3 forecating

7/30/2019 3 forecating

1/37

Forecasting


2/37

Demand ManagementTo coordinate and control all thesources of demand so that the

productive system can be usedefficiently and the product delivered ontime.

Sources of demand

Dependent Demand Indept. Demand


3/37

What is Forecasting?Process of predicting a future event

Underlying basis ofall business decisions

Production

Inventory

Personnel

Facilities

Sales willbe $200Million!


4/37

Types of forecast by time

horizonShort-range forecast

usually less than 3 months

Job scheduling, worker assignmentsMedium-range forecast

3 months to 2 years

Sales & production planning, budgeting

Long-range forecast2+ years

New product planning, facility location


5/37

Seven Steps in Forecasting

SystemDetermine the use of forecast.

Select the items to be forecasted.

Developing the time horizon for forecast.Select the forecasting model.

Gather the data needed to make the forecast.

Make the forecast.Validate and implement the forecast.


6/37

Forecasting ApproachesQualitative Methods Quantitative methods

Used when situation is vague

& little data exist

New products

New technology

Involves intuition, experience

Used when situation is

stable & historical data exist

Existing products

Current technology

Involves mathematical

techniques


7/37

Qualitative Methods

Qualitative

Market

Research

Historical

Analogy

Delphi Method

Grass roots

Panel

Consensus


8/37

Delphi Method1.Choose the experts to participate. There should

be a variety of knowledgeable people in differentareas.

2.Through a questionnaire, obtain forecasts from

all participants.3. Summarize the results and redistribute them to

the participants along with appropriate newquestions.

4. Summarize again,refining forecasts andconditions , and again develop new questions.

5.Repeat step4 if necessary.distribute the finalresults to all.


9/37

Quantitative Methods

Time-series models Associative models

Simple

moving

average

Weighted

Moving

average

LinearRegression

Exponential

Smoothing


10/37

Time series Components

Trend

Seasonal

Cyclical

Random


11/37

Trend ComponentPersistent, overall upward or downwardpattern

Due to population, technology etc.

Several years duration

Mo., Qtr., Yr.

Response

1984-1994 T/Maker Co.


12/37

Seasonal Component

Regular pattern of up & downfluctuations

Due to weather, customs etc.

Occurs within 1 year

Mo., Qtr.

Response

Summer

1984-1994 T/Maker Co.


13/37

Cyclic Component

Repeating up & down movementsDue to interactions of factorsinfluencing economy

Usually 2-10 years duration

Mo., Qtr., Yr.

Response

Cycle


14/37

Irregular Component

Erratic, unsystematic, residual fluctuationsDue to random variation or unforeseen

events

Union strike

Tsunami

Floods/Earthquake

Short duration &

nonrepeating


15/37

General Time Series models

Any observed value in a time series isthe product (or sum) of time seriescomponents

Multiplicative model

Yi= Ti Si Ci Ri

Additive model

Yi= Ti+ Si+ Ci+ Ri


16/37

Simple Moving Average formula

This model assumes an average is agood estimator of future behavior.

The formula for MA is:

Ft = At-1+At-2+At-3+..+At-nn

Ft: forecast for the coming period.

n: No. of periods to be averaged.

At-1: actual occurrence in the past period.


17/37

Moving Average Example

Youre manager of a museum store thatsells historical replicas. You want to

forecast sales (000) for 2003using a 3-

period moving average.1998 4

1999 6

2000 52001 3

2002 7 1995 Corel Corp.


18/37

Moving Average Solution

Time ResponseYi

MovingTotal(n=3)

MovingAverage

(n=3)

1998 4 NA NA1999 6 NA NA

2000 5 NA NA2001 3 4+6+5=15 15/3 = 5

2002 72003 NA


19/37

Moving Average solution

Time ResponseYi

MovingTotal

(n=3)

MovingAverage

(n=3)1998 4 NA NA

1999 6 NA NA

2000 5 NA NA

2001 3 4+6+5=15 15/3 = 52002 7 6+5+3=14 14/3=4 2/32003 NA


20/37

Moving Average solution

Time ResponseYi

MovingTotal(n=3)

MovingAverage

(n=3)1998 4 NA NA

1999 6 NA NA

2000 5 NA NA

2001 3 4+6+5=15 15/3=5.02002 7 6+5+3=14 14/3=4.72003 NA 5+3+7=15 15/3=5.0


21/37

Moving Average Graph

95 96 97 98 99 00

Year

Sales

2

4

6

8 Actual

Forecast


22/37

Weighted Moving average Formula

While the moving average formula implies anequal weight being placed on each value thatis being averaged, the weighted movingaverage permits an unequal weighting on

prior time periods.

