99 Ranch Market is an Asian supermarket chain with over 40 stores. They implemented SAP HANA to revamp their data warehouse and analytics capabilities. This allowed for faster daily data loads, improved reporting flexibility, and more targeted customer promotions. It also optimized logistics processes like inventory and replenishment. The implementation of SAP HANA reduced manual data combining and performance issues while simplifying the IT landscape. 99 Ranch now has faster analytics, simplified maintenance, and reduced total cost of ownership.

2. How 99 Ranch, A Market Grocer Supercharged their
data warehouse with SAP HANA
Jonson Chen – Tawa Supermarket Inc.,
Sarath Arabandi – Zensar Technologies Inc.,
SESSION CODE: RL295

3. Tawa Supermarket – A Global Asian Foods Supermarket - Customer
99 Ranch Market was established in 1984, with its first store in
Westminster (Little Saigon), California. With the support of our loyal
customers and hardworking employees, we have grown to become
one of the largest Asian supermarket chains in the United States, with
over 40 store locations in California, Nevada, Texas and Washington
State. (Read more: https://www.99ranch.com)
Zensar – Implementation Partner
Zensar Technologies is a technology partner of choice for global organizations looking to strategically transform,
grow, and lead in today’s challenging business environment. Backed by a strong track-record of innovation, over
7600 associates and footprint in more than 22 global locations, Zensar’s comprehensive range of software
services and solutions enable its 400+ forward-looking customers to cross new thresholds of business
performance (Read more: http://www.zensar.com).
ABOUT..

4.  Revamping the KPI’s to take advantage of daily data
 Improve data visibility and reporting flexibility
 Analytics that are actionable, especially to grocery
 Optimize logistics and replenishment processes, such as stock-level
monitoring, stock transfers, inventory, and replenishment automation
 Create more targeted Customer Promotions as well as new Customer
attraction
Business Challenges

5.  Manual & time consuming process to combine POS & SAP data, also
limiting timely analysis
 Performance issues with current BW reports
 Provide flexible and granular reporting to business users, allowing them to
quickly run complex requests
 Simplify the IT landscape and lower total cost of ownership (TCO)
Current Challenges / Future Requirements

6.  Faster analytics and reporting
 Faster data loads into the data warehouse
 Simplified system maintenance activities - No longer needs to create and
maintain aggregates and indexes
 Reduced remodeling effort to apply structural changes quickly enough to
meet business changes
 Reduced administration and TCO
Return on Investment

7.  Dramatically Improved System Performance
 Enhanced functionality within Admin Cockpit
 Enhanced Scalability & Performance for faster
decision making
 Accelerated Data Loads
 POSDM in-Memory Analytics
 Simplified Database Administration
Major Benefits of BW on HANA

8.  Migration of SAP BW 7.3 system from MSSQL to HANA database
 SAP Retail Content
 50 users
 23 InfoProviders
 100 Reports
 Migrate existing POS transaction data to the new SAP POS DM 1.0
 Customize and Activate POS In-Memory Analytics
 Convert standard cubes to HANA optimized cubes
Scope & Infrastructure

9. BW Landscape
POS Systems
SAP ECC
SAP BW on HANA
Kronos
SAP PI
SAP BO
SAP BO

10.  Technical Migration of BW system to new HANA DB
 Mixed data flow versions (3.X & 7.X)
 Integration Points:
• SAP POSDM
• SAP PI
• KRONOS
 Go-Live window
• Fixed Dates / Short window for cutover
Challenges

11.  Daily data loads 10x faster
 Reduced technical maintenance activity with the
elimination of indexes and aggregates
 Average BW query speed improved by 15x
 Selected Examples:
• Article GP Margin report has been improved from
48 minutes to 3 minutes
• Retail assessment report has been improved from
15 minutes to 30 seconds
• Sales Projection report has been improved from
35 minutes to 45 seconds
Benefits after Implementation

12.  Analyze Store performance by using
POS In-Memory Analytics
 50% increase in predictive analysis
capability
 60% faster Inventory replenishment all
categories
 30% faster Inventory replenishment for
perishable products
 Close to 90% reduction in report loading
and display time
Benefits after Implementation

13.  Hardware sizing including additional applications running on top of HANA
 Partitioning of large Info providers prior to Migration
 Apply the latest patches and notes
Learning Points

14.  BW Housekeeping activities to be run prior to system Migration
 Always use latest Kernel, R3load and R3ta tools
 Use BW on HANA migration cockpit tool to perform all tasks for Pre and Post
migration activities
Best Practices

15. Q&A

16. Thank You for Participating!
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SESSION CODE: RL295
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