Case Study: Lessons from Animal Health International's SAP NetWeaver BW to SAP HANA Migration...

© Copyright 2013Wellesley Information Services, Inc.

All rights reserved.

Case Study: Lessons from Animal Health International's SAP NetWeaver BW to SAP HANA Migration Project

Tim Hays Dr. BergAnimal Health International, Inc. Comerit Inc.

2

What We’ll Cover …

Introduction to Animal Health International Animal Health International goes to HANA Animal Health International’s vision for Customer and Inventory

Stratification on HANA Defining KPIs Identifying Data Designing the Solution Wrap-Up

3

Animal Health International

Animal Health International, headquartered in Greeley, Colorado, is the premier animal health company and a leading distributor of production and companion animal health products

85 locations; Over 500 sales representatives

4

Animal Health International

• 80,000 Customers Large and Small Animal Veterinarians, Producers and Retailers Companion Animal, Equine, Beef, Dairy, Poultry and Swine

• 85,000 SKUs sourced from over 2,000 Manufacturers Pharmaceuticals, vaccines, diagnostics, equipment, software,

pet foods, devices and other animal health supplies• Call Centers, Distribution Centers, Retail Stores, Mobile Stores

(Trucks), B2B E-Commerce

5




6

2013 Infrastructure before HANA

2013 Landscape before SAP HANA• SAP ERP 6.0 EhP6 • SAP BW 7.3• Virtualized on HP-UX, DB2 v9.7

7

Next generation SAP Real-time Data Platform

8

Installation of SAP Hana

June 2013 – Go-Life with BW 7.30 on Hana Actual migration of environment took 5 weeks Prepping the BW on DB2 system took 3 month Rolled out SAP HANA Studio and Data services

9

BW 7.3 on HANA Landscape

10

Reducing the layers and complexity

SAP HANARow/Column store

OLAPOLTP

Structured Unstructured

Real-time Replication Batch ETL/ELT

Planning Predictive/Statistical

Application Services UI Development tools

11




12

AHI’s Stratification Vision

Customer Stratification:

Method of stratifying customers into key groups to improve process and relationships.

Example: Silver, Gold, Platinum customer status (hotel, airline industry)

13

AHI’s Stratification Vision

Common Practice

1. No defined customer stratification 2. Customer groups based on market type or product line3. Top customers based on revenue or gross profit

14

AHI’s Stratification Vision (cont.)

Good PracticeBased on more than one factor

1. Volume [sales $] based2. Gross margin3. Business potential4. Includes lagging and leading

indicators

15


Best PracticeBased on many factors

1. Business potential2. Profitability3. Customer loyalty 4. Cost to serve

16


The vision is defined by the questions being asked

CEOHow can I increase Profit Company wide ?

Salesrep.

Which Customer should I pursue?

Salesmanager

Which Customer

s are most

profitable?

Plant manage

r

How much

should I purchase? When?

Marketing

manager

How can I increase

brand loyalty?

17

AHI’s Stratification Vision on HANA

Why Hana ?1. The first time we are able to analyze the

necessary data volume needed for stratification (e.g. 700.000 records daily for stock data married up with Copa data)

2. Gives us the flexibility to have near real-time reporting if necessary

3. Reduced reporting response time by over 30 % allowing us to include larger user community

18




19

Lagging and Leading Indicators

Important indicatorsYou decide which ones are most importantto you and concentrate on those

Customer Loyalty

Buying Power

Cost to Serve

Customer Rebates

Profitability

20

Customer Ranking

define your ranking criteria

Status Rank % of CustomersElite Customer A 8%

Premium Customer B 20%

Core Customer C 44%

Base Customer D 20%

Casual Customer E 8%

21




22

SAP Data Sources

MM

ERP Data

SD

3rd Party

Government, Social

CRM

COPA

FinancialBPC

Initiate a data reconnaissance effort • Data you have in ERP but not yet available in Data Warehouse• Data ready to report on in Data warehouse (might need some

remodeling)• Data coming from entirely external data sources (in our case

governmental websites containing Agriculture statistics)

23

Opportunity Data

find data relevant to you

24

Data Challenges

• Multiple sources of Internal data More than one ERP system More than one CRM platform

• Data is not collected congruently Rebates, chargebacks, net-downs impact sales revenue and

profitability calculations• 3rd party data is hard to correlate

No common key between internal and external data• Dataset is too large to process with available tools

85,000 products * 80,000 customers * 400 plants

25

It Starts with the Data

Data

Analytics

Stratification Views

26

Stratification Views

DataInventory

COPAExternal Statistics

Customer ranking

view

Inventory Ranking

view

Profitability View

Sales Penetratio

n view

Inventory, Copa and external statistics data are the building blocks of AHI’s Stratification solution

Based on this data we have Customer ranking reports, Inventory ranking reports, Sales Penetration reports and Profitability reports

27




28

Example: AHI’s Inventory Stratification

Inventory Stratification:

Method of stratifying inventory (items/SKUs) into high and low performing groups.

Example: A B C D

29

Inventory Stratification

Sales Revenue

Hits

GMRIO

Average Inventory____________________________________

Gross Margin

Count of Order Lines

Sales as % of Total Sales

30

Inventory Ranking

define your ranking criteria

Rank % of TotalA 60%

B 20%

C 10%

D 10%

31


Stage Inventory snapshot data for amount and value on hand per plant

32

Inventory Stratification (cont.)

COPA data staged

33

Inventory Stratification (cont.)

Combine Inventory Snap Shot with COPA data

InventorySnapshot

COPA Invoice

level

Inventory Stratification Reporting Level

34

Customer Stratification

Apply algorithms to basic data and populate Stratification staging DSO

35


• Inventory Stratification Demo Using HANA

36

Business Adoption

• Education Sessions for Business and IT

• Executive sponsorship This cannot be an IT or Marketing project, it must have high-

level support• Create a cross-functional project team

Marketing IT Sales

• Tie to incentive programs• Make it a game

37

Customer Stratification

Predictive Analytics

HANA R – K MeansHANA R – Decision Trees

38




39

7 Key Points to Take Home

• Stratification is critical to understanding customer and inventory performance over time

• Customer and inventory stratification methodology will require business adoption to be useful

• Available data will determine starting point for ranking criteria• Acquiring quality data can be a challenge• Ranking customers and inventory with limited data set is better

than no ranking at all• Stratification can be a big data problem requiring tools like HANA• Business adoption will require education and an implementation

plan

40

Where to Find More Information

• www.naw.org/publications/pubs_item_view.php?pubs_itemid=145 Customer Stratification Overview

• http://id.tamu.edu/intensive-customer-stratification-program Texas A&M University Education

• http://events.sap.com/sapphirenow/en/session/2371 Gray Bar utilizing HANA for Customer Stratification

• www.sap.com/asset/index.epx?id=aaff7f67-7400-490e-a800-ad08f4f1eb09 SAP Customer Value Intelligence Overview

41

Your Turn!

How to contact us:Tim Hays

[email protected]

Dr. [email protected]

mailto:[email protected]

mailto:[email protected]

42

Disclaimer

SAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet®, PartnerEdge, and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and in several other countries all over the world. All other product and service names mentioned are the trademarks of their respective companies. Wellesley Information Services is neither owned nor controlled by SAP.

Case Study: Lessons from Animal Health International's SAP NetWeaver BW to SAP HANA Migration...

Documents

Transcript of Case Study: Lessons from Animal Health International's SAP NetWeaver BW to SAP HANA Migration...