3 session scm demand management 2014 iimr

8/10/2019 3 Session Scm Demand Management 2014 Iimr

1/17

Demand Forecasting

in a Supply Chain.

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: [email protected], [email protected],

http://web.iitd.ac.in/~ravi1

eman orecas ng

Forecasting

Predict the next number in the pattern:

a) 3.7, 3.7, 3.7, 3.7, 3.7, ?

. , . , . , . , . ,

c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?


2/17

Forecasting

Predict the next number in the pattern:

a) 3.7, 3.7, 3.7, 3.7, 3.7, 3.7

. , . , . , . , . ,

c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5,

.

9.0

BALANCE OF FORECASTING

EFFORTSReference:

Ravi Shankar, IndustrialEngineering & Management

(2010)

Types of forecasting methods

Rely on data andRely on subjective

opinions from one

Qualitative methods Quantitative methods

techniques.or more experts.


3/17

Qualitative Methods

Grass Roots

Assessment

Executive Judgment

Historical analogy

Market Research

Panel Consensus

Delphi Method

Qualitative

Methods

Qualitative forecasting methods

Executive Judgement: banking on the experience of executives,

who have dealt with similar situations.

Historical Analogy: identifying another similar market.

Market Research: trying to identify customer habits; new product

ideas.

Grass Roots: deriving future demand by asking the person

closest to the customer.

Panel Consensus: deriving future estimations from the synergy

of a panel of experts in the area.

Delphi Method: similar to the panel consensus but with

concealed identities.

Quantitative forecasting methods

Time Series: models that predict future demand based

on past history trends

Causal Relationship: models that use statistical

techniques to establish relationships between variousitems and demand


4/17

Components of Demand

Average demand for a period of time

Trend Seasonal element

Random variation

Demand

Time

SeasonalPattern

Demand

Time

Cycle

Demand

Time

Average

Product Demand over Time

Demand

Time

Trend

Random

movement

Demand

Time

Trend withSeasonalPattern

11

Time Series Models

Try to predict the future based on past data

Assume that factors influencing the past willcontinue to influence the future


5/17

Naive Approach

Demand in next period is the same as

demand in most recent periodAugust Sales = 120

Usually not good

Simple Moving Average

Assumes an average is a good estimator of

future behavior

Used when trend is lesser or absent

n

A+...+A+A+A=F 1n-t2-t1-tt1t

+

+

Ft+1 = Forecast for the upcoming period, t+1

n = Number of periods to be averaged

A t = Actual occurrence in period t


Forecast sales for months 4-6 using a 3-period

moving average.

n


+

+

SalesMonth (000)

1 4

2 6

3 54 ?5 ?6 ?


6/17

2a. Simple Moving Average

Sales Moving Average

n


+

+

Forecast sales for months 4-6 using a 3-period

moving average.

Month (000) (n=3)1 4 NA

2 6 NA

3 5 NA4 ?5 ?

(4+6+5)/3=5

6 ?


Sales Moving Averageon (n=3)1 4 NA

2 6 NA

3 5 NA4 3

5 ?

5

6 ?

?


2a. Simple Moving AverageSimple Moving Average

on (n=3)1 4 NA

2 6 NA

3 5 NA4 35 ?

5

6 ?

(6+5+3)/3=4.667


7/17


Sales Moving Averageon (n=3)1 4 NA

2 6 NA

3 5 NA4 35 7

5

6 ?

4.667?


2a. Simple Moving AverageSimple Moving Average

on (n=3)1 4 NA

2 6 NA

3 5 NA4 3

5 7

5

6 ?4.667

(5+3+7)/3=5

Gives more emphasis to recent data

Weighted Moving Average

1n-tn2-t31-t2t11t Aw+...+Aw+Aw+Aw=F ++

decrease for older data

sum to 1.0


8/17

Weighted Moving Average: 3/6, 2/6, 1/6

Month Weighted

MovingAverage

1 4 NA

1n-tn2-t31-t2t11tAw+...+Aw+Aw+Aw=F

++

Sales

(000)

2 6 NA

3 5 NA4 31/6 = 5.16756 ?

??

Weighted Moving Average: 3/6, 2/6, 1/6

Month Sales(000)

Weighted

Moving

Average

1 4 NA

1n-tn2-t31-t2t11tAw+...+Aw+Aw+Aw=F

++

2 6 NA

3 5 NA4 3 31/6 = 5.1675 76

25/6 = 4.16732/6 = 5.333

Exponential Smoothing

Assumes the most recent observations have

the highest predictive value

gives more weight to recent time periods

= -t+1 t t tet

Ft+1 = Forecast value for time t+1

At = Actual value at time t

= Smoothing constant


9/17


Week (i)Demand (Ai)1 820

2 775Given the weekly demand

data what are the ex onential

Ft+1 = Ft + (At - Ft)

3 680

4 655

5 750

6 802

7 798

8 689

9 775

10

smoothing forecasts for

periods 2-10 using =0.10?

Assume F1=D1

Week Demand 0.1 0.6

1 820 820.00 820.00

2 775 820.00 820.00

Ft+1 = Ft + (At - Ft)

=

i Ai Fi


3 680 815.50 793.00

4 655 801.95 725.20

5 750 787.26 683.08

6 802 783.53 723.23

7 798 785.38 770.49

8 689 786.64 787.00

9 775 776.88 728.2010 776.69 756.28

F2 = F1+ (A1F1) =820+0.1(820820)

=820

Week Demand 0.1 0.6

1 820 820.00 820.00

2 775 820.00 820.00

Ft+1 = Ft + (At - Ft)

=

i Ai Fi


3 680 815.50 793.00

4 655 801.95 725.20

5 750 787.26 683.08

6 802 783.53 723.23

7 798 785.38 770.49

8 689 786.64 787.00

9 775 776.88 728.20

10 776.69 756.28

F3 = F2+ (A2F2) =820+.1(775820)

=815.5


10/17

Week Demand 0.1 0.61 820 820.00 820.00

2 775 820.00 820.00

Ft+1 = Ft + (At - Ft)

=

i Ai Fi


3 680 815.50 793.00

4 655 801.95 725.20

5 750 787.26 683.08

6 802 783.53 723.23

7 798 785.38 770.49

8 689 786.64 787.00

9 775 776.88 728.20

10 776.69 756.28

Week Demand 0.1 0.6

1 820 820.00 820.00

2 775 820.00 820.00

Ft+1 = Ft + (At - Ft)

= =

i Ai Fi


3 680 815.50 793.00

4 655 801.95 725.20

5 750 787.26 683.08

6 802 783.53 723.23

7 798 785.38 770.49

8 689 786.64 787.00

9 775 776.88 728.2010 776.69 756.28

2.Collecthistoricaldata

1.Identifythepurpose

3.Examinedata(plot)

4.Selectappropriatemodels

5.Computeforecastsforhistorical

o se a orecas ng e o

7b.Adjust

parameters

orselectnew

model

9.Monitorresults

8.Includequalitativeinformation

7a.Forecast overplanninghorizon

6.Isaccuracyacceptable?

Yes

No

30


11/17

Measures of Forecast Error

MAD =

A - F

n

t tt=1

n

et

a. MAD = Mean Absolute Deviation

b. MSE = Mean Squared Error ( )

n

F-A

=MSE 1=t

2

tt

Ideal values =0 (i.e., no forecasting error)

MSE=RMSEc. RMSE = Root Mean Squared Error

MAD =

A - F

n

t tt=1

n

FtAt

= 40

4=10

MEAN ABSOLUTE DEVIATION

(MAD)

Month Sales Forecast

1 220 n/a

2 250 255

3 210 205

4 300 320

5 325 315

55

20

10

|At Ft|

= 40

What are the Mean Squared Error (MSE) and

Root Mean Squared Error (RMSE) values?

FtAt

= 550

4=137.5

( )

n

F-A

=MSE

n

1=t

2

tt RMSE = 137.5

=11.73

Month Sales Forecast

1 220 n/a

2 250 255

3 210 205

4 300 320

5 325 315

55

20

10

|At Ft| (At Ft)2

2525

400

100

= 550


12/17

Measuring Bias in Forecast: Tracking signal

The tracking signal is a measure of how often our

estimations have been above or below the actual value. It

is used to decide when to re-evaluate using a model.

RSFETS = =

n

tt )F(ARSFE=

Positive tracking signal: most of the time actualvalues are above our forecasted values

Negative tracking signal: most of the time actualvalues are below our forecasted values

If TS > 4 or < -4, investigate!

Example of Tracking Signal

2/7/2014 35

Linear regression for Forecasting

Linear regression is based on

1. Fitting a straight line to data

2. Explaining the change in one variable through changes

in other variables.

By using linear regression, we try to explore which

independent variables affect the dependent variable

dependent variable = a + b (independent variable)


13/17


14/17

What does that mean?

Coke Sales

Average

Monthly

Temperature

Least Squares Method of Linear Regression

Then the line is defined by

bXaY +=

xbya =

22 xnx

yxnxyb

=

Month Advertising Sales X 2 XY

January 3 1 9.00 3.00

Februar 4 2 16.00 8.00

y = a + b X

Regression Example

=

22 xnx

yxnxyb xbya =

March 2 1 4.00 2.00

April 5 3 25.00 15.00

May 4 2 16.00 8.00

June 2 1 4.00 2.00

July

TOTAL 20 10 74 38


15/17

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 themanufacturing process

Case Study#1: HP desktop

43

Boardassembly

Hard diskAssembly

TestingPowersupply110 V

Boardassembly

Hard diskassembly

Testing

Powersupply110 V

Powersupply220 V

TestingPowersupply220 V

Delayed product

differentiation

Productpostponement

Case Study#1: HP desktop

Power

su l

Product Product

Month 110 V PC 220 V PC

1 10000 8000

44

Board

assembly

Hard disk

assemblyTesting

110 V

Power

supply

220 V

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

Forecast accuracy improves at different levels

110 V 220 V Total

Months Demand MA(4 ) Error Demand M A(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

(10000+14000+16000+12000)/4)

13000-18000

45

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%

(5000+1250+3750+1500+2250) / 6100-[(5+1.25+3.75+1.5+2.25)/(18+15+14+11+13+11)]100


16/17

Case Study#2: Aggregate Forecast

2/7/2014 (c) Dr. Ravi Shankar, AIT

(2008-09)

46

Learning Lesson of Case 1

What are the Learning

Lessons of this case

47

Study?

General Guiding Principles for Forecasting

G 40: Forecasts are more accurate for largergroups of items.

G 41: Forecasts are more accurate for shorterper o s o me.

G 42: Every forecast should include anestimate of error.

G 43: Before applying any forecasting method,the method should be tested and evaluated.


17/17

Product Redesign Helps Supply Chain

Competitiveness

G 44: Delayed product differentiation is the key tothis redesign

G 45: Similarly, forecast at the most upstream of thesupply chain (if possible)

49

: poss e, never use orecas n orma on athe lower levels. At the lower levels, decisionsshould be based on actual demand

3 session scm demand management 2014 iimr

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