khnc - retail deployment scenarios with customer case study

KHNC - Retail Deployment Scenarios with Customer case study

Debraj Roy– Customer Solution Adoption (CSA)May, 2012

© 2012 SAP AG. All rights reserved. 2Confidential

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3

Learning Points

How Retailers can use the power of Hana to impact their Business

Retail Deployment Options

Case- Study, Lessons Learned in different areas of HANA implementation


Topics1

BW on HANA –Retail Scenarios and Deployments

3

HANA based Retail Scenarios and Deployments

2

HANA for Retail

Customer Case study 5

Q&A

6


Topics1


3


2

HANA for Retail


Q&A

6


Retail Information is Exploding

Customer DataCompetitive Prices

GPS

Promotion Planning

Spe

ed

Hot Trends

Multi ChannelSto

re E

nhan

cem

ents

Em

ployee

Records

Suppliers

Purchase Orders

Merchandise Em

ails

Tweets

Planning

Social Media

MobileInstant M

essages


Successful retail companies…

Deliver products and services that drive loyalty to the brand

Reach millions of consumers with targeted seamless cross channel experiences

Capitalize on new sales opportunities and maximize margin return

Improve customer services to increase competitiveness

Personalizing customer interaction

Identifying and seizing new opportunities

Accelerating fulfillment lifecycle

They do it by…


SAP HANA for RetailValue Proposition

Simplify and unify business processes

Order fulfillment based on enterprise-wide product availability transforms customer experience

Hourly sales and stock information across all organizations and channels

Access precise data to make informed decisions

Real time profitability analysis at assortment and location helps pinpoint performance issues

Instant calculation of vendor rebates ensure all monies are collected on time

Speed You Need -Where it Impacts Your Business

Respond with personalized and targeted information and offers anywhere, any time

Customer interaction provides insight to develop new and exciting assortments


1 BillionStore-item quantities by zone

800%Increased reportable detailed sales history from 3 to 24 months

8,100xFaster reporting speed

In-memory Computing Technology ImpactsVelocity - Volume - Value


Common Retail Scenarios relevant for In-Memory Computing

Discontinued Items Analysis, Non-Moving Articles Analysis

Stock Balance Reports and Stock History

Out Of Stock Analysis (On-Hand Stock(RT), Goods Movement)

POS Data Analysis by Article, Store , Day

Summary Sales Reports

On Shelf Availability (Predictive)

Real Time Stock Position Analysis

Sales Analysis Reports (On-Hand , On-Transit , Sales reports)

Inventory Scenarios

POS Analytics Scenarios

Inventory with POS*

* New Scenario enabled by HANA


Topics1


3


2

HANA for Retail


Q&A

6


Retail Reporting Scenarios – Enabled by HANA

Real time visibility into Current stock situation by replicating ECC material master and filtered stock information into HANA

• Discontinued Items Analysis, Non-Moving Articles Analysis -Identify non-moving and discontinued items in real time so that retailers could increase their revenue by creating promotions, transferring to other stores, returning to vendors, making other adjustments, etc

• Stock Balance Reports Store wise visibility into current stock situation /stock summary

Agile stock modeling combining ECC master data and stock information's with non-SAP forecasting information from a 3rd party system

• Stock History Identify historical stock information's in real time so that retailers could see the trend of sales in the past and stock forecasting information stored in 3rd party system at any point of time

Inventory Scenarios


Retail Reporting Scenarios – Enabled by HANA

Point of Sales Analysisby loading granular POS data (billions of records), into HANA ( billions of records) . Sales Summary and ad-hoc analysis across multiple dimensions like Products, Customers , Time, Stores and multiple sales key figures like total sales, basket sales, Customer spend, etc

POS combined with Inventory

POS Analytics Scenarios

Out Of Stock Analysis (POS*, Inventory Goods Movement)Feeding precise POS data into HANA and combining it with the ECC goods movement can help retailers reduce inventory carrying costs and gain critical insight into customer buying patterns and behavior

*Note that SAP POS DM data can also be loaded to HANA. However it is preferred to migrate to POS DM on HANA to benefit from standard content.


Inventory and POS Data Analysis Scenarios –Deployment with HANA

Pure Inventory Scenarios with SAP ECC

SAP HANA

SAP ERP

SAP BO Clients

ReportingSQL / BICS

Sou

rce

Sys

tem

Inventory, Master Data

Stock AnalysisModels

SLT/ real-time

Current Stock

Article Mvmt. …

Master Data HANA-View

Inventory and POS Combined - SAP & Non–SAP Source Systems

SAP HANA

SAP ERP

SAP Business Objects Clients

ReportingSQL / BICS

Sou

rce

Sys

tem

Master Data Stock AnalysisModels

SLT/ real-time

Non SAP / DW

Inventory

Data Services

SAP BO ClientsStock history

Sales Analysis.

