Flipkart Improves Inventory Utilization with QlikView
公司规模
Large Corporate
地区
- Asia
国家
- India
产品
- QlikView Enterprise Edition
- QlikView 11
技术栈
- Open Source ERP software
- Microsoft Excel
- MySQL
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Cost Savings
技术
- 分析与建模 - 实时分析
适用行业
- 电子商务
- 零售
适用功能
- 销售与市场营销
- 仓库和库存管理
用例
- 库存管理
- 供应链可见性(SCV)
服务
- 数据科学服务
- 系统集成
关于客户
Flipkart is the largest B2B e-commerce business in India. It was founded in 2007 by entrepreneurs Sachin Bansal and Binny Bansal with the objective of making books easily available to anyone with internet access. Since then, Flipkart has diversified from book sales into other products. It sells entertainment products—from movies, music to consumer electronics—as well as healthcare and personal products, home appliances, and clothing. The rapid diversification has also brought rapid growth, illustrated by the company’s expanding network of warehouses and procurement operations.
挑战
Flipkart, the largest B2B e-commerce business in India, experienced rapid growth and diversification, expanding its network of warehouses and procurement operations. This scale added to the need for Flipkart to continually upgrade its business discovery software to better serve multiple business units with new applications. As a startup, Flipkart initially used open source technology to support the launch, but soon found that open source business intelligence (BI) tools had limited functionality. Scalability was a problem and it was hard to drill down into their data to produce complex reports. Flipkart was experiencing an increasingly complex data flow from different business units—for example, inventory and supply chain.
解决方案
In August 2011, the company invested in its first licensed business discovery software with QlikView Enterprise Edition, which has since been upgraded to QlikView 11. Flipkart needed a smart system to collate and display meaningful and actionable analysis for decision makers across different business units and locations. QlikView provided the flexibility and speed in their analytics system that they needed. The first application for sales took just six weeks to implement and each subsequent functional area— supply chain, inventory and finance—took a similar effort. Another high-performing application in QlikView is for inventory management. Sales people and managers access data and find information quickly without any IT support.
运营影响
数量效益
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
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.
