This document summarizes a project between Vistex, SAP, and IBM to use SAP HANA for real-time profitability analytics. The traditional approach of replicating operational data to data warehouses for reporting was slow. With SAP HANA, a proof of concept showed gross-to-net profitability reports could be generated in under one second using 20 million transaction records, versus the minutes required traditionally. The in-memory capabilities of SAP HANA enabled fast, detailed analysis without pre-aggregation.

1. REVENUE AND SPEND INSIGHTS: ANALYZING
GROSS-TO-NET PROFITABILITY USING SAP® HANA
A CO-INNOVATION STORY WITH VISTEX AND IBM
Editors
Varma Datla, Vistex
Matthew Hays, Vistex
Catherine Moran, SAP
Kevin Liu, SAP
March 2012
Version 1.0
White Paper
SAP Co-Innovation Lab

2. An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 2
Acknowledgements
This document is the work of a virtual project team at SAP Co-innovation Lab, whose members have
included:
Christopher Y Chung (IBM), David Cruickshank (SAP), Varma Datla (Vistex), Matthew Hays (Vistex),
Kevin Liu (SAP), Magnus Meier (SAP), Siva Gopal Modadugula (SAP), Catherine Moran (SAP), Rebecca
Newell (SAP), Hans-Joachim Odlozinski (SAP), Juergen Schmerder (SAP), Sanjay Shah (Vistex), Goran
Stoiljkovski (SAP), Wilson Ramos (SAP), Tag Robertson (IBM), and many colleagues from IBM, SAP, and
Vistex who helped with the project

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Content
1 Executive Summary ....................................................................................................................................... 4
2 Challenges of Traditional Approaches to Analytics .................................................................................. 4
2.1 Infrastructure and Technological Constraints ...................................................................................................4
2.2 Increasing Complexity of the Gross-to-Net Equation .......................................................................................5
2.3 Increasing Demand for Insights ........................................................................................................................5
3 Revolutionary Solution for Today’s Business Analytics ........................................................................... 5
3.1 Why in-memory is relevant for Analytics ...........................................................................................................6
3.2 Revenue and Spend Insights ............................................................................................................................6
4 Co-innovate at SAP Co-Innovation Lab ....................................................................................................... 7
4.1 Key components of the COIL test landscape ...................................................................................................7
4.2 Architecture of the COIL test landscape ...........................................................................................................7
4.3 Hardware...........................................................................................................................................................8
5 Reporting Example ........................................................................................................................................ 9
5.1 Scenario ............................................................................................................................................................9
5.2 Requirements ....................................................................................................................................................9
5.3 Outcomes ....................................................................................................................................................... 10
6 Conclusion.................................................................................................................................................... 14

4. An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 4
1 Executive Summary
SAP Incentive Administration and SAP Paybacks and Chargebacks by Vistex are applications that extend
SAP® ERP and SAP CRM software functionality. These applications provide companies with an embedded,
fully-integrated solution for handling discount pricing, marketing fund claims, sales commissions & broker
fees, in- & outbound royalty payments, and volume- & growth-based sales or purchasing rebates.
With visibility into costs, pricing and incentives, SAP Incentive Administration and SAP Paybacks and
Chargebacks can analyze the gross-to-net profitability of sales in complex channels involving any number of
intermediary channel partners. The solutions define the eligible products, channels, customers, pricing and
incentives in each sales agreement. In many cases, multiple agreements may be involved in selling a product
to the end-customer since there is typically an agreement between each intermediary partner in the supply
chain.
These applications are able to consolidate a view of the multiple agreements, incentives and transactions,
providing insight into revenue and spend. Operationally, the system needs to process the large influx of daily
transactions, and analytical reporting consumes a significant portion of system resources.
SAP® HANA™ technology takes these solutions to the next level by enabling real-time insight into revenue
and spend. First, the solution lowers data demands on the operational database used for transaction
processing. Second, the solution virtually eliminates the data latency that is inherent in replicated data marts
using traditional database technologies. Third, the solution accelerates analytical processing to provide
insight on larger data sets (including Big Data) at faster speeds than traditional analytical tools.
SAP Co-innovation Lab (COIL) global network is designed for driving open innovation projects and
initiatives to extend SAP’s solution coverage and enhance our solution infrastructure efficiency with partners.
Vistex partnered with SAP and IBM in the SAP Co-Innovation Lab to develop a solution to provide real-time
profitability analytics while reducing the overall impact on transactional processing and other business
operations. As part of the project, an operational system was created with millions of lines of transactional
data reflecting a large size (multi-billion dollar revenue) company, and reports analyzing revenue and spend
were defined. This environment was used to evaluate the speed at which the analytical data set could be
updated with new and changed data, as well as the time necessary to analyze and report revenue and spend
data using multiple transaction types.
SAP HANA was used to reduce typical analytical report generation from several minutes to less than one
second, while maintaining an up-to-date analytics data source that is updated within milliseconds of data
changes in the operational system.
2 Challenges of Traditional Approaches to Analytics
Analyzing gross-to-net profitability—and business analytics in general—can pose challenges technically and
operationally. The root-cause of these challenges can be found in several areas.
2.1 Infrastructure and Technological Constraints

