statistical sales forecasting using sap bpc
TRANSCRIPT
Statistical Sales Forecasting using SAP BPC
Capgemini’s unique statistical sales forecasting solution integrated with SAP BPC 10.0
helps global fortune 1000 company built robust & accurate sales forecasting model
1. Executive Summary
Budgeting, Planning & Forecasting is not new to companies. A company
must have a robust & effective process to avoid any mismatch in strategic
initiatives, capital allocation, inventory management, revenue guidance
etc. But even though companies engage in different types of planning for
sales, operations, expenses, HR, finance etcm all of these forecasting
processes rely on historical data to come up with forecast numbers,
assuming external factors don’t change. However, there are many
limitations in this approach. First and foremost being the assumption that
“external factors do not change”.
In today’s global business environment, external factors play a key role,
and planners need to consider the impact of external factors while
generating forecast numbers. In this article, we discuss:
1. A new approach to sales forecasting – the “external market-driven
statistical sales forecasting solution”.
2. The reason why we believe this statistical sales forecasting solution
the most accurate and ideal way of forecasting.
In addition, we will explain how this model can be incorporated with
existing ERP infrastructure (SAP BPC), and we discuss the business benefits
of implementing such a solution.
In Summary,
Capgemini’s unique
statistical sales
forecasting method
brings following
business benefits
Accurate sales
prediction
Better Inventory
management
Better capital
allocation
Better insights about
factors influencing
sales
Accurate earning
guidance
Reduced budgeting &
forecasting cycle
time
Integrated with
other planning
models
2. Challenge
Our client is a Fortune 1000 company which has a global presence across
multiple end-market industries. The Client’s current sales forecasting process
was based on a large number of offline spreadsheets. Planners used to
download the actual data from SAP and manipulate the forecast numbers on a
case-by-case basis, often projecting an average of historical data. Multiple
iterations were done to error out any discrepancies. The whole process was
time consuming, error prone and required a lot of manual effort.
Keeping the above problem in sight, Client wanted to implement a robust
product & region-level sales forecast model based on external factors which
are likely to influence sales for a particular region or a particular product
family. In addition, client wanted see the correlation between the external
factors and which factor(s) has the most influence for a particular product in a
particular region. Finally, client wanted to automate the whole process by
integrating it with existing ERP.
3. Solution
Keeping above requirements in our sights, we developed a BPC-based
statistical sales forecasting solution. The solution considers external factors
and uses multivariable regression analysis to correlate these external factors.
Based on the regression formula, sales forecast numbers are generated. The
whole process is implemented in SAP BPC NW 10.0 and integrated with
existing SAP ERP system. Multiple reports and dashboards with drill down
functionality were developed to have better insights.
Figure 1: Solution Mapping
Having talked about how we addressed each of the requirements, let’s talk
about the solution in detail.
4. What is Statistical Sales
Forecasting? The statistical forecasting solution is different from the traditional forecasting
approach which uses historical data for forecasting. In addition to historical data, the
statistical forecasting approach uses external economic indicators like GDP, total
industry output, disposable income, etc to predict future sales. Using this statistical
method for predicting sales not only gives an accurate prediction but also gives
insights to business about what trends can help them increase their sales in that
particular region.
5. How it is Implemented? Capgemini has developed a unique and proprietary method for implementing this
solution. As shown below, the methodology consists of 4 steps:
Figure 2: Implementation Process
Understanding Requirement The first step is to interview the stakeholders to understand the business requirements
Research Economic Indicators The second step is to analyze the economic indicators. Capgemini assists clients in selecting the data that is the right fit for their business model and the forecast accuracy needs
Run Multivariable Regressions Statistical regression analysis is the exercise of analyzing the fit of a time series of dependent (sales) and independent (economic indicator) variables to a linear historical pattern. Regression analysis can measure the correlation between a dependant variable (sales) w.r.t to a number of independent variables (economic factors).
Capgemini’s unique
methodology helps
business to build
robust business
model integrated
with existing SAP
Infrastructure
For instance, in below example (sales) is dependant variable while X1, X2 etc are independent variables (economic indicators).
Figure 3: Regression
The fit of an equation can be measured using adjusted R-squared (R2). R2
provides a measure of how well observed outcomes fit to a multivariable regression formula, and it is equal to the proportion of total variation of outcomes (residuals) which are explained by the regression formulas vs. taking a simple average of the historical data. Between two regression equations with the same dependent variable and different independent variables, the equation with higher Adjusted R-Squared has a better fit. However, R2
in itself is not the only goal of a good-fit regression for the predictive modeling tool. The first lesson one learns in Statistics 101 is that correlation does not necessarily imply causation. R2
can be manipulated by adding more explanatory variables, which can improve the appearance of historical fit while having no effect on the model’s predictive quality. Therefore, managers interested in bringing a data-driven approach to their firms should always ask for verifying evidence that data analysis results are true in the real world. A rigorous statistical regression methodology needs to be custom-tailored to a firm’s unique business profile. Capgemini differentiates its statistical regression analysis approach by applying modern statistical methods to siphon out noise and identify the most accurate economic relationships. Capgemini recommends repeating this regression exercise quarterly to reflect changing economic conditions.
