Hazelcast IMDG Accelerates Inventory Management, Speeds Purchases
Company Size
1,000+
Region
- America
Country
- United States
Product
- Hazelcast IMDG
- Apache Cassandra
Tech Stack
- In-Memory Data Grid
- RESTful Services
- Omni-Channel APIs
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Application Infrastructure & Middleware - Data Exchange & Integration
- Application Infrastructure & Middleware - Database Management & Storage
Applicable Industries
- E-Commerce
- Retail
Applicable Functions
- Sales & Marketing
- Warehouse & Inventory Management
Use Cases
- Inventory Management
- Predictive Replenishment
- Real-Time Location System (RTLS)
Services
- Software Design & Engineering Services
- System Integration
About The Customer
The customer is a top ten retailer in the United States, known for its extensive omni-channel selling platform. This retailer has a significant online presence and relies heavily on real-time inventory management to ensure accurate order fulfillment. The company has historically used Apache Cassandra as its system of record but faced challenges in meeting its stringent service-level agreements (SLAs) due to latency issues.
The Challenge
E-commerce sales are growing rapidly, but traditional inventory management systems struggle to keep up with the high volume and velocity of data. This often results in inconsistencies between inventory databases and customer-facing systems, leading to lost sales and dissatisfied customers. Traditional relational databases and some NoSQL solutions fail to provide the necessary speed and scalability, causing latency issues and poor customer experiences.
The Solution
The retailer implemented Hazelcast IMDG, an in-memory data grid, to enhance its inventory management system. Hazelcast IMDG offers schema-less, in-memory data storage, which is significantly faster than traditional databases. It virtualizes and redundantly maintains memory across software nodes, maximizing hardware utilization and scalability. By integrating Hazelcast IMDG with their existing Apache Cassandra system, the retailer achieved real-time visibility into inventory, enabling accurate order fulfillment and reducing latency to just 2ms, well within their 40ms SLA.
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.
