REVENUE AND SPEND INSIGHTS: ANALYZING GROSS-TO-NET PROFITABILITY USING SAP HANA

Transcription

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

3 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 3 Content 1 Executive Summary Challenges of Traditional Approaches to Analytics Infrastructure and Technological Constraints Increasing Complexity of the Gross-to-Net Equation Increasing Demand for Insights Revolutionary Solution for Today s Business Analytics Why in-memory is relevant for Analytics Revenue and Spend Insights Co-innovate at SAP Co-Innovation Lab Key components of the COIL test landscape Architecture of the COIL test landscape Hardware Reporting Example Scenario Requirements Outcomes 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

5 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 5 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. Figure 1: SAP Incentive Administration and SAP Paybacks and Chargebacks with traditional BW system 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 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 realtime 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 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.

8 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 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 Intel Xeon 8C Processor Model X7560

9 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM GB Memory, eight memory cards, each with eight DIMM sockets (64 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

10 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM Outcomes The timing of the report generation using SAP HANA and the traditional approach are shown in the table below. SAP HANA Less than 1 second (Runtime is measured for this specific scenario, no general statement is made for all analytical scenarios.) Directly on line item level, no pre-calculated data aggregation levels required No limit on drill-downs and details Data immediately available for reporting, no waiting on data load processes to data warehouse SAP Business Warehouse Several minutes (A tradition Data Warehouse would take several manual steps to achieve the same results as SAP HANA and would typically take several minutes.) Pre-calculated data aggregation levels Processing time for next navigation step depends on whether aggregate exists Parallel drill-down to multiple dimensions may 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)

11 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 11 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.

12 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 12 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

13 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 13 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.

14 An SAP HANA CO-INNOVATION STORY WITH VISTEX AND IBM 14 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. Copyright/Trademark

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