SESSION#2a: AGGREGATE DEMAND: A CASE STUDY (CFVG: 2012)

COMPETITIVE THROUGH DEMAND

MANGEMENT: CASE STUDY OF HP

SUPPLY CHAIN

Dr. RAVI SHANKARProfessor

Department of Management StudiesIndian Institute of Technology DelhiHauz 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.comhttp://web.iitd.ac.in/~ravi1

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In this session we plan to cover

� Concept of Aggregate Forecast in a

Supply Chain

� How good is forecast?

� Case Study: HP

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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

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Forecasting

Forecasts make a MAJOR IMPACT (Positive or Negative) on:

• Revenue

• Market Share

• Cost

• Inventory

• Profit

Sales will be $200 Million!

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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)

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Qualitative Methods

Grass Roots Market Research

Panel Consensus

Executive Judgment

Historical analogy

Delphi Method

Qualitative

Methods

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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

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

Simple Moving Average Formula

F = A + A + A +...+A

nt

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

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

nt

t-1 t-2 t-3 t-n

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

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500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Week

Dem

an

d 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

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Case 1: Case Study of HP(1)

Demand Forecasting: The

Supply Chain

Context

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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

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Simple Moving Average Formula

F = A + A + A +...+A

nt

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 averagedA t-1 = Actual occurrence in the past period for up to “n” periods

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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

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� 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 diskAssembly

TestingPower supply110 V

Board assembly

Hard disk assembly

Testing

Power supply110 V

Power supply220 V

TestingPower supply220 V

Delayed product differentiation

Product postponement

Case 1: HP desktop (Aggregate Forecast)

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Case 1: HP desktop

Board

assembly

Hard disk

assemblyTesting

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

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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

ForecastAccuracy 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

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Learning Lesson of Case 1

What are the Learning Lessons of this case

Study?

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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