Automating FP&A Analytics Using SAP Visual Intelligence and Predictive Analysis
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1 September 9 11, 2013 Anaheim, California Automating FP&A Analytics Using SAP Visual Intelligence and Predictive Analysis Varun Kumar
2 Learning Points Create management insight tool using SAP Visual Intelligence Develop visualizations that facilitate decision making and analysis P&L forecasting using SAP Predictive Analysis
3 Agenda Business Context FP&A Margin Analytics Linking Analytics to Value Creation Developing visualizations for decision making and analysis Data progression Waterfall charts Scatter plots and Bubble Charts Box and Whiskers Plot & Stacked charts Management Insight Tool :-Automation Approach Short term vs. Long Term Mathematical Forecasting using historical trends Forecasting using Multiple Linear Regression and ARIMA Steps in SAP Predictive Analytics DEMO Wrap-up
4 Business Context Management Insight Tool Automation of P&L Forecasting Different systems for business consolidation, planning and forecasting and financial reporting. The Data required for analysis is at a lot of different places and its highly detailed. Desire for more commentary around drivers of margins and variance with focus on what numbers mean vs. what they are. Fields forecasting process takes significant time and effort to update after the financials close. Business Units use different standards to update forecasting in the planning and forecasting application. Provide mathematical baseline to increase accuracy of the field forecasting function.
5 Linking analytics to value creation Key Metrics for Margin Analysis Value Creation Growth Margin Net Sales Gross Profit Operating Leverage Cases (volume) Rate ($/Case) Rate ($/Case) Cause of Change Expenses from Ops Corporate expenses
6 Developing displays that facilitates decision making and analysis Seq. Key Business Questions : - Margin Analysis Metrics 1. Are Sales / Volumes / Margins Growing? Volume, Rate of change. Visualizations Data progression and possible patterns, 12 months trend chart 2. What is driving the growth/change in Sales/ Volumes/ Margins compared to Last year, Plan, forecasts? 3. Are we realizing higher margins compared to Plan, LY and forecasts? 4. What aspects of our operations are contributing to improving/lowering margins? 5. Are we meeting our customers expectations across distribution centers? 6. Are expenses reducing? Are we making progress in leveraging our scale? Change by customers, products % change, point change Cause of Change Service level %, volume & sales Expenses, TY, LY, Plan, Forecasts Waterfall Charts, TY vs. LY, TY vs. Plan, TY vs. forecast Vertical bar chart, horizontal bar chart Percentage of parts or as ratios to a whole represented using a pie chart. Waterfall Chart, cause of change analysis cost, price, Volume, mix calculations Box and whiskers Plot Vertical bar chart, horizontal bar chart, bar chart with two y axis, Stacked Column charts and 12 months trend charts 7. How is our portfolio doing in terms of delivering profitable growth? Are we Growing at the expense of pricing? Are we growing with the customer we have? % Change, point change, ratios Scatter Plots/ Bubble Charts Operating income Point change vs. Net Sales Growth Scatter Plots/ Bubble Charts Gross Margin Point change vs. Net Sales Growth Scatter Plots/Bubble Charts New Lost business Ratio vs. growth from penetration.
7 Data Progression, Trends Are sales, margins & volumes growing? Are we realizing higher margins compared to Last year, Plan, forecasts?
8 Waterfall Charts & CVP Analysis What is driving this growth/change in volume, sales, margins compared to Last year, Plan, forecasts? What aspects of our operations (cost basis, pricing, volume, mix change etc.) are contributing to improving/lowering margins? Simple Waterfall Chart Complex Waterfall Chart
9 Scatter Plots and Bubble Charts Are we growing profitably? Are we growing at the expense of pricing? Are we growing with the customers we have? How is our portfolio doing for profitable Growth?
10 Box and Whiskers Plots & Stacked Columns Charts Are we meeting our customers expectations across distribution centers? Are expenses growing? Are we making progress in leveraging our scale?
11 Management insight Tool : Automation Approach Short Term solution :- streamline data gathering to feed into reporting process Longer term solution :- pull data from transaction systems and warehouse Set up retrieves to streamline data gathering from readily accessible systems. Identify alternative approaches to pull data for a more automated solution Identify alternative approaches to access data not in current systems (e.g. ed spreadsheets, csv files, freehand SQL) Online Report generation and delivery with click thru capabilities and collaboration options. Scope of Today s Discussion Create consolidated database/spreadsheets to drive reporting and visualizations Identify alternative warehousing systems/architecture to support solution development and deployment.
