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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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Planning and Forecasting Forecasting
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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Planning and Forecasting Forecasting
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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Planning and Forecasting Forecasting
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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Planning and Forecasting Forecasting
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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Moving Average Solution
Time
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Moving Average Solution
Time
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95 96 97 98 99 00Year
Sales
2
4
6
8 Actual
Forecast
Moving Average Graph
Planning and Forecasting Forecasting
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Planning and Forecasting Forecasting
Methods
Quantitative Forecasting
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
Planning and Forecasting Forecasting
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Planning and Forecasting Forecasting
Methods
Quantitative Forecasting
2. Weighted Moving Average:
==+ ==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?
Planning and Forecasting Forecasting
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Planning and Forecasting Forecasting
Methods
Quantitative Forecasting
2. Weighted Moving Average:
==+ ==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
Planning and Forecasting Forecasting
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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
Quantitative Forecasting
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
Exponential Smoothing Solution
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
Exponential Smoothing SolutionFn+1
= 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
Exponential Smoothing SolutionFn+1
= 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
Exponential Smoothing Solution
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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
Exponential Smoothing Solution
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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
Exponential Smoothing Solution
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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
Exponential Smoothing Solution
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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
Exponential Smoothing Solution
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
Exponential Smoothing Solution
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
Planning and Forecasting Forecasting
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g g
Methods
Quantitative Forecasting
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.
Planning and Forecasting Forecasting
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g g
Methods
Quantitative Forecasting
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: