Integrated demand planning approach

1. © 2004 Hewlett-Packard Development Company, L.P.
The information contained herein is subject to change without notice
Integrated Demand
Planning Approach:
The Experience of an
Automobile Company in
India
Saranik Ghosh
Mitesh Verma

2. July 12, 2014 2
Presentation Outline
Indian Automobile Industry Overview
Demand Characteristics @ Customer
Need for an Integrated Demand Planning Approach
HP’s Demand Planning Approach
Benefits to Customer

3. Indian Automobile Industry
• Witnessing exponential growth in the recent
years
• Dominated by three major players
• Frequent introduction of new models
• Entry of global players intensifying domestic
competition

4. July 12, 2014 4
Demand Characteristics @ Customer

5. July 12, 2014 5
Need for an Integrated Demand
Planning Approach
Slice-Dice capability for What-if Analysis
Product Lifecycle
Factors
Level
Trend
Seasonality
Events
Limited Demand History
Traditional time-series
methods generate forecast
primarily based on “historical
data”
Cannot generate forecast in
absence of actual demand
data.
Ignores the impact of
lifecycle stage of the product

6. July 12, 2014 6
Our Demand Planning Approach
Fine Tune
Demand
Profile
Data
Cleaning &
Correction
Event
Corrected
Forecast
Compare
Forecast Vs
Actual
Track
Forecast
Error
Generate
Base
Forecast
Generate
Demand
Profile
Generate
Lifecycle
Template
Update
Average
monthly
sales
Generate
Base
Forecast
Calculate
Trend Index
Calculate
Seasonal
Index
Past Retail
Sales Data
Factors/Events
impacting Sales
Corrected
Demand
History
Seasonal Index
Trend Index
Predefined
Event
Templates
Custom Event
Templates
Library of Similar
Products
Mature Volume Estimate
Lifecycle Duration Estimate
Product
Type ?
New Products
Existing Products
Existing
Products
New
Products

7. July 12, 2014 7
Data Cleaning and Correction
• De-Eventize history
 Impact of events affecting demand
 Promotions and Ad Campaigns
 Competitor Activities
 Production Issues
 Product Upgrades
 Natural Calamities
 Magnitude of correction based on
 Inputs from planners
 Pre-defined and Custom Event
Templates
 Historical data analysis
• Institutionalized impact analysis of events
Data
Cleaning
and
Correctio
n
Past Retail
Sales Data
Factors/Events
Impacting Sales

8. July 12, 2014 8
Various events impacting demand Corrections to account for these events
Data Cleaning and Correction

9. July 12, 2014 9
Product Type –New Product
Product
Type
Forecasting for New Products
New Product

10. July 12, 2014 10
Generate Lifecycle Curve
Generate
Lifecycle
Curve
• Generate normalized lifecycle curves for like
products
 Percentage of lifecycle duration Vs
Percentage of cumulative demand
• Create Generic Lifecycle curve
 Plot weighted average lifecycle curve from
like products’ lifecycle curves.
 Define control limits
Forecasting for New Products
Library of
Similar Products
• In the absence of
demand history, retail
sales of similar products
is leveraged to generate
lifecycle curve for new
products

11. July 12, 2014 11
Generate Demand Profile and Base
Forecast
• Generate demand profile
 Convert cumulative demand to absolute
percentage demand for corresponding
lifespan percentage.
• Generate base forecast
 Apportion mature volume estimate by
corresponding demand percentage.
Forecasting for New Products
• The demand profile and
base forecast is
generated leveraging the
generic lifecycle curve,
mature volume estimate
and estimated lifespan.
Generate
Demand
Profile
Generate
Base
Forecast
Mature Volume Estimate
Lifecycle Length
Estimate
Generic Demand Profile
Mature volume estimate and lifespan based on like product history,
inputs from planners , marketing team and market research.

