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Integrated Demand Planning Approach: The Experience of an Automobile Company in India

Saranik Ghosh

Mitesh Verma

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

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

July 12, 2014 4

Demand Characteristics @ Customer

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

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

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

July 12, 2014 8

Various events impacting demand Corrections to account for these events

Data Cleaning and Correction

July 12, 2014 9

Product Type –New Product

Product

Type

Forecasting for New Products

New Product

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

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.

July 12, 2014 12

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

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

July 12, 2014 14

Product Type –Existing Product

PRODU

CT YPE

Forecasting for Existing

Products

Existing Product

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

ProductsCalculate

Seasonal

IndexRelevant monthly seasonal

indices applied based on

festivals’ schedule at the

beginning of the year

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

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

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

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

July 12, 2014 20

Questions ?