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.

peleshuk@biportal.org