1. SESSION#2a: AGGREGATE DEMAND: A CASE STUDY (CFVG: 2012)
COMPETITIVE THROUGH DEMAND
MANGEMENT: CASE STUDY OF HP
SUPPLY CHAIN
Dr. RAVI SHANKAR
Professor
Department of Management Studies
Indian Institute of Technology Delhi
Hauz Khas, New Delhi 110 016, India
Phone: +91-11-26596421 (O); 2659-1991(H); (0)-+91-9811033937 (m)
Fax: (+91)-(11) 26862620
Email: r.s.research@gmail.com
http://web.iitd.ac.in/~ravi1

2. 2
In this session we plan to cover
Concept of Aggregate Forecast in a
Supply Chain
How good is forecast?
Case Study: HP

3. 3
Forecasting
A statement about the future value of a variable of
interest
Future Sales
Weather
Stock Prices
Other Short term and Long term estimates
Several Methods
Quantitative
History and Patterns
Leading Indicators / Associations (Housing Starts & Furniture)
Qualitative
Judgment
Consensus
Used for making informed Decisions and taking Actions based on those decisions

4. 4
Forecasting
Forecasts make a MAJOR IMPACT (Positive or Negative) on:
• Revenue
• Market Share
• Cost
• Inventory
• Profit
Sales will
be $200
Million!

5. 5
Three Major Types of Forecasts
Judgmental
– Uses subjective, qualitative “judgment” (opinions,
surveys, experts, managers, others). Most useful when
there is limited data and with New Product Introductions
Time series
– Observes what has occurred over previous time periods
and assumes that future patterns will follow historical
patterns
Associative Models
– Establishes cause and effect relationships between
independent and dependent variables (rainy days and
umbrella sales, pricing and sales volume, attendance at
sporting events and food sold, others)

6. 6
Qualitative Methods
Grass Roots Market Research
Panel Consensus
Executive Judgment
Historical analogy
Delphi Method
Qualitative
Methods

7. 7
Quantitative Techniques
Basic time series approaches
Moving averages, simple & weighted
Exponential smoothing, simple & trend adjusted
Linear regression (linear trend model)
Techniques for seasonality and trend -
Decomposition of time series
Causal approach
Simple Linear Regression
Multiple Linear Regression

8. Time Series Analysis
Time series forecasting models try to
predict the future based on past data
You can pick models based on:
1. Time horizon to forecast
2. Data availability
3. Accuracy required
4. Size of forecasting budget
5. Availability of qualified personnel

9. Simple Moving Average Formula
F =
A + A + A +...+A
n
t
t-1 t-2 t-3 t-n
The simple moving average model assumes an
average is a good estimator of future behavior
The formula for the simple moving average is:
Ft = Forecast for the coming period
N = Number of periods to be averaged
A t-1 = Actual occurrence in the past period for up to “n” periods

10. Simple Moving Average Problem (1)
Week Demand
1 650
2 678
3 720
4 785
5 859
6 920
7 850
8 758
9 892
10 920
11 789
12 844
F =
A + A + A +...+A
n
t
t-1 t-2 t-3 t-n

11. Week Demand 3-Week 6-Week
1 650
2 678
3 720
4 785 682.67
5 859 727.67
6 920 788.00
7 850 854.67 768.67
8 758 876.33 802.00
9 892 842.67 815.33
10 920 833.33 844.00
11 789 856.67 866.50
12 844 867.00 854.83
F4=(650+678+720)/3
=682.67
Calculating the moving averages gives us:
©The McGraw-Hill Companies, Inc., 2004
11

12. 500
600
700
800
900
1000
1 2 3 4 5 6 7 8 9 10 11 12
Week
Demand
Demand
3-Week
6-Week
Plotting the moving averages and comparing them shows
how the lines smooth out to reveal the overall upward trend
in this example
Plotting the moving averages and comparing them shows
how the lines smooth out to reveal the overall upward trend
in this example
Note how the
3-Week is
smoother than
the Demand,
and 6-Week is
even smoother
Note how the
3-Week is
smoother than
the Demand,
and 6-Week is
even smoother

13. 13
Case 1: Case Study of HP(1)
Demand
Forecasting: The
Supply Chain
Context

14. 14
How do we Forecast-Time Series
Analysis
Time series forecasting models try to predict the future
based on past data.
You can pick models based on:
1. Time horizon to forecast
2. Data availability
3. Accuracy required
4. Size of forecasting budget
5. Availability of qualified personnel

15. 15
Simple Moving Average Formula
F =
A + A + A +...+A
n
t
t-1 t-2 t-3 t-n
The simple moving average model assumes an
average is a good estimator of future behavior.
The formula for the simple moving average is:
Ft = Forecast for the coming period
n = Number of periods to be averaged
A t-1 = Actual occurrence in the past period for up to “n” periods

16. 16
Aggregate Forecasts at SC Level
Aggregate forecasts are more accurate
Forecast at the most aggregate/generic level
possible
Similarly, forecast at the most upstream of the
supply chain (if possible)
If possible, never use forecast information at the
lower levels. At the lower levels, decisions should
be based on actual demand

17. 17
Is it always possible to use it?
Only if the power supply can be assembled in small lead time
Power supply assembly should be at the end of the
manufacturing process
Board
assembly
Hard disk
Assembly
Testing
Power
supply
110 V
Board
assembly
Hard disk
assembly
Testing
Power
supply
110 V
Power
supply
220 V
Testing
Power
supply
220 V
Delayed product
differentiation
Product
postponement
Case 1: HP desktop (Aggregate Forecast)

18. 18
Case 1: HP desktop
Board
assembly
Hard disk
assembly
Testing
Power
supply
110 V
Power
supply
220 V
Product Product
Month 110 V PC 220 V PC
1 10000 8000
2 14000 4000
3 16000 2500
4 12000 6500
5 18000 2000
6 15000 4000
7 14000 3000
8 11000 7000
9 13000 5000
10 11000 6000

19. 19
Forecast accuracy improves at different levels
110 V 220 V Total
Months Demand MA(4) Error Demand MA(4) Error Demand MA(4) Error
1 10000 8000 18000
2 14000 4000 18000
3 16000 2500 18500
4 12000 6500 18500
5 18000 13000 -5000 2000 5250 3250 20000 18250 -1750
6 15000 15000 0 4000 3750 -250 19000 18750 -250
7 14000 15250 1250 3000 3750 750 17000 19000 2000
8 11000 14750 3750 7000 3875 -3125 18000 18625 625
9 13000 14500 1500 5000 4000 -1000 18000 18500 500
10 11000 13250 2250 6000 4750 -1250 17000 18000 1000
MAD 2291.67 1604.17 1020.83
Forecast
Accuracy 83.23% 64.35% 94.38%
(10000+14000+16000+12000)/4)
13000-18000
(5000+1250+3750+1500+2250) / 6 100-[(5+1.25+3.75+1.5+2.25)/(18+15+14+11+13+11)]100

20. 20
Learning Lesson of Case 1
What are the Learning
Lessons of this case
Study?

21. 21
Product Redesign Helps Supply Chain
Competitiveness
Demand management helps competitiveness and
cost reduction
Delayed product differentiation is the key to this
redesign
Aggregate forecasts are more accurate
Forecast at the most aggregate/generic level
possible
Similarly, forecast at the most upstream of the
supply chain (if possible)
If possible, never use forecast information at the
lower levels. At the lower levels, decisions should
be based on actual demand