The formula for WMA is:

Ft

= w1

At-1

+w2

At-2

+.+wn

At-n

wi

w t: weight given to time period t occurrence

wi = 1(Weights must add to one)


23/37

NumericalA department store may find that in a fourmonth period, the best forecast is derived by

using 40% of the actual sales for the mostrecent month, 30% of two months ago, 20%of three months ago and 10% of four monthsago.If actual sales experience was

Month1 Mon 2 Mon 3 Mon 4 Mon 5100 90 105 95 ?


24/37

Exponential Smoothing formula

The equation for exponential smoothing is:

Ft : the exponentially smoothed forecast forperiod t

Ft-1 : the exponentially smoothed forecast for

prior period.At-1 : the actual demand in the prior period.

: smoothing or weighting constant.

Ft

= Ft-1 + (At-1 - Ft-1)


25/37

Exponential Smoothing Example

During the past 6 quarters, the Port ofBaltimore has unloaded large quantities

of grain. ( = .10). The first quarterforecast was 175.

QuarterActual

1 180

2 168

3 1594 175

5 190

6 205

Find the forecast for

the 7th quarter?


26/37

Exponential Smoothing Solution

Ft= Ft-1 + 0.1(At-1 - Ft-1)

Quarter Actual Forecast, Ft

( = 0.1)1 180 175(Given)

2 168 175+0.1(180-175) 174.50

3 159 174.50+0.1(168-174.50) 174.75

4 175 173.185 190 173.36

6 205 175.02

7 ? 175.02+0.1(205-175.02)=178.02


27/37

Forecast Effects of SmoothingConstant

Weights

Prior Period

2 periods ago

(1 - )

3 periods ago

(1 - )2=

= 0.10

= 0.90

Ft = At- 1 + (1- ) At- 2 + (1- )2At- 3 + ...

10% 9% 8.1%

90% 9% 0.9%


28/37

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9

Quarter

Actua

lTonage

ActualForecast (0.1)

Forecast 0.5


29/37

Linear RegressionUsed for forecasting linear trend line.

Functional relationship between two or

more correlated variables.

Used to predict one variable given theother.

Estimated by least squares method

Minimizes sum of squared errors


30/37

Linear Regression Equations

Equation:

Slope:

Y-intercept:

ii bxaY =

22

i

n

1i

ii

n

1i

xnx

yxnyxb

=

=

=

xbya =


31/37

Using a Trend LineYear Demand

1997 74

1998 791999 80

2000 90

2001 1052002 142

2003 122

The demand for

electrical power at

N.Y.Edison over the

years 1997 2003 isgiven at the left. Find

the overall trend.


32/37

Finding a Trend LineYear Time

PeriodPower

Demandx2 xy

1997 1 74 1 74

1998 2 79 4 1581999 3 80 9 240

2000 4 90 16 360

2001 5 105 25 525

2002 6 142 36 852

2003 7 122 49 854

x=28 y=692 x2=140 xy=3,063


33/37

The Trend Line Equation

megawatts151.5610.54(9)56.702005inDemand

megawatts141.0210.54(8)56.702004inDemand

56.7010.54(4)-98.86xb-ya

10.5428295

(7)(4)14086)(7)(4)(98.3,063

xnxyxn-xyb

98.867

692

n

yy4

7

28

n

xx

222

==

==

===

==

=

=

======


34/37

Actual and Trend Forecast


35/37

Nodel Construction Company renovates oldhomes in West Bloomfield.The company has

found that its dollar volume of renovation workdepends on West Bloomfield area payroll.Dataof past 6 years is given:

Local payroll(in millions) Nodels sales

1 2.0

3 3.0

4 2.5

2 2.01 2.0

7 3.5

Nodel mgt. wants to predict sales thru method ofleast squares.


36/37

Forecast errorsSeek to minimize the Mean Absolute Deviation (MAD)

If: Forecast error = demand - forecast

Then:

n

errorsforecast=MAD n

errorsforecast

=MAD


37/37

Standard deviation and

tracking signalS.D = 1.25 MAD

TS = RSFEMAD

RSFE: The running sum of the forecast

errors.MAD: The average of all the forecasted

errors.

3 forecating

Documents