Gitacloud SAP IBP for Demand Webinar April 5th 2018

1© 2018 GitaCloud, Inc. All Rights Reserved.
SAP IBP for Demand Webinar
GitaCloud Webinar Series 2018

Moderator Introduction
• Welcome to this webinar focused on IBP for Demand
• This webinar is brought to you jointly by GitaCloud and SAP
• All attendee microphones will stay muted
Webinar Moderator: Guenter Schmidt – Principal, GitaCloud
• 25+ years implementing and supporting SAP Supply Chain solutions in Europe, North America and Asia
• Expertise in Semiconductor / High-Tech, Aerospace, innovative and non-mainstream use-cases
• ex-Broadcom Sr. Director Business Transformation and Sr. Director IT, responsible for SAP ecosystem
• ex-TriQuint Semiconductor IT Applications Director, IT owner of Response Planning Processes

Before we begin

Our Speakers
Dr. Ramesh Bollapragada – Director, College of Business, San Francisco State University
• Comes from Bell Labs.
• Accomplished educator and researcher in Supply Chain domain.
• Teaches Supply Chain Decision Sciences round the globe.
• Multiple Professor of the Year Awards.
• Statistical Forecasting Specialist. Several patents.
Anand Doraiswamy – Head – Supply Chain, Dabur International
 SCM professional offering over decade of end-to-end Supply Chain solutions that directly results in efficiency
improvements & cost savings.
 Integrated Supply Chains across globe which include the Americas, Europe, Middle East & Africa.
 Developed world class tools & methodology for demand & supply planning from scratch encompassing the entire
value chain
Tod Stenger – SCM Solution Management, SAP America
 Solution Owner for SAP IBP for Demand
 Supply Chain Management specialist with experience in designing and implementing SCM solutions for SAP
 Past roles include Product Manager SCM and Platinum Consultant at SAP
 Specialties: Supply Chain Management - Forecasting, Supply Planning, Sales & Operations Planning

Our Speakers
Ashutosh Bansal – President & CEO, GitaCloud
 2 decades of SAP Supply Chain Solutions Implementation Experience at multiple Fortune 500 companies in US
 SCM/IBP SME: Ashutosh has led sales & delivery for multiple IBP engagements globally
 Was at SAP for 10 years: where Ashutosh played High-Tech Industry Principal, Sales Executive, and SCM SME roles
 Ashutosh blogs actively on IBP topics at www.GitaCloud.com/blog and LinkedIn (11K+ followers)
Ashish Meshram – Lead IBP Consultant, GitaCloud
 Experienced IBP Consultant. Led IBP S&OP Implementation at a USA based CPG company
 Led IBP Demand Pilot at a CPG company in Middle East
 Led IBP POCs for multiple customers ranging from Mining, CPG, Pharma, and High-Tech industries
 Led several IBP customer workshops across IBP S&OP, Demand, Inventory, and Response & Supply applications
Deep Chhichhia – IBP Consultant, GitaCloud
 Experienced IBP Consultant. Led S&OP Implementation at a CPG company in 2017
 Led Forecast Error reduction by 60% for another large CPG company – top 2 international markets
 Configured IBP Order Based Planning and SDI based integration for 2 large customer POC engagements
 Led several IBP customer workshops across IBP S&OP, Demand, Inventory, and Response & Supply applications

Agenda
Customer Case
Study: Dabur
International
IBP for Demand Showcase Wrap-up
Q&A
Kick-off
5 min 15 min 60 min
Guenter Schmidt Anand
Doraiswamy,
Dr. Ramesh
Bollapragada
Ashish Meshram, Deep Chhichhia All
IBP for
Demand
Overview
10 min
Tod Stenger

Agenda
Customer Case
Study: Dabur
International
Kick-off
5 min 15 min
Doraiswamy,
Dr. Ramesh
Bollapragada
IBP for
Demand
Overview
10 min
Tod Stenger
Q&A
60 min

• Dabur International is a wholly owned subsidiary of Dabur Group, headquartered in Dubai, UAE
• It has an overall turnover of around USD 1.3 billion and a market capitalization of nearly USD 9 billion
• It manages a global portfolio of 1000+ natural personal care and health care products
• Successfully seen its growth of its brands and has presence in 100+ countries across all 5 continents
International Operations

