SAP BPC Architecture & Project
Examples
Business Planning and Consolidation
BPC Embedded
Integrated Planning
By Sergei Peleshuk
August 2017
Agenda
▪ Introduction to SAP BPC
▪ Example 1: Planning App for Finance and non-Finance
KPIs
▪ Example 2: Promotional Sales Planning
▪ Example 3: Retail Sales and Demand Planning
Typical Planning Applications
Source: SAP
Why BPC Embedded?
▪ Strategic planning product from SAP
▪ Built in powerful HANA-enabled
disaggregation capabilities
▪ Planning Functions can be HANA-
optimized leveraging SQL Script in
AMDP
▪ Access to standard data logic
libraries in SQL Script and ABAP
▪ Master Data management by
business users via BW Workspaces
▪ No need to replicate data and/or
models between BW and BPC
▪ Integration with SAP Business
Objects reporting tools (AFO,
Lumira)
Components, BPC Embedded
Models Planning Engine
BW-IP
BPC Embedded Planning Components:• Business Process Flow
• Auditing
• Work Status
• User Management
• …
HANA Platform
BW
Analysis Office 2.4Embedded Model
Connector
Lumira 2
Designer Web Admin Client
Embedded
Model
BPC with S/4 and BW4
Source: SAP
Embedded Architecture Options
BPCIPBW/4HANA
ECC, S4
BPC 11 (or 10.1) Embedded
BPCIPBW 7.5S/4HANA
BPC 10.1 Optimized for ERP
Other Source
Systems
SAP BO BI Frontend Tools 2017
BPC
Embedded
Frontends
Agenda
▪ Introduction to SAP BPC
▪ Example 1: Planning App for Finance and non-Finance
KPIs
▪ Example 2: Promotional Sales Planning
▪ Example 3: Retail Sales and Demand Planning
Example 1: Planning App for Finance
and non-Finance KPIs
Objectives
• Deliver an application that allows collecting Finance KPIs from ERPs automatically; non-Finance KPIs and budgets manually via a common platform
• Deliver a set of dashboards for Client reporting on a mix of Finance and non-Finance KPIs at the same level of granularity
Background
• $20B Retail organization
• 35 countries
• 20 non-harmonized ERP systems (SAP and non-SAP)
• 2000 sites to maintain non finance KPIs
• No corporate network or common infrastructure
Technologies
• BO Design Studio1.5, BPC Embedded 10, BW 7.4 on HANA
KPI Components
Master DataGeography
Site
• Outlet
• Country
• Region
• Lot
• Area (Square meters)
• Work stations
• Number of Rooms
• Site Population
• Average Daily Attendance
• Opening Days
Contracts (Client, Site, Service Level)
• Type of Contract
• Type of Contract 2
• Type of Building
• Sector
• Relationship
Service
• Service Line
• Activity
GL Accounts
• Local GL Account
• ERP Unit Code
• Group Account
Currency Translation rules (USD, GBP, EUR, …)
• By Client
• According to Compass FY
Client
• Client Name
• Client Category
Time
• Date, Period, Year, Contractual Year
Transactional DataFinance (Amounts in Local currency)
• Actual Amount
• Costs
• Net Costs
• Staff/Labour Costs
• Subcontractor
• Cost of materials
• Sales
• Corporate Overhead
• Profit from Baseline
• Level 2-Offiste
• Level 2-Global
• Invoiced Revenue
• Gross Margin
• Net Margin
• Subsidy
• Profit & Loss
• Client Budget
• Actual Savings = Budget
• Targeted Savings = Budget
Ops
• Approved Suppliers
• Top 10 sold items
• Bottom 10 sold items
• Number of Transactions
• Wastage
• Rooms occupied
• Room checkouts
Granularity by Dimension
BPC/BW Modelling - Transactional
Portal Entry Page Mockup
In Design Studio
Portal Entry Page in the Browser
Example 1 Summary
▪ Data Entry & Analysis page is available to site users via a browser and standard
Internet connection.
▪ Country/Site Master data and Transactional data is maintained by authorized
users at sites via a web page.
▪ BW Analysis authorizations are used to restrict user access by site, country, KPI
Category.
▪ Designed in Business Objects Design Studio 1.5 connecting to BEx Queries on
top of BW IP in BW 7.4 on HANA.
▪ When users update KPIs and click Save, the numbers are automatically
updated in the direct-update ADSOs with data disaggregation executed to the
lowest level of granularity.
