planning forecasting part (b)

8/14/2019 Planning Forecasting Part (B)

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Planning and Forecasting(Part B)

Eng. Ahmed Bakhsh


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What is Forecasting?

Process of predicting a

future event

Underlying basis of

all business decisions

Production

Inventory Personnel

Facilities

Sales willbe $200

Million!


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Short-range forecastUp to 1 year; usually less than 3 months

Job scheduling, worker assignments

Medium-range forecast3 months to 3 years

Sales & production planning, budgeting

Long-range forecast3+ years

New product planning, facility location

Types of Forecasts by Time

Horizon


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Types of Forecasts

Economic forecastsAddress business cycle, e.g., inflation rate,

money supply etc.

Technological forecastsPredict rate of technological progress

Predict acceptance of new product

Demand forecastsPredict sales ofexistingproduct


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Planning and Forecasting

To predict/approximate what a certain future event or condition will be.

One can forecast:

Production levels

Technological developments

Needed manpower

Governmental regulations

Needed Funds

Training needs

Resource needs

Sale levels. The most critical information to forecast.

Forecasting


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Seven Steps in Forecasting Determine the use of the forecast

Select the items to be forecasted

Determine the time horizon of the forecast Select the forecasting model(s)

Gather the data

Make the forecast Validate and implement results


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Planning and Forecasting Forecasting

Two types of information to forecast:

Qualitative Information

Quantitative Information


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Qualitative Forecasting

Used when:

Past data cannot be used reliably to predict the future.Technological trendsRegulations

When no past data is available, usually becausethe situation is very new.Entry into new marketsDevelopment of new products


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Jury of executive opinionDelphi Method

Sales Force CompositeConsumer Market Survey (Users Expectations)

Methods

Qualitative Forecasting


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Involves small group of high-level managers

Group estimates demand by working together

Combines managerial experience with statisticalmodels

Relatively quick

Group-thinkdisadvantage

1995 Corel Corp.

Jury of Executive Opinion


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Delphi Method

Iterative group

process

3 types of people

Decision makers

Staff

Respondents

Reduces group-think

RespondentsRespondents

(Sales will be 45,

50, 55)

StaffStaff(What willsales be?

survey)

Decision MakersDecision Makers

(Sales?)

(Sales will be 50!)


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Sales Force Composite Each salesperson projects

his or her sales

Combined at district &national levels

Sales reps know

customers wants

Tends to be overly

optimistic

SalesSales

1995 Corel Corp.


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Consumer Market Survey Ask customers

about purchasing

plans

What consumers

say, and what they

actually do are often

different

Sometimes difficult

to answer

How many hours willyou use the Internet

next week?

How many hours willyou use the Internet

next week?

1995 Corel

Corp.

F i


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Quantitative Information

Used when data is tangible, can be used reliably topredict the future, and there is sufficient historicaldata upon which to base forecasts.

SalesProfitsProduction levels


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Quantitative Forecasting Methods

Quantitative

Forecasting

Linear

Regression

Associative

Models

ExponentialSmoothing

MovingAverage

Time Series

Models

TrendProjection


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Set of evenly spaced numerical data Obtained by observing response variable at regular time

periods

Forecast based only on past values Assumes that factors influencing past and present will

continue influence in future

Example

Year: 1998 1999 2000 2001 2002

Sales: 78.7 63.5 89.7 93.2 92.1

What is a Time Series?


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TrendTrend

SeasonalSeasonal

CyclicalCyclical

RandomRandom

Time Series Components


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Regular pattern of up & down fluctuations

Due to weather, customs etc.

Occurs within 1 year

Mo.,Qtr.

Respons

e

Summer

1984-1994 T/Maker Co.

Seasonal Component


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Common Seasonal PatternsPeriod ofPattern

SeasonLength

Number ofSeasons in

Pattern

Week Day 7

Month Week 4 4

Month Day 28 31

Year Quarter 4

Year Month 12

Year Week 52


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Repeating up & down movements

Due to interactions of factors influencing

economy

Usually 2-10 years duration

Mo., Qtr., Yr.Mo., Qtr., Yr.