Dabur International: Business Locations

• Rolling monthly demand plan from Sales & Marketing.
• Push based Primary Sales model.
• Rolling monthly dispatch & production plan from Supply Chain.
• SAP ERP - MRP run for RM/PM Procurement.
• Factory Planners develop detailed factory schedule.
Current State

• Forecasting is not through a comprehensive automated system.
• Month-end Primary Sales push skews & distorts true demand consumption insights.
• Low forecast Accuracy & Fidelity
• Promotion calendar not frozen in advance leading to last minute rush.
• High demand whip-lash month-to-month.
• Low ability to systematically sense demand and drive supply chain.
• No scientific way of predicting New product demand.
Challenges

• Sudden irregularities in historical data due to currency fluctuations / geo political issues for 2017
which made forecasting difficult.
• Multiple data files from Dabur contain a different subset of SKUs for the same country and hence it
was difficult to clean and organize data for countries
• Effect of promotions (consumer and trade) not included because of lack of information
• Hit / Miss not an ideal parameter for calculating forecast accuracy (does not take into account the
amount ordered). Example- All SKUs with 0 amount ordered were considered as a HIT
Challenges

Forecasting Pilot Execution Approach
Review and familiarize with
data files
Define Statistical
Forecasting scope: Choose
countries
Choose Forecast Error
Metric: Weighted Mean
Absolute Percentage Error
MAPE
Establish Forecast Accuracy
Baseline: based on Raw
weighted MAPE in forecast
Set-up SAP IBP Planning
Area
Upload historical data for
29 months for each SKU in
chosen countries
Data Analysis:
Intermittency Review,
Outlier Correction /
Missing Value Substitution
Generate Statistical
Forecast using different
models: lag 0, lag 1, lag 2
Review forecast error for
lag 0, lag 1, lag 2 by model;
pick best model
Compare baseline
accuracy with the accuracy
from Statistical forecast:
quantify value-added
Present results

Forecast Accuracy Metric based on HIT/MISS
Market Type A A+ B B+ C
1A 15% 20% 20% 25% 75%
1B 25% 35% 35% 55% 75%
1C 50% 100% 75% 200% 100%
2A 40% 100% 50% 175% 100%
2B 40% 60% 40% 75% 100%
If forecast vs. orders are within +/- 15% for an A segment product, then this forecast is considered a
HIT, else a MISS.
We count the number of products which qualified as a HIT, divide it by total products in A segment to
report the forecast accuracy for A segment.
The tolerance band changes by product segment and market type.

Forecast Accuracy Metric based on Weighted MAPE
• SKU specific MAPE = 0 if Demand and Forecast are both zero,
else MAPE = Absolute [Difference between Demand and Forecast] divided by maximum of [Demand , Forecast]
• Weighted MAPE for the Market for a given period = (SKU specific MAPE * Demand for that SKU) / Demand for all SKUs in the
Market in that period
Example: SKU FG code: GCxxxx0007
• Demand Jan ‘17 = 200
• Jan ‘17 forecast = 110
• Absolute error = |200-110| = 90
• SKU MAPE for SKU GCxxxx0007 for Jan 2017 = 90 / (Max(110,200) = 45%
• SKU Weight : SKU specific demand / Total Demand for the Market = 200/77675= 0.0026
• Weighted MAPE (for Country 1 market across all SKUs for Jan 2017) = Sum(SKU specific MAPE * SKU Weight for all SKUs in the
market) = sum(0.45*0.0026 = 0.0012+…) = 36%
• Forecast Accuracy = 100% - wMAPE = 100% - 36% = 64%

Country 1: Forecasting Results from SAP IBP
Statistical Forecast Accuracy by Lag for May 2017
based on wMAPE measure
May’17
Accuracy
Baseline
Improved By
Lag 0: May 2017 forecast (May Cycle) based on Actuals till April
2017
64% 83%
Lag 1: May 2017 forecast (Apr Cycle) based on Actuals till
March 2017
63% 66%
Lag 2: May 2017 forecast (Mar cycle) based on Actuals till
February 2017
64% -
Statistical Forecast Accuracy across full historical
horizon based on wMAPE measure
24 Months
(Jan’15 –
Dec’16)
29 Months
(Jan’15 -
May’17)
Lag 0: Ex-Post (Historical) Forecast Accuracy 74% 68%
Metric Baseline
SKU Count 447 SKUs
Baseline: Lag 0 wMAPE based Forecast Accuracy for May 2017
(May Cycle)
wMAPE based Accuracy 35% (as
opposed to Hit/Miss based 66%)
Baseline: Lag 1 wMAPE based Forecast Accuracy for May 2017
(Apr Cycle)
wMAPE based Accuracy 35% (as
opposed to Hit/Miss based 56%)
35%
38%
65%
66%
64%
74%
68%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Baseline Lag 0
Baseline Lag 1
Statistical Lag 0
Statistical Lag 1
Statistical Lag 2
Ex-Post 24 Months
Ex-Post 29 Months
Forecast Accuracy Comparison (UAE 447 SKUs)