Master Data

HANA-View

POS

Sales Data as FlatFiles Data Services


Topics1


3


2

HANA for Retail


Q&A

6


Retail Reporting Scenarios – Enabled by BW on HANA

Inventory BW Content• Inventory value monitoring• Returns analysis for supplier/ customer returns• ABC classification• Cross selling / substitutions analysis

Near real-time Custom Content : • On-Hand , On-Stock report provide an overview by site

showing on hand sales units, on hand dollars, on order units, on order dollars. Provides stores with a snapshot of relevant inventory data for making present and future ordering decisions

• Stock Balance Reports Store wise visibility into current stock situation /stock summary)

Inventory Analytic Scenarios

Value Proposition:Revenue due to managed inventory (promotion, sales, return to vendors, etc)



Near Real-time Basket Analysis.

• Customer Loyalty Analysis becomes supercharged with total customer attributes and behaviors by combining customer data with POS data

• Replenishment quantity Analysis by merging sales data with real consumption data from non-SAP sources, will provide the ability to plan correct replenishment quantities

• Sales and Forecast Analysis down to the lowest level of detail to identify key value items while integrating customer buying patterns to drive profitable promotions

• EDW Reporting Services provided by the Global Retail Hub

POS Data Analytic Scenarios

Value PropositionSLA to process POS sales data within agreed processing windows



Near Real-time Stock position

• Stock Trend Analysis (POS Data, Inventory Goods Movement) Feeding NON SAP POS data into BW on HANA and combining it with the ECC goods movement or NON SAP goods movement help retailers to reduce inventory carrying costs and gain critical insight into customer buying patterns and behavior

• Out Of Stock Analysis (POS Data, Inventory Goods Movement) Feeding NON SAP POS data into BW on HANA and combining it with NON SAP goods movement help retailers to monitor stock Level and active promotions to optimize Material Store availability and Level of Stock value

Inventory + POS Data Analytic Scenarios

Value PropositionMonitoring of Stock Level and active Promotions to optimize Material Store availability and Level of Stock value


Stock Position – Deployment options with BW on HANA at a glance

SAP & Non–SAP

SAP HANA SP3

SAP ERP


ReportingSQL / BICS

Sour

ce

Syst

em

Master Data

SAP BW 7.3 SP5ETL /Extractors

Non SAP

POS Data ETL

SAP BO ClientsOn Hand Stock

On Hand Stock

Out Of StockOut Of Stock

…

Master DataMulti-

Provider

POS DSO

Inventory Inventory DSO

SAP HANA SP3

SAP ERP


ReportingSQL / BICS

Sou

rce

Sys

tem

Master DataSAP BW 7.3 SP5

BW Virtual Provider

ETL /Extractors

Non SAP

POS DataETL

SAP BO ClientsOn Hand

StockOut Of Stock …

Master DataMulti-

Provider

TLOGF Analytic View

Inventory

BW InventoryContent

SAP & Non–SAP

SAP HANA SP3

SAP ERP


Reporting

SQL / BICS

Sour

ce

Syst

em

Master Data

SAP BW 7.3 SP5ETL /Extractors

Non SAP

POS Data SLT

SAP BO ClientsOn

Stock

On Hand Stock

Out Of StockOut Of Stock …

Master Data Multi-Provider

Inventory

Inventory*

TLOGF

Analytic View

BW Virtual Provider

SAP BW Inventory + Real time POS Extraction Based

Near Real Time

* Inventory data from SAP ERP can also be replicated realtime to HANA data marts


Topics1


3


2

HANA for Retail


Q&A

6


Customer Scenario–Use Case

Why HANA•To drive in store consumer traffic and sales by having the right product in stores at right time (detect and replace non-moving, discontinued articles)

•Reducing Inventory costs for non-moving and discontinued articles •Strategic advantage with vendors – by negotiating lower rates/discounts for non moving articles

•Gain visibility into discontinued articles in near real time

Benefits•Multi-million dollar increase in store sales by accelerating the replacement of non-moving articles

•Reduction in non-moving articles from 5% to 2% annually leading to multi-million dollar savings on a yearly basis


Case Study –Project Scope and components

SAP Project Scope

•Near real time Inventory reports –Catalog of 8 M articles, combination with

100 stores,(approx. 800 M information ,1M record per material/store)

•Total data volume 2.84 GB,100 users

•SAP Components: HANA-SLT-BI (Webi, Explorer)

•Hana - Two Boxes 256 GB (DEV) and 512GB(PROD), 120 GB DB


Inventory Scenarios -Before and after HANA

• Not having real time visibility into current stock situation

• Higher storage costs for stock

• Opportunity cost of lost sales

Business Pain Points before HANA

• Reduce the long processing times to analyze non-moving articles and discontinued articles.