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Generate Event Corrected Forecast
Generat
e event
correcte
d
Forecast
• Correct base forecast for planned future events based on
 Planner inputs
 Database of past events
 Predefined and Custom event templates
Predefined Event
Templates
Custom Event
Templates
• Planned events requiring correction
 Promotions and Ad Campaigns
 Competitor Activities
 Product Upgrades
 Supply Issues
Templates for Promotion

13. July 12, 2014 13
Fine Tune Demand Profile
Compare
Forecast
Vs Actual
Sales
Fine Tune
Demand
Profile
Track
Forecast
Error
•Actual monthly sales compared to forecast to
improve forecasting accuracy in future.
•Measures of forecast error
Mean Absolute Percentage Error (MAPE)
Tracking Signal (TS)
•Corrective action for forecast errors outside
acceptance limit
Revisit estimated impact of events
Identify possible events not accounted for
Adjust demand profile
 forecast > actuals, depress subsequent
months’ forecast
 forecast < actuals, inflate subsequent
months’ forecast

14. July 12, 2014 14
Product Type –Existing Product
PRODU
CT YPE
Forecasting for Existing
Products
Existing Product

15. July 12, 2014 15
• Retail sales substantially impacted by local festivals
• Festivals follow Indian calendar – Annual Drifts
• Seasonal Indices apportion annual sales by months
• Seasonal Index calculation
 Calculate average monthly sales – Annual Sales/12
 Calculate Monthly Seasonal Index – Monthly Sales/Average Monthly Sales
 Weighted Average of historical monthly seasonal index to generate projected
seasonal indices.
• Alternative Seasonal Indices calculated for possible festival combinations
Calculate Seasonal Index
Forecasting for Existing
Products
Calculate
Seasonal
Index
Relevant monthly seasonal
indices applied based on
festivals’ schedule at the
beginning of the year

16. July 12, 2014 16
Trend accounting necessary for accurate forecast generation.
• Trend index (TI) = Weighted average (WA) sales for last three months
WA sales for corresponding months of previous year
• Average monthly sales (AMS) forecast =TI *AMS of the previous year
• Trend Index calculation necessary only for the first month
• Actual Sales of first month to adjust AMS for remaining months
Base Forecast Calculation
• Base Forecastm=1 to 12 =AMS * Seasonal Indexm=1 to12
• AMSi updated every month based on the actual sales data
• AMSi =Weighted Average (AMSi-3, AMSi-2, AMSi-1)
Calculate Trend Index & Base
Forecast
Forecasting for Existing
Products
Calculat
e Trend
Index
Generate
Base
Forecast
AMSi is used for
generating the forecast for
subsequent months

17. July 12, 2014 17
Generate Event Corrected Forecast
Generat
e event
correcte
d
Forecast
• Correct base forecast for planned future events based on
 Planner inputs
 Database of past events
 Predefined and Custom event templates
Predefined Event
Templates
Custom Event
Templates
• Planned events requiring correction
 Promotions and Ad Campaigns
 Competitor Activities
 Product Upgrades
 Supply Issues
Templates for Promotion

18. July 12, 2014 18
Update Average Monthly Sales
Compare
Forecast
Vs Actual
Sales
Update
Average
monthly
sales
Track
Forecast
Error
•Actual monthly sales compared to forecast to
improve forecasting accuracy in future.
•Based on actual sales
Recalculate average monthly sales (AMS)
Update rolling AMS
•Measures of forecast error
Mean Absolute Percentage Error (MAPE)
Tracking Signal (TS)
•Corrective action for forecast errors outside
acceptance limit
Revisit estimated impact of events
Identify possible events not accounted for

19. Forecast
Accuracy
Product
Types
What-if
Capabilities
Benefits to Customer
Events
- Improved from 67%
to 85%
- Facilitates impact
analysis of various
events
- Evaluation of
multiple scenarios
possible
- Forecasting
possible for both New
and existing products
Forecasting
Tool
Implemented
Integrated Demand Planning Approach

20. July 12, 2014 20
Questions ?