Revenue and Spend Insights%V3a Analyzing Gross-To-Net Profitability Using SAP HANA

8/12/2019 Revenue and Spend Insights%V3a Analyzing Gross-To-Net Profitability Using SAP HANA

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White Paper

SAP Co-Innovation Lab

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


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An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 3

Content1 Executive Summary ....................................................................................................................................... 4

2 Challenges of Traditional Approaches to Analytics .................................................................................. 42.1 Infrastructure and Technological Constraints ...................................................................................................42.2 Increasing Complexity of the Gross-to-Net Equation .......................................................................................52.3 Increasing Demand for Insights ........................................................................................................................5

3 Revolutionary Solution for Todays Business Analytics ........................................................................... 53.1 Why in-memory is relevant for Analytics .......................................... .................................................. ...............63.2 Revenue and Spend Insights ............................................................................................................................6

4 Co-innovate at SAP Co-Innovation Lab ....................................................................................................... 74.1 Key components of the COIL test landscape ...................................................................................................74.2 Architecture of the COIL test landscape .......................................... .................................................. ...............74.3 Hardware ...........................................................................................................................................................8

5 Reporting Example ........................................................................................................................................ 9

5.1 Scenario ............................................................................................................................................................95.2 Requirements ....................................................................................................................................................95.3 Outcomes ....................................................................................................................................................... 10

6 Conclusion.................................................................................................................................................... 14


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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 foranalytical 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.

Figure 1: SAP Incentive Administration and SAP Paybacks and Chargebacks with traditional BW system

3 Revolutionary Solution for Todays Business Analytics

Todays solution to analytical reporting needs to be a fundamental leap ahead of traditional approaches.

CHALLENGES

Standard Reporting

Reports draw fromoperational 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


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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. HANAs 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.

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 allagreements 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

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 reportingdimensions


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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 bydriving open innovation projects and initiatives to extend SAPs solution coverage and enhance our solution

infrastructure efficiency. Both Vistex and IBM are members of the SAP Co-innovaiton Lab and leveraged the

labs 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 SAPs 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 X5Machine 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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5.3 Outcomes

The timing of the report generation using SAP HANA and the traditional approach are shown in the table

below.

SAP HANALess than 1 second(Runtime is measured for this specific scenario, no general

statement is made for all analytical scenarios.)

SAP Business WarehouseSeveral minutes(A tradition Data Warehouse would take several manual

steps to achieve the same results as SAP HANA and would

typically take several minutes.)

Directly on line item level, no pre-calculateddata aggregation levels required

No limiton drill-downs and details Data immediately available for reporting,no waiting on data load processes to data

warehouse

Pre-calculated data aggregation levels Processing time for next navigation step

depends on whether aggregate exists Parallel drill-down to multiple dimensionsmay not be possible anymore

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 needto 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 companys 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 ofopportunities to pursue, such as underperforming channels or below-forecast sales to certain customers or of

particular products.


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Revenue and Spend Insights%V3a Analyzing Gross-To-Net Profitability Using SAP HANA

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