▪ Non-finance KPIs are combined in Composite providers with granular Finance
KPIs (FIGL line item data) for analytical reporting
▪ A set of analytical dashboards designed for Country and Group users (Lumira)
Agenda
▪ Introduction to SAP BPC
▪ Example 1: Planning App for Finance and non-Finance
KPIs
▪ Example 2: Promotional Sales Planning
▪ Example 3: Retail Sales and Demand Planning
Example 2: Promotional Sales Planning
Objectives
• Deliver an application for Promotional Sales Planning that would be leveraging existing sales actuals, master data in BW and the power of HANA
• Deliver a set of dashboards for Brand and District Management on a mix of Sales Actuals and Targets by Product Group, shop, period, etc.
Background
• €200M Retail organization
• 400 shops
• Previously planning was done using numerous spreadsheets with a lot of manual interventions
Technologies
• BO Analysis for Office 2.4, BPC Embedded 10.1, BW 7.4 on HANA
Promotion Sales Planning Process
Upload Master data Yearly
• Promo Sales Periods (T1, T2, T3,..) by year
• Product Group to Vendor Sub-range
• Promo Schedule by Product Group, Period (T1, T2, T3,..)
Maintain Sales Plan target (1,2,3) by Shop
• Copy Actuals to Plan Targets using % increase/ decrease (Manual)
• Generate sales plan figures for new periods by Plant/ Group
Adjust Plan at granular level
• Review Plan lowest level by Product Group/ Plant
• Introduce adjustments
• Save
Maintain Master Data
Group – VSR
Mapping
• Brand
• Group Name
• Vendor Sub-
Range
Promo Periods
◦ Brand
◦ Year
◦ Period
◦ Date From
◦ Date To
Promo Schedule
◦ Brand
◦ Year
◦ Period
◦ Group Name
Granularity
Dimensions
• Shop
• Department
• Segment
• Company
• Product Group
• VSR
• Article
Key Figures
• Amount Actual (Net Sales), EUR
• Target 1, 2, 3, EUR
• T-Period*
• Cal Year
* Planning Level
Planning Architecture
Sales Actuals
BP
C E
mbedded
BW
BO
Sales Promo
Plan
BO Analysis for Office
BW Queries
ReportingManual
Planning
Planning
Function
Aggregation
Levels
Group VSR
Map Promo Schedule
SD CSV CSV
Day VSR Group
ZADVSRGR
CSV
Promo
Period
Sales Promo
Plan
Material
Generate valid
combinations
Promo Schedule
Char Rels
Dashboards: Sales vs. Target
Example 2 Summary
▪ Designed a model in BPC Embedded (BW 7.4 on HANA), BO AFO, Webi
▪ Master data upload procedures developed
▪ A set of input forms designed for the head office management to maintain Sales Promo Targets by brand, shop, period
▪ A set of analytical KPIs and dashboards have been designed in AFO and Webi for the Head Office Analytics, District Managers and Shops
▪ Data Input forms & Analysis pages for Sales Promotions are published and available to users (AFO) via the BI Platform
Agenda
▪ Introduction to SAP BPC
▪ Example 1: Planning App for Finance and non-Finance
KPIs
▪ Example 2: Promotional Sales Planning
▪ Example 3: Retail Sales and Demand Planning
Example 3: Retail Sales and Demand
Planning
Objectives
• Deliver a BPC Sales and Demand Planning application leveraging existing actual sales, peak seasonal details, evolving master data and business changes like new products, new shops, etc.