• Reduce Large infrastructure maintenance costs (Heterogeneous landscapes with sometimes multiple Inventory systems per DC)

Advantages after HANA


Landscape at Customer-Before and after HANA

Customer Landscape before HANA

SAP ERP SAP BW[Optional]

BW StockContent

Customer Landscape after HANA

Inventory Master Data

SAP HANA

Stock AnalysisModels

SLT/ real-time

Master & Stock Data HANA-CA View/SQL Script

BO ReportsBO Reports

ECC BW

SAP ERP

Inventory Master Data

ECC


Lessons Learned – Project Management

Scope small and take phased roll out approach for fast and successful implementation

Adopt ideas as appear Adopt ideas as appear in implementation/testing phase rather than trying to obtain full proof solution in blueprinting phase

Often Data provisioning is most common underestimated task in the project plan

HANA Rev’s are released frequently, be prepared to put enough placeholders in the plan


Project Plan and Time line

Duration of the project : 30 weeks

Project Timeline Reference

Weeks

Project Preparation 2Business Blueprint 4Realization 18Final Preparation 4Go Live and Post Production Support

2


Resources

Resource Name

Project Manager

HANA,SLT, BI Inst / Maint Expert

HANA Modeler / SQL scripting Lead

HANA Modeler

BI 4.0 Architect & DeveloperQA HANA Deployment

MM Business consultant


28

Critical Areas-Most time spent

Modeling

Data transformation and provisioning

Performance Tuning


Lessons Learned – Data Compression in HANA

02468

1012

Compression Ratio

Compression Ratio

The best compression can be achieved in HANA if the data in source system is un compressed


Lessons Learned – Data Provisioning using SLT

Some extra attention needed for cluster table load (For Example partitioning, parallel load mode etc)

Source system information filtering capabilities are available from SP3 onwards (Before SP3 through an SAP note 1625755),so filter as much as you can in the source system)

Common configuration problemWhen creating a new schema in SLT system, you get the error message “Error when connecting to Source system” in the WebDynpro.Possible reasons

1.Missing add-on DMIS_2010 in your source system.Check and make sure that in your source system you have installed the required add-on DMIS_2010.(Please refer to SAP Note 1468391)

2. Missing proper roles for the RFC userIn the system log (SM21) and ABAP Check if role SAP_IUUC_REPL_REMOTE assigned to the RFCuser. Assign the role if it isn’t assigned yet


Lessons Learned – System Installation and Maintenance

Backup the log file ,archive it and clear it frequently, otherwise you will encounter out of memory problem as log files grow very quickly

Keep a system landscape diagram handy before starting installation or configuration

In the SLT system tune up the SLT batch processes, make enough free process available for replication

In the SLT system tune up the SLT batch processes, make enough free process available for replication

Take considerable time to do Sizing of the source(determination of the compression ratio),Sizing of the HANA box (For concurrent user and increase of the memory at runtime)


Lessons Learned – HANA Modeling

Query to Analytical view is faster than calculation view ,ideally keep CA view simple with minimal parameters and joins

Unions of Analytical views in Calculation views are faster than joining Analytical views

Model the business logic first then find the attributes, thus joining with minimal master data tables

HANA doesn’t have ABAP like statements, something to keep in mind for the modelers, only supported language is SQL script so

looping could be expensive

Push the filters in Attribute view level , putting the condition in where clause within HANA SQL script is very expensive in terms

of performance


Lessons Learned – BO Integration

Keep array size more (5000) in universe to prepare a bigger record set (set in the connection definition (CNX) file)

In order to benefit of HANA performance in calculations you should always try to retrieve the least quantity of data in your report for example aggregate data, not detailed information

Measures defined in the information model will be seen as dimensions in the information design tool (as any other field of the table). Make sure you turn them to measures


Lessons Learned – Authorization

Analytic privileges can be transported using import/export method and it needs to be done manually

SAP HANA Template roles are quite useful

CONTENT_ADMIN

MODELING

MONITORING

Do not change this roles, copy and change the name to use.

The number of invalid log on attempts can be reset using the following SQL command (useful to unlock locked users)

ALTER USER <USER_NAME> RESET CONNECT ATTEMPTS;


Lessons Learned – Performance Tuning

Best practice is to partition tables containing more than 2 B data to reduce the merge time and memory consumption

If there is a need to join tables on a calculated attribute, the workaround is to have an additional column in the table itself with the calculated value populated which is faster in terms of reporting as well

The SQL Parser will create a composite key for the fields joined which will in theory is more performance demanding than a filter. So avoid join tables on MANDT fields ,instead use filters.


36

Key Learnings

How Retailers can use the power of Hana to impact their Business

Retail Deployment Options

Case- Study, Lessons Learned in different areas of HANA implementation


Resources

How to get HANA now (29 Nov 2011)

http://en.sap.info/rds-hana-erp-implementation/61898

SAP HANA Adoption (Dec 13 2011) :

http://www.sap.com/corporate-en/press/newsroom/press.epx?pressid=18063


Questions

Thank You!

Debraj RoySolution Expert HANA Application I Customer Solution Adoption (CSA) SAP Labs I M +1 480 401 6600 I mailto:[email protected]


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khnc - retail deployment scenarios with customer case study

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