Purpose of Forecasting Main

7/31/2019 Purpose of Forecasting Main

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Purpose of Forecasting

• Forecasting is an attempt to determine in

advance the most likely outcome of an

uncertain variable.

• Logistics requirements to be predicted include

customer demand ,raw material prices,labour

costs and lead times.



Nature of Demand

• No Forecasting method is deemed to besuperior then others in every respect .In orderto generate a forecast demand must show

some sign of regularity.

Lumpy Demand

• When demand is lumpy or irregular there is somuch randomness in the demand pattern thatno reliable prediction can be made.







Role of Logistician in Generating

Forecasts

• Medium and Long term forecasting are hardly

given to logisticians now. Most frequently they

are taken up by the marketing managers who

try to influence their demand through

advertisements and sales. Logisticians look

into producing short term forecasts.



Forecast Model

“Model spread” system

push distribution to DCs

Total

Company

Productline

Product class

SKUs

Central DC (safety

stock)Branch/regional DCs

SKUs



Derived v/s Independent demand

• Demand for certain items cannot be related to the demand of

some other items.

• However demand for some products can be derived from the

requirements of some other items.

Principles of Forecasting

Steps in Forecasting



Qualitative Methods

• Jury of execution opinion

• Sales force assessment

• Delphi Method

• Market Research

• Naive approach



Quantitative Methods

• Casual Methods

• Time Series Extrapolation Methods



Time Series Extrapolation Methods

• Trend

• Cyclical Variation

• Seasonal Variation

• Residual Variation



DEMAND = T X S X C X R

Where :

T = TrendS = Season

C = Cycles

R = Random Variables



Moving Averages Method

Moving Average= Σ Demand in previous n periods

n

Where n=the number of periods in the moving average



Month Actual Washingmachine sales,units

Three-month moving average

Jan 10

Feb 12Mar 13 (10+12+13)/3=11.67

Apr 16 (12+13+16)/3=13.67

May 19 (13+16+19)/3=16

June 23 (16+19+23)/3=19.33July 26 (19+23+26)/3=22.67

Aug 30 (23+26+30)/3=26.33

Sept 28 (26+30+28)/3=28

Oct 18 (30+28+18)/3=25.33Nov 16 (28+18+16)/3=20.67

Dec 14



Weighted Moving Average

Weighted moving

Average =Σ{(weight for period n) X

(Demand in period n)}ΣWeights



Weights Applied Period

3 Last Month

2 2 mths ago

1 3 mths ago

6 SUM OFWEIGHTS

M h A l W hi 3 h i h d i



Month Actual Washingmachine sales,Units

3-mth weighted moving average

Jan 10

Feb 12Mar 13 (1x10+2x12+3x13)/6=12.16

Apr 16 (1x12+2x13+3x16)/6=14.33

May 19 (1x13+2x16+3x19)/6=17

June 23 (1x16+2x19+3x23)/6=20.5July 26 (1x19+2x23+3x26)/6=23.83

Aug 30 (1x23+2x26+3x30)/6=27.5

Sept 28 (1x26+2x30+3x28)/6=28.33

Oct 18 (1x30+2x28+3x18)/6=23.33Nov 16 (1x28+2x18+3x16)/6=18.67

Dec 14



Exponential Smoothing

Ft = Ft-1 +α(At-1-Ft-1)

Where Ft

=New forecastFt-1 = Previous forecast

α = Smoothing Constant

At-1 = Previous period’s actual demand



Week Sales Exponential Smoothing Forcast. Α=0.2

1 17 17

2 21 17+0.2(17-17)=17

3 19 17+0.2(21-17)=17.8

4 23 17.8+0.2(19-17.8)=18.04

5 18 18.04+0.2(23-18.04)=19.03

6 16 19.03+0.2(18-19.03)=18.83

7 20 18.83+0.2(16-18.83)=18.26

8 18 18.26+0.2(20-18.26)=18.61

9 22 18.61+0.2(18-18.61)=18.49

10 20 18.49+0.2(22-18.49)=19.19

11 15 19.19+0.2(20-19.19)=19.35

12 22 19.35+0.2(22-19.35)=18.48



MEASURES OF FORECAST ERROR

1.Mean Absolute Deviation (MAD)

MAD= Σ|Forecast Errors|

n



Week Actual Sales Rounded Forecast AbsoluteDeviation

1 17 17 0

2 21 17 4

3 19 18 1

4 23 18 5

5 18 19 1

6 16 19 3

7 20 18 2

8 18 19 1

9 22 18 4

10 20 19 1

11 15 19 4

12 22 18 4

30



MAD= Σ|Forecast Errors| n

=3012

= 2.5



Mean Squared Error

MSE=Σ(Forecast Errors)

n

2



The Least Square Method

y=a+bx

Where;y= Compound value of the variable to be predicted

a= y-axis intercept

b= slope of the regression linex= independent variable

Also; a=y-bx



(Σxy – nxy)

(Σx –nx )

The slope b can be found by

2b=



Year Timeperiod(x)

Electricalpower

demand

(y)

X^2 xy

1990 1 74 1 74

1991 2 79 4 158

1992 3 80 9 240

1993 4 90 16 360

1994 5 105 25 5251995 6 142 36 852

1996 7 122 49 854

Σx=28 Σy=692 Σx^2=140 Σxy=3063



x=Σx =28 = 4 y=Σy = 692 = 98.86

n 7 n 7

b=(Σxy – nxy)

(Σx –nx^2 )

b=295 =10.54

28

Therefore; a=y-bx

=98.86-10.54(4)=56.7



Therefore to predict fpr 1997;

y=a+bx

=56.70+10.54(8)

=141.02



Seasonal Variations in Data



Monthly sales of IBM notebook computers at Bangaluru are shown below for 1999-

2000

Month SalesDemand

-1999

SalesDemand

-2000

Average 1999-2000

AverageMonthly

Demand

AverageSeasonal

Index

Jan 80 100 90 94 0.957

Feb 75 85 80 94 0.851

Mar 80 90 85 94 0.904

Apr 90 110 100 94 1.064

May 115 131 123 94 1.309

June 110 120 115 94 1.223

July 100 110 105 94 1.117

Aug 90 110 100 94 1.064Sept 85 95 90 94 0.957

Oct 75 85 80 94 0.851

Nov 75 85 80 94 0.851

Dec 80 80 80 94 0.851



Total average demand = 1128

Average Monthly Demand= 1128

12

=94

Seasonal Index= Average1999-2000 Demand

Average Monthly Demand



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Purpose of Forecasting Main