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Integration with SAP BPC
The last step is to automate the whole process by integrating it with existing ERP solution. SAP BPC NW 10.0 is one of the most prominent budgeting & forecasting tool available. Once the model is developed, it can be easily with SAP BPC.
Figure 4: BPC Design
Economic Changes Quarterly Model Tune-Up Integrated with SAP-BPC
Ref. Steps Technical Solution 08.10.05 Load economic Indicators into
BPC
Statistical accounts were created in Account
Dimension to store economic indicators in BPC
08.20.00/
08.20.10
Load BW Actual into BPC A separate Z*BPC cube was created with 1:1 map
with BPC model. Using ETL, Actual from ECC were
loaded into BI and formatted based on BPC model
08.20.20 Generate Forecast Sales Regression formulae were created using excel. BPC
input schedule workbook was used to save the sales
data into BPC
08.20.25 Granular Level Data For reporting, sales data were allocated using BPC
allocation engine into more granular level. Allocation
were made based on last year allocation % values
08.20.30 GM% GM% was entered manually by business users. A BPC
input schedule was designed with using statistical
account to store allocation % values
08.30.35 COGS Calculation Cost of goods sold was calculated using SAP BPC
script. The formula
was developed
in BPC to generate the COGS
08.20.40 Reports Variance reports like Actual Vs Forecast were
developed using BPC EPM add in
08.20.50 Push to other Models Finally, SAP BPC script was used to push data from
BPC Forecast Model to other models like Operations
Table 1: BPC Process Steps
Reports & Dashboards
Reports and dashboards are last but key element of the methodology. Multiple reports were developed to give compare Actual Vs Predicted sales number. In addition, dashboards were created for top management to have better insights about the product.
Variance Report: Actual Vs Plan( Region wise)
Figure 5: Variance Report
Waterfall Report : Variance between old method Vs Statistical Sales Forecast Method
Figure 6: Waterfall Report
EPM Reports &
Dashboards gives
better insights
6. Why SAP BPC based Statistical Sales
Forecasting? Given the intertwined nature of the global economy, external factors influence sales
of a particular product in a particular region. BPC-based statistical forecasting relying
solely on historical sales to predict forecast numbers will often prove wrong. Best
practice should be for planners to consider external economic factors that influence
sales, though it is difficult to gauge the impacts of disparate economic factors on a
company’s business. Therefore, developing a model which considers both historical
data as well as external economic factors will generate more accurate sales numbers,
and automating and integrating this model into SAP-BPC saves management time and
effort. Let’s look at some of the benefits and their impacts on business.
Accurate sales prediction
Better Inventory management
Better capital allocation
Better insights about factors
influencing sales
Accurate earning guidance
Automated short term ( 1 year) and
long term ( 5 year) forecast process
Reduced budgeting & forecasting
cycle time
Integrated with other planning models
like operational planning
Dashboard with variance analysis
More Informed
Corporate Financial
Decisions
Better External Guidance
Reduced time for
budgeting & forecasting
Reduced total cost of
ownership
Better insights
Solution Benefits Business Impact
7. Capgemini’s offering Capgemini’s unique offering comprises of leveraging the expertise of Capgemini
Consulting to develop the sales forecast model and Capgemini’s EPM practice to
implement the sales forecast model into SAP BPC NW 10.0. With both teams working
together to leverage their strengths, this unique service offering has delivered tangible
results for our clients that have been received with astonishing success. The current
project was implemented within a timeframe of 12 months. Based on its success, we
are working on implementing a similar model for operational planning for new clients.
Figure 7: Cagemini’s offering
8. Going Forward As we observed, the statistical based sales forecasting solution is much more accurate
and efficient for business than historical forecasting alone. This model is not restricted
to a single industry. It can be expanded to all the sectors and business models. As a
technology company, we are looking to expand the scope of the model to offer similar
solutions across all planning processes that may include operations planning, financial
planning, HR etc. In addition, we are also looking at SAP’s predictive analytics and
open source R to improve the efficiency of the process.
9. Authors Gleb Drobkov,
Senior Consultant
[email protected] Business & Technology Innovation, North America
Pratyush Panda,
Senior Consultant