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Companies have long relied on operational data that has been replicated to data marts for reporting purposes.
Reporting is system resource-intensive, and transaction processing cannot be subject to delays. Immediately,
it becomes apparent from this model that the transaction processing system is not the ideal system to use for
analytical reporting. Replicating operational data into a data mart is not real time, and frequent refreshing of
this data is necessary to provide a current view of operational data.
2.2 Increasing Complexity of the Gross-to-Net Equation
Due to the growing number of business partners a particular customer can have, the number of agreements,
variance in models, and the volume of transaction data, the complexity of gross-to-net analyses increases. As
a result, analyses performed on revenue and spend data require new levels of sophistication.
2.3 Increasing Demand for Insights
As the pace of change in the business climate accelerates due to economic and social factors, the time to
analyze business conditions and react to evolving situations shrinks. Information analysts are asked to
perform increasingly sophisticated analyses at ever faster speeds to provide the revenue and spend insights
that enable their businesses to compete and win.
CHALLENGES
Standard Reporting
• Reports draw from
operational system
• Analytics impact business
Optional Reporting
• Typically once-a-day
update of data
• Data may be aggregated
to improve analytic speed
Limited Insight
• Aged data yields equally
aged analysis
• Analysis may be limited
by data aggregation
Figure 1: SAP Incentive Administration and SAP Paybacks and Chargebacks with traditional BW system
3 Revolutionary Solution for Today’s Business Analytics
Today’s solution to analytical reporting needs to be a fundamental leap ahead of traditional approaches.

6. An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 6
SAP HANA technology provides a secondary data source for business analytics that can be updated in real-
time without significantly impacting the primary data source, used for business operations. SAP Landscape
Transformation replication software is used to replicate new and changed data almost instantaneously,
keeping the analytical data source current. HANA’s in-memory structure eliminates input/output contention
to physical storage and accelerates the analysis of large data sets.
3.1 Why in-memory is relevant for Analytics
HANA provides the capacity and speed to sift through detailed data without aggregation, so the analytical
results can be drilled into for deeper insight. It has the ability to query and analyze very large data sets to
perform intensive tasks such as consolidated, multi-year line-item and lifetime analysis of revenue by product
or customer. HANA enables instant access to relevant decision information in a user-initiated or automated
fashion.
BENEFITS
Improve Reporting
• Business user-driven data
analysis
• Instant response times
Eliminate Boundaries
• No pre-defined data
aggregation levels
• Complete lifetime, line-item
analysis
Gain Deeper Insight
• Big Data and ad hoc queries
• No limitations on reporting
dimensions
Figure 2: SAP Incentive Administration and SAP Paybacks and Chargebacks with HANA as secondary database
3.2 Revenue and Spend Insights
Gross-to-net analyses are especially difficult to perform using traditional analytical technologies because it
involves a significant amount of data investigation, calculation and summarization. This extensive analysis
requires all of the financial transactions related to the sale of products. These transactions are spread
throughout the sales channel, involving any number of channel partners, the end customer, and all
agreements to which the transaction is governed. The relevant transactions must be found, linked, computed
and summarized for presentation.
Furthermore, valuable information can be found by drilling into data to search for specific customers or
products, make comparisons, and find patterns or discrepancies. The reporting mechanism must maintain the
ability to drill into high-level reports through a number of data dimensions. This requires the ability to query

7. An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 7
and manipulate very large data sets quickly, since these actions are performed in real-time by the business
analyst.
With its advanced analytical capabilities and extreme speed, HANA performs the complex queries,
calculations and analysis in a single user request, delivering results quickly while maintaining the ability to
drill into the summarized data to see rich detail with equal speed.
Performing gross-to-net analyses on real-time transaction data allows businesses to determine profitability
instantly and react to changes in manufacturing cost, trade spend, and other expenses quickly. Faster
detection of changes in profitability allows businesses to adjust pricing as soon as possible to limit the impact
of cost changes on profitability.
4 Co-innovate at SAP Co-Innovation Lab
SAP Co-innovation Lab (COIL) is a global lab network that is designed to bring value to our customers by
driving open innovation projects and initiatives to extend SAP’s solution coverage and enhance our solution
infrastructure efficiency. Both Vistex and IBM are members of the SAP Co-innovaiton Lab and leveraged the
lab‘s project enablement platform to conduct a HANA Proof-of-Concept (PoC) to determine the feasibility
and potential performance improvement of in-memory processing using HANA technology in data-intensive
applications commonly encountered by Vistex solutions. In particularly, the PoC checked the feasibility of
putting transactional data (obtained from IP Docs) in-memory to support near-real-time operational reporting.
4.1 Key components of the COIL test landscape
The following SAP and partner components were deployed at COIL to support this PoC:
- ECC 6 IDES with ehp5 with IBM DB2 and SLES
o Vistex Solution Extension for ECC
- BI Platform 4.0 on Windows 2008 64 bit and SQL server, with Advanced Analysis OLAP and MS
Office
- HANA 1.0 with SLT
4.2 Architecture of the COIL test landscape
SAP HANA provides a secondary data source dedicated to analytical reporting. This data source is updated
in real-time by SAP’s Landscape Transformation replication software. The implementation of SAP HANA
does not disturb the original architecture of SAP ECC and Vistex.
The architectural diagram for the SAP Co-Innovation Lab set-up is shown below.

