 pandemic, Harmons faced the challenge of keeping its shelves stocked amid panic buying and supplier shortages. The company has 3,000 employees and generates $500 million in revenue.
The Challenge
Harmons, a family-owned and operated supermarket chain with 19 locations throughout Utah, faced a significant challenge during the COVID-19 pandemic. The sudden increase in demand for certain products, coupled with supplier shortages, made it difficult to keep shelves stocked. The situation was exacerbated when local celebrity and Utah Jazz star Rudy Gobert tested positive for COVID-19, raising awareness about the seriousness of the virus and leading to a surge in sales. Harmons needed a way to quickly identify the top-selling items and adjust their orders accordingly.
The Solution
Harmons used Domo to quickly pull a report showing the top 1,000 items sold within the last 24 hours. This data helped them identify what they needed to order to keep up with demand. The speed at which Harmons was able to pull its sales data proved to be a decisive advantage when it came time to put in orders. Harmons was able to use Domo to not only see its top-selling items, but to scan its stocks to see what was missing. This helped the grocer prioritize its top 4,000 SKUs out of its normal 60K+ SKUs without having to worry about lower-priority items. In addition, Harmons was able to specifically track its out-of-stock items and increase its orders appropriately, reducing its out-of-stock issues by 60%.
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
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
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
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.