ResponseResponse

Cycle

Cyclical Component


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Erratic, unsystematic, residual fluctuations

Due to random variation or unforeseen events

Union strike

Tornado

Short duration &

nonrepeating

1984-1994 T/Maker Co.

Random Component


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Product Demand Charted over 4Years with Trend and Seasonality

Year1

Year2

Year3

Year4

Seasonal peaks Trend component

Actualdemandline

Average

demandover fouryears

Demandf o

rproduc

tor

service

Randomvariation


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Naive Approach Assumes demand in next

period is the same as

demand in most recentperiod

e.g., If May sales were 48,

then June sales will be 48

Sometimes cost effective &efficient

1995 Corel Corp.


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Moving Average

Simple Moving Average Method

Weighted Moving Average Method

Pl i d F ti Forecasting


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Methods

Quantitative Forecasting

1. Simple Moving Average:

=+ =

n

t tn AnF11

1

Assumptions

Time series has a level and a random component onl

No TrendNo seasonal or cyclical variations

n=current value n+1 = forecast value for nextA=actual value


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Youre manager of a museum store that sells

historical replicas. You want to forecast sales

(000) for2003 using a 3-period moving

average.1998 4

1999 6

2000 5

2001 32002 7

1995 Corel Corp.

Moving Average Example


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Moving Average Solution

Time


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Time


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Time


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95 96 97 98 99 00Year

Sales

2

4

6

8 Actual

Forecast

Moving Average Graph



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Methods


2. Weighted Moving Average:

n=current value n+1 = forecast value for next

A=actual value w=weight value

==+ ==n

t t

n

t ttn wwhereAwF 111 1

Assumptions

Used when trend is presentOlder data usually less importantWeights based on intuitionOften lay between 0 & 1, & sum to 1.0



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Methods



==+ ==n

t t

n

t ttnwwhereAwF

1111

Example

Period Actual Value

1999 25002000 1500

2001 1000

2002 500

Weights are (0.1, 0.2, 0.3, 0.4)

respectively.

Find Sales for the year 2003?



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Methods



==+ ==n

t t

n

t ttnwwhereAwF

1111

Solution

Period Actual Value Weight

1999 2500 0.12000 1500 0.2

2001 1000 0.3

2002 500 0.4

F(2003) = 0.1*2500

+ 0.2*1500+ 0.3*1000+ 0.4*500

F(2003)= 1050


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Increasing n makes forecast less

sensitive to changes

Do not forecast trend well

Require much historical data

All data (in the simple moving

average technique) are weighted

equally and data which are too old to

be included are weighted by zero

Disadvantages of

Moving Average Methods



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Fn+1 = Forecast value

An = Actual value

= Smoothing constant

(Use for computing forecast)

)(1 nnnn FAFF +=+

nn FA )1( +=

Planning and Forecasting

Methods


3. Exponential Smoothing Equations:

Forecasting


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During the past 8 quarters, the Port of Baltimore has unloaded large quantities

of grain. ( = .10). The first quarter forecast was 175..

Quarter Actual

1 180

2 168

3 1594 175

5 190

6 205

7 180

8 182

9 ?

Exponential Smoothing Example

Find theforecast forthe 9th quarter.


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Fn+1 =

Fn + 0.1(

An -

Fn)QuarterQuarterActualActualForecast, FN+1

( == .10.10))

11 180 175.00 (Given)22 168168

33 159159

44 175175

55 190190

66 205205

175.00 +175.00 +

Exponential Smoothing Solution


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QuarterQuarterActuaActualForecast, FN+1

( == .10.10))

11 180180 175.00 (Given)175.00 (Given)

22 168168 175.00 +175.00 + .10.10((

33 159159

44 175175

55 190190

66 205205

Exponential Smoothing SolutionFn+1

= Fn

+ 0.1(An

- Fn)


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QuarterQuarterActualActualForecast,Forecast, FFN+1N+1

(( == .10.10))

11 180180 175.00 (Given)175.00 (Given)

22 168168 175.00 +175.00 + .10.10(180(180 --

33 159159

44 175175

55 190190

66 205205


Fn+1

= Fn

+ 0.1(An

- Fn)