Country 1: Forecast Results by SKU Classification
SKU
Classification
SKU
Count
Baseline
Lag 0
Baseline
Lag 1
Statistical
Lag 0
Statistical
Lag 1
Statistical
Lag 2
A+ 30 24% 59% 57% 57% 67%
A 57 57% 53% 54% 33% 46%
B+ 21 39% 70% 55% 49% 46%
B 69 62% 49% 52% 58% 63%
C 164 49% 35% 71% 69% 71%
No classification 106 79% 83% 81%
30
57
21
69
164
106
SKU Count By Class
A+ A B+ B C No Classification
57%
54%
55%
52%
71%
79%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
A+
A
B+
B
C
No Classification
Forecast Accuracy Comparison (UAE 447 SKUs)
Statistical Lag 2 Statistical Lag 1 Statistical Lag 0 Baseline Lag 1 Baseline Lag 0

Agenda
Customer Case
Study: Dabur
International
Kick-off
5 min 15 min
Doraiswamy,
Dr. Ramesh
Bollapragada
IBP for
Demand
Overview
10 min
Tod Stenger
Q&A
60 min

CUSTOMER
Tod Stenger, SAP
April 5, 2018
SAP Integrated Business Planning
Demand management functionality

Develop more accurate mid-term statistical forecasts
React faster to short term demand changes with
pattern recognition based algorithms
Collaborate to ensure the most accurate demand plan
Drive more accurate deployment of product based on
short term demand
Inventory
Demand Response and Supply
Sales & Operations
Supply Chain Control Tower
SAP Integrated Business Planning for demand

Tactical & Operational Demand Management
Building Blocks and Key Use Cases
Demand
Management
Consensus
Demand
Planning
Statistical
Forecasting
Demand
Sensing
Calculate, analyze, and manage forecast accuracy
New product introduction: Phase-In, Phase-Out, and Like Modelling
Planning based on priorities and segmentation results
Create and collaborate on a Consensus Demand Plan
Create statistical forecasts with sophisticated algorithms
Run daily forecasts with Demand Sensing

 Statistical Forecasts
 Sales Input
 Key customer
activities
 Mid term trends
Medium Term (Demand
Planning)
 Order patterns
 Short term trends
Short Term (Demand
Sensing)
 Macro-economic
indicators
 Global/Regional
trends
 Marketing input
Long Term (S&OP)Horizon
Drivers
IBP
IBP for sales and operations IBP for demand
Increasing forecast accuracy over time

Sales History and
other historic data
Statistical
Forecast
(Output from Algorithms)
Input from
Sales
Departments
Input from
Marketing
Departments
Input from local
Demand
Planners
Consensus
Demand Plan
From Statistical Forecasting to a Consensus Demand Plan
Granularity: Demand Planning or S&OP (Weekly or Monthly)

Consensus
Demand Plan
Daily Sensed
Demand Plan
SAP‘s Demand Sensing
Algorithms
Shipments Sales Orders Trade
Promotions
POS Data …
From Statistical Forecasting to a Consensus Demand Plan
Granularity: Daily

time
Forecast
wk1 wk2 wk3 wk4 wk52…
Demand Planning: e.g., executed monthly in weekly buckets
Demand Sensing: Generally done daily in daily buckets
Difference between short-term forecast and consensus mid/long-term
forecast
Demand Planning = Time series methods – based on history - trend and seasonal patterns
Demand Sensing = Adjust the demand plan – data from present and recent past - pattern recognition
algorithms
Integrated Business Planning for Demand
Demand Planning vs. Demand Sensing

Weekly Adjustments
Daily Disaggregation
Forecast patterns
Shipment patterns
Optimal blend by lag patterns
Open Order data
correlation
corrections
Bias Tracking
Controls
Intelligent forecast
consumption logic
• Daily outputs for execution
systems
• Aggregated weekly or
monthly values for planning
and reporting
• Bias and variability
calculations
Output
Optimal weighting
Pattern recognition
Demand Sensing identifies optimal blend of inputs to better
predict demand