12 Short Term Solution Cont. Gain a broader understanding of the various source system and requirements for data munging & custom calculations. All financial reporting systems have an interface to Excel, to run retrieves. Use freehand SQL or CSV files for various database sources that do not support retrieves.
13 Short Term approach detailed Source Data files Consolidated reporting backup SAP Predictive Analytics Financial Consolidation Retrieves Identify granularity of data needed for each metric Financial Reporting Data Mart Planning & Forecasting Others Retrieves Retrieves Flat Files Spreadsheets organized for reporting Automatically refreshes when new retrieves are run Generated and distributed manually Investigate sources for each metric at the desired level of granularity Estimate effort needed to streamline gathering of data available in readily accessible systems Catalogue data not available in readily accessible systems for further investigation
14 Automation of P&L Forecasting Mathematical Forecasting using historical trends Forecasting using Multiple Linear Regression and ARIMA Steps in SAP Predictive Analytics DEMO
15 Mathematical Forecasting using Historical trends Native algorithms and desktop R algorithms can be used. Causal Forecasting Time Series Field Forecast P&L Forecasting solution works with limited data volumes and hence usage of HANA PAL algorithms or HANA integration with R may not be required. Multiple Linear Regression Desktop R algorithms Moving Average Exponential Smoothing Native SAP-PA Algorithm ARIMA R Desktop Customization Planning and Forecasting System Native algorithms: which logic is implemented natively in PA's core. Desktop R algorithms: implemented with R scripts that are run against a local (desktop) R installation
16 math models to predict performance Goal Drivers Forecast Approach Create base mathematic model to predict the future trend of Sales and P&L with minimal variance External variables: Inflation (PPI) Internal variables (input and output): Key Assumptions Input drivers: cases sold, margin compression, expenses per case Output variables: Sales ($), Net COGS ($) and net Opex ($) Exclude impact of Strategic initiatives Trend model based on 5 years of historical data Input Drivers Cases Sold Inflation (PPI) Inflation Pass Thru Expenses Per Case Output Variables Sales ($) Net COGS ($) Net OPEX ($) 16
17 math models to predict performance Methodology Calculations Time Series (ARIMA) Predict future values of input drivers base on observed value to describe trend, seasonality and randomness Volume forecast Multiple Linear Regression To describe the change of dependent variables to the change of independent variables Input Variables (Cases Sold, Inflation Pass-thru, Expenses per Case i.e. the relationship to sales to cogs, price to cogs Inflation (PPI index) : USDA Food Inflation forecast Net COGS = Volume + PPI Index Sales = COGS + Impact of margin compression (inflation pass thru) Margin = Sales Net COGS Expense = Volume + Expense per case
18 Steps in SAP Predictive Analytics Acquire Corporate data Perform Time Series Calculations (ARIMA, Exp. Smoothing) Synthesize and transform Apply Multiple Liner Regression Build Visualizations
19 Demo
20 Recap of SAP Predictive Analysis features Pros Functionality for drawing Waterfall charts and box and whiskers plots is delivered right out of the box Cons One cannot create a dashboard that would show a trend chart and a Scatterplot or waterfall chart on the same page. Leverages investments in SAP Universes and HANA, Excel calculations can be pushed backwards. End user Interaction with the visuals and collaboration Starting SAP Predictive Analysis , it is possible to embed custom R-Script as new components. It is not possible to save the visualizations created in Predict panel in the Visualize pane.
21 Next steps for SAP predictive Analytics connected to SAP Backend. Identify customers likely to decrease spend and prescribe interventions Address early warning signs of decline and intervene Surveys Insights Key Question Identify customers who need sales force attention Transactions Events Salesforce Operational System Alert when changes in weekly sales indicate a statistically unusual pattern Overlay other metrics or aggregate to higher levels
22 Key learnings How to create management insight tools using SAP Visual Intelligence How to develop visuals that facilitate decision making and analysis How to perform P&L forecasting using SAP Predictive Analytics
23 Thank you for participating. Please provide feedback on this session by completing a short survey via the event mobile application. SESSION CODE: 1105 Learn more year-round at
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