• Leverage master data in BW and the power of HANA on large data sets
• Deliver Demand Planning capabilities by product, shop, week; integrate with ECC
• Deliver a set of reports and dashboards with a range of KPIs on Actuals vs. Plan for head office management and shops
Background
• €200M Retail organization
• 400 shops
• Previously planning was done in Excel spreadsheets and MS Access application with limited capabilities and a lot of manual interventions
Technologies
• BO Analysis for Office 2.4, BPC Embedded 10.1, BW 7.5 on HANA
Planning Process
Maintain Master data
• Identify Articles for Sales Planning by Product Class (Bestseller, Not to miss, Derivé)
• Maintain New Shops
• Store relationships (comparable)
• Exceptions
Generate “Normalized Actuals”
• Copy Actuals Sales from Prior Year, generate Plan Version 0
• Apply Forecast Drivers (backend rules 1, 2)
Apply Frontend Rules
• Copy Normalized Sales to Plan Versions 1-4
• Apply Forecast Drivers (frontend rules/input forms)
Granularity
Dimensions
• Shop*
• Department
• Segment/Whse
• Company
• Article
• VSR
• Vendor
• Status
• Family/Sub-family
• Cal Week
• Cal Month
• Cal Quarter
• Cal Year
• Version * Planning Level
Key Figures
• Amount Actual (Net Sales), EUR by Profit Center
• Actual Sales Qty, PC
• Current Stock Level, PC
• Normalized Sales Qty, PC (V0)
• Plan Qty, PC (V1, V2,…)
Calc: Purchase Forecast Amount (based on PMP Price)
Backend Rules
For All Classified Articles (uploaded in BPC):
• Year -> Year +1
• Group Sales by Profit Center (including online sales)
• Apply Corrections (including Exceptional sales) uploaded from Excel file by Shop, Article, week, Delta in Qty
• Generate Cluster Reference Table
• Apply rules per Product Class (Out Of Stock)
• For Days that are Out of stock replace weekly actuals with equivalent from a Sales by Cluster table. If 0 check the upper cluster, or 1 if it’s XXL
• For new shops use Cluster Table with Percentage
• For new SKUs replace SKUs using master data table for SKU comparable
1
2
Planning Architecture
Sales Actuals
BP
C E
mbedded
BW
BO
Sales Plan
BO Analysis for Office
BW Queries
Reporting/
Dashboards
Manual
PlanningPlanning
Function
Aggregation
Levels
SD ECC FM: Read Forecast
MD
MD
MD
MD spreadsheets
MM Material Stock
MM
Normalized Actuals
Lumira
Backend Rules 2
BW Queries
Sales Plan
Copy Version X -> Y
• Version From/To
• Month From/To
Corrections
(Exceptional)
Excel
Sales by week
Out of Stock
Backend Rules 1
Frontend Rules
• SUPPLIER (Vendor/VSR)
• Per supplier and/or VSR, add coefficient
• SKU (Article, Shop, week)
• Per SKU, add coefficient
• POS (Shop)
• Per POS, add coefficient
• New POS, select comparable POS and/or add
coefficient
• TIME (Week) – Demand Planning
• Define and apply weights per week per product
cluster
V1
V2
V3
V4
Project Man Days
MD
1. Functional Specs/Design Doc 8
2. Design Model in BPC Embedded/BW 8
3. File Upload logic (MD+ exceptions) 5
4. Normalized Actuals Logic (up to 6 Drivers) 15
5. Input Forms by Vendor, Article, Shops, Time 8
6. Copy Version Backend logic (SQL Script) 3
7. Test scenarios/UAT (up to 10), corrections 10
8. Move to Production 3
9. Adjustments based on Prod data 5
10.SAP ECC Integration 15
Total: ± 80MD
Future Enhancements
• Identify Outliers automatically on the backend, use standard HANA
function for Anomaly Detection:
PAL uses k-means to realize anomaly detection in two steps:
• Use k-means to group the origin data into k clusters.
• Identify some points that are far from all cluster centers as anomalies.
• Implement Time Series Predictive Algorithms on the backend (e.g.
for Plan Version 1) using standard HANA functions, e.g.:
• Linear Regression with Damped Trend and Seasonal Adjust
• Auto ARIMA
• Integrate with SAP BPC Cloud and Predictive (after upgrade to
BW/4HANA)
Example 3 Summary
▪ Designed a model in BPC Embedded (BW 7.5 on HANA),
BO AFO leveraging the latest Agile modelling technologies
in BW and the power of SAP HANA.
▪ A bunch of backend rules implemented to generate
“Normalized Actuals” used as Plan Version 0.
▪ A set of master data upload procedures have been
developed using BW Workspaces in BW 7.5 on HANA
▪ Frontend rules in multiple input sheets (AFO) mimic
business planning logic by Brand, Shop, Product, and Time
generating Plan Version 4 as a Demand Plan by Shop
Contact Us
Sergei Peleshuk is an experienced Project and Delivery Manager, Architect with over 15
years of experience in Business Analytics.
Sergei is helping clients to benefit from the latest SAP BI, BPC and HANA technologies.
His focus is on delivering efficient solutions in Business Analytics, Data Warehousing,
Business Planning, Predictive Analytics and Big Data. Sergei is a hands-on consultant,
who has helped clients with the application architecture, roadmaps, vendor and product
selections, cloud infrastructure and building and managing successful project teams.
Sergei is a recognized speaker and blogger on SAP Business Analytics and writes articles
on sapexperts.com and biportal.org.