Deployment and
transportation
decisions
Production and
packaging sequences
Material
purchasing
Short Term Planning Horizon
Inventory Optimization
Today
What planning processes does Demand Sensing impact?
Illustration of the different planning horizons

National DC
East DC
West DCWeekly forecast of 40 units
Daily Replenishment Schedule w/
updated forecast
Mon
Tue
Wed
Thur
Fri
West Daily Demand Trend
Mon
Tue
Wed
Thur
Fri
East Daily Demand Trend
Effect of Demand Sensing

National DC
East DC
West DCWeekly forecast of 40 units
Mon
Tue
Wed
Thur
Fri
West Daily Demand Trend
Mon
Tue
Wed
Thur
Fri
East Daily Demand Trend
Daily Replenishment Schedule w/
updated forecast
Sales trends are picked up with Demand
Sensing and updates short term forecast.
Effect of Demand Sensing

 Substitute missing values
 Outlier correction
 Promotion Elimination
(Demand Sensing only)
 …
Pre-Processing
algorithms
Time Series algorithms
Regression based
algorithms
 Single Exponential Smoothing
 Adaptive Response Rate Single Exponential
Smoothing
 Double Exponential Smoothing
 Triple Exponential Smoothing
 Automated Exponential Smoothing with
parameter optimization
 Croston’s Method
 Simple Average
 Simple Moving Average
 Weighted Average
 Weighted Moving Average
 ARIMA models
 Brown’s Exponential Smoothing
 Gradient Boosting (planned for 2018)
 Multiple Linear
Regression (MLR)
 …
Statistical Forecasting Methods in IBP

• Flexible definition of forecast error
calculations
• Any key figures as inputs
• Calculations based on any lag
• Error measures:
• Bias
• Coefficient of Variability (CV)
• MAPE
• WMAPE
• MPE
• MSE
• RMSE
• MASE
• MAD
• Executed using operator KPI_PROFILE
• Determine what is most accurate:
• Stat forecast vs Actuals
• Consensus vs Actuals
• Sales Plan vs Actuals
Forecast Error Calculation

DSiM HCI IBP
Aggregated Sales
Stock Snapshot
Sales for IBP
Stock for IBP
1:1
Mapping
Staging
(Key Figure )
Planning Area
(SAP6)
Key Figures
Reuse the cleansed and harmonized Point of Sale (POS) Data from your retailers in
• Use the POS data in your Analytics and Dashboards for detailed analysis
• Use the POS data as additional input factor for your Demand Sensing and Planning Processes.
• Integration concept takes care of data aggregation from DSiMs Retailer Store Level in Daily Buckets to IBPs
Manufacturer DC level in Weekly Buckets.
Integration of Point of Sale (POS) Data from
SAP Demand Signal Management (DSiM 2.0 FP04)

Agenda
Customer Case
Study: Dabur
International
Q&A
Kick-off
5 min 15 min 60 min
Doraiswamy,
Dr. Ramesh
Bollapragada
IBP for
Demand
Overview
10 min
Tod Stenger

IBP for Demand Showcase
Demand Forecasting in Action…

ABC-XYZ Segmentation
New Product
Introduction
End Of LifeStable SKU Portfolio
Volume
Y: Medium Volatility
(Trend / Seasonal
Demand)
CX CY CZ
BX BY
Z: High Volatility
(Random or
Sporadic Demand)
X: Low Volatility
(Constant Demand)
Volatility
C: Low
Volume
B: Medium
Volume
A: High
Volume
BZ
AX AY AZ
Sales, Marketing Demand Planner Re-order Point

Segmentation: Customer Example
New Product
Introduction
End Of LifeStable SKU Portfolio
Volume
Trending
.5 < CV < 1.0
WS WT WE
JS JT
Erratic
CV > 1.0
Stable
CV < .5
Volatility
Walkers
10%
Joggers
25%
Sprinters
60%
JE
SS ST SE
CS CT CECrawlers
5%
Sales, Marketing Demand Planner Re-order Point