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QuarterQuarterActualActualForecast, FN+1

( == .10.10))

11 180180 175.00 (Given)175.00 (Given)

22 168168 175.00 +175.00 + .10.10(180(180 - 175.00- 175.00))

33 159159

44 175175

55 190190

66 205205


= Fn

+ 0.1(An

- Fn)


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QuarterQuarter ActualActualForecast,Forecast, FFN+1N+1

(( == .10.10))

11 180180 175.00 (Given)175.00 (Given)

22 168168 175.00 +175.00 + .10.10(180(180 - 175.00- 175.00)) = 175.50= 175.50

33 159159

44 175175

55 190190

66 205205


= Fn

+ 0.1(An

- Fn)


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Fn+1

= Fn

+ 0.1(An

-

Fn)QuarterQuarterActualActual

Forecast, FN+1(== .10.10))

1 180 175.00 (Given)

22 168168 175.00 + .10(180 - 175.00) = 175.50175.00 + .10(180 - 175.00) = 175.50

33 159159 175.50175.50 ++ .10.10(168 -(168 - 175.50175.50)) = 174.75= 174.75

44 175175

55 190190

66 205205



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Fn+1

= Fn

+ 0.1(An

-

Fn)

QuarterActualForecast, FN+1

(= .10)

1995 180 175.00 (Given)

1996 168 175.00 + .10(180 - 175.00) = 175.50

1997 159 175.50 + .10(168 - 175.50) = 174.75

1998 175

1999 190

2000 205

174.75+.10(159 - 174.75)= 173.18



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Fn+1

= Fn

+ 0.1(An

-

Fn)

QuarterActualForecast, F

N+1

(= .10)

1 180 175.00 (Given)

2 168 175.00 + .10(180 - 175.00) = 175.50

3 159 175.50 + .10(168 - 175.50) = 174.75

4 175 174.75 + .10(159 - 174.75) = 173.18

5 190 173.18 +.10(175 - 173.18) = 173.36

6 205



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Fn+1 =

Fn + 0.1(

An -

Fn)

QuarterActualForecast, FN+1

(= .10)

1 180 175.00 (Given)2 168 175.00 + .10(180 - 175.00) = 175.50

3 159 175.50 + .10(168 - 175.50) = 174.75

4 175 174.75 + .10(159 - 174.75) = 173.18

5 190 173.18 + .10(175 - 173.18) = 173.36

6 205 173.36+ .10(190- 173.36) = 175.02



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Fn+1 =

Fn + 0.1(

An -

Fn)

TimeActualForecast, FN+1

(= .10)

4 175 174.75 + .10(159 - 174.75) = 173.185 190 173.18 + .10(175 - 173.18) = 173.36

6 205 173.36 + .10(190 - 173.36) = 175.02


7 180

8

175.02 +.10(205- 175.02) = 178.02

9


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Fn+1 =

Fn + 0.1(

An -

Fn)

TimeActualForecast, FN+1

(= .10)

4 175 174.75 + .10(159 - 174.75) =173.185 190 173.18 + .10(175 - 173.18) =173.366 205 173.36 + .10(190 - 173.36) =175.02


7 180

8

175.02 + .10(205 - 175.02) =

178.02

9 178.22 +.10(182-178.22) = 178.58

182

178.02 + .10(180 -178.02) = 178.22?

Impact of


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Impact of

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

1 2 3 4 5 6 7 8 9

Q u a r

ActualTonage

ActualForecast (0 .1)

Forecast (0 .5



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g g

Methods


3. Exponential Smoothing

Same data assumptions as Moving Average

It overcomes disadvantages of Moving Average.

Forecast for current period is found as theforecast for the last period plus a proportion ofthe error made in the last forecast.



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g g

Methods


3. Exponential Smoothing

No waiting period before reliable forecasts canbe calculated.

It is only required to retain three figures for

any forecast: the past forecast for currentperiod, the current actual, and the smoothingconstant.

The value of can be made to change oradapt to changed circumstances, such as forexample to make the series more sensitive torapidly changing data

Advantages:

planning forecasting part (b)

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