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4.3 Hardware
SAP HANA software requires specialized hardware to provide the memory storage and memory access
bandwidth. IBM provided the hardware necessary to install SAP HANA software in the SAP Co-Innovation
Lab. The specifications for the hardware are listed below.
IBM System x3850 X5
Machine Type: 7145
Number of processor: 4, Intel Xeon Nahalem EX, 8C
Memory: 512GB / Hard drives: 8 X 300GB
High IOPS SSD: 1 X 320GB
More details for IBM System x3850 X5
4 socket @ Intel Xeon 8C Processor Model X7560

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512 GB Memory, eight memory cards, each with eight DIMM sockets (64 DIMM @ 8GB/DIMM).
2.4 TB SAS per chassis (using eight 300 GB 2.5" SAS HDDs).
320 GB High IOPS SLC DUO Adapter for IBM System x
5 Reporting Example
The following setup was used in the SAP Co-Innovation Lab environment to simulate the transactions that
would be used in a typical revenue and spend analysis report. This volume of data represents the annual
transaction data for a small enterprise or the monthly transaction data for a medium-to-large enterprise.
5.1 Scenario
• 5 Million Billing line items
• 5 Million Sales rebates items
• 5 Million Sales Incentive items
• 5 Million Chargeback items
5.2 Requirements
To test the ability to perform typical analyses, the following capabilities needed to be performed to ensure
robust utility.
• Report on product- or customer-related profitability
• Drill down to customer rebates
• Sales incentives
• Chargebacks for a single company

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5.3 Outcomes
The timing of the report generation using SAP HANA and the traditional approach are shown in the table
below.
SAP HANA SAP Business Warehouse
Less than 1 second Several minutes
(Runtime is measured for this specific scenario, no general (A tradition Data Warehouse would take several manual
statement is made for all analytical scenarios.) steps to achieve the same results as SAP HANA and would
typically take several minutes.)
• Directly on line item level, no pre-calculated • Pre-calculated data aggregation levels
data aggregation levels required • Processing time for next navigation step
• No limit on drill-downs and details depends on whether aggregate exists
• Data immediately available for reporting, • Parallel drill-down to multiple dimensions
no waiting on data load processes to data may not be possible anymore
warehouse
The gross-to-net analysis report was generated in a fraction of a second using SAP HANA. This is
significantly faster than the minutes required to produce the same report using traditional technologies.
Figure 3: The Gross-to-Net Analysis for a Company (all customers, all materials)

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In addition to the superior speed of analytics, the detailed data is still retained and is available to drill deeper
into the analysis. Drilling into the report is equally as fast as the initial report generation, so there is no need
to sacrifice details for speed or vice versa.
Figure 4: The same Gross-to-Net Analysis demonstrating the ability to drill into details (one customer, each material)
The same analytical capabilities demonstrated for Gross-to-Net analysis can also be applied to particular
components of the profitability analysis. The following reports demonstrate drilling into the profit
components for Sales Incentives and Sales Rebates.

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Figure 5: Drilling into the Sales Incentives component of the Gross-to-Net Report
Figure 6: Drilling into the Sales Rebates component of the Gross-to-Net Report

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The demonstrated speed of analysis and delivery of the report enables the use of mobile devices for
requesting and reviewing revenue and spend data.
Figure 7: Gross-to-Net Analysis delivered to a mobile device (Apple iPad shown)
Analyses can be filtered on several dimensions and components of the analyses can be drilled into as shown
below.

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Figure 8: The Sales Incentive component of the Gross-to-Net report is filtered on fiscal period (Apple iPad shown)
6 Conclusion
Although each company’s transaction data will vary and its approach to revenue and spend analyses will
differ, the scenario demonstrated by Vistex in the SAP Co-Innovation Lab proved that significant
improvements in analytical capabilities can be achieved without sacrificing delivery speed or data latency.
SAP HANA enables Vistex to provide revenue and spend insights faster than traditional technologies. The
data used in the analyses performed by SAP HANA can be obtained in real-time without impacting other
business processing operations. The business analyst can drill into the delivered results with equal speed to
find insights that are masked by summarization. The full data record from the original source can be
available in SAP HANA and displayed in the report without impacting performance.
The benefits of this speed and capability are obvious to the business user requesting and viewing the analysis.
The benefits to the business itself are realized by the faster detection of challenges to be overcome, such as
increased expenses in cost to manufacture or trade spend affecting profitability; and quicker discovery of
opportunities to pursue, such as underperforming channels or below-forecast sales to certain customers or of
particular products.
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