Auto-ARIMA / SARIMA Forecasting Models in IBP 1802
ARIMA models are a good fit for
• Short term forecasting purposes
• Mature products with long lifecycles
• Products with considerable sales history
• Volatile trends where automatic and rapid
adjustments to forecast is key
• No noticeable trend or seasonality exists
• Data on demand drivers is not available
ARIMA models are not a good fit when
• Historical data is highly intermittent
• New Products or products for which sufficient
history is not available
• ARIMA models can take longer to calculate
compared to Exponential Smoothing Models

ARIMA Component Parameter What it does?
AR: Auto Regressive p • Select number of previous periods to generate forecast.
I: Integrated d • Differencing: Values are replaced with deltas this number of times to
make the non-stationary time series becomes stationary.
MA: Moving Average q • The optimal number of periods for which the moving average of forecast
errors is calculated.
• This is to eliminate irrelevant changes (white noise) by replacing forecast
error by average error for adjacent set of periods

Demand Sensing in Action…

Introduction to Demand Sensing
Full Demand Signal Spectrum
Sales and
operations
planning
Consensus
Demand
planning
Demand
Sensing
Demand
signals
IoT & Sensors
Years to Months
Months to Weeks
Days
Streaming
Time Granularity of Demand
SAP Demand Signal
Management
SAP Connected
Goods
Source of Demand Signal
Ultimate Sources
of Demand

Lag Based Snapshots
Product Location Customer Week 14 Week 15 Week 16 Week 17 Week 18 Week 19
GC100027 L455 30045 2984 2849 1575 2138 3929 3045
Product Location Customer Lag Week 14 Week 15 Week 16 Week 17 Week 18 Week 19
GC100027 L455 30045 0 2984
1 2849
2 1575
3 2138
4 3929
5 3045

Settings for Demand Sensing
Historical Horizon: 52 Weeks
Effective Historical Horizon =
Historical Horizon – [Maximum (Bias Horizon, Open Order Horizon) + 2 * (Sensing Horizon – 1)]
If Historical Horizon = 52 weeks, Sensing Horizon = 6 weeks, Bias Horizon = 5, Open Order Horizon = 8, then
Effective historical horizon =
52 – [Maximum (5, 8) + 2 * (6 – 1)] = 52 – [8 + 10] = 52 – 18 = 34 weeks
Effective Historical Horizon: 34 Weeks

Agenda
Customer Case
Study: Dabur
International
Wrap-up
Q&A
Kick-off
5 min 15 min
Doraiswamy,
Dr. Ramesh
Bollapragada
All
IBP for
Demand
Overview
10 min
Tod Stenger
60 min
Ashish Meshram, Deep Chhichhia

47
GitaCloud Forecasting Services
Forecasting Pilot: Showcase 20% or more reduction in forecast error In 4 weeks or less

Upcoming IBP Demand, Inventory Bootcamps
KEY INFORMATION
When?
30th April – 4th May, 2018
Where?
SAP San Ramon Office at
2700 Camino Ramon,
San Ramon, CA 94583,
USA
How much?
$3999 for full 5-day event
$2299 for Demand or Inventory only
How to register?
Full 5-day Demand & Inventory
Bootcamp Link
2-day IBP Demand Bootcamp
Link
2-day IBP Inventory Bootcamp
Link
Questions?
Email at connect@gitacloud.com
Call at +1 925 519 5965

GitaCloud Digital Learning Platform
Visit learning.gitacloud.com

• Evaluate SAP IBP with GitaCloud Bootcamps:
• SAP IBP Demand & Inventory Bootcamp in San Francisco Bay Area, USA: April 30th – May 4th, 2018
• SAP IBP Platform & Configuration Bootcamp in Mumbai, India: July 9th – 13th, 2018
• SAP IBP Platform & Configuration Bootcamp in San Francisco Bay Area, USA: July 30th – August 3rd, 2018
• SAP IBP Response & Supply Bootcamp in San Francisco Bay Area, USA : September 17th – September 21st, 2018
• Visit www.gitacloud.com/events for full events calendar
• Ramp-up on SAP IBP on your own schedule:
• GitaCloud Digital Learning Platform: learning.gitacloud.com
• GitaCloud YouTube Channel
• Connect with GitaCloud for an SAP IBP Workshop at your premises:
• Email: connect@gitacloud.com
• Phone: +1-925-519-5965
• Address: 6200 Stoneridge Mall Road, 3rd Floor, Pleasanton, CA 94588, USA
• Website: www.gitacloud.com
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Gitacloud SAP IBP for Demand Webinar April 5th 2018

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