Transcript forecasting 2005.ppt

Linkages
• How much we are going to sell is obviously
important to marketing
• Forecasts help us to plan investments - or to
determine if an investment is a good idea
• Forecasts tell us if we will have to hire new people
and or train our existing people in new skills
• Technological forecasts might indicate the need to
change our MIS function
Forecasting 1
Forecasts as part of planning
• How much demand we are going to have leads to a number
of other questions
– large demand for standard products: line flow
– demand for custom products: jumbled flow
– demand leads to capacity
– demand indicates when we schedule work
– etc.
• In other words a forecast is one of the first things
we need when planning – for the long term and the
short term.
Forecasting 2
Why do forecasts matter ?
• People: If we do a bad job forecasting demand we
may not have the right number (or type) of people
on hand.
• Capacity: If we under forecast we will not be able
to make enough stuff (lost sales) over forecasting
will result in expensive wasted capacity.
• Supply chain: Our suppliers are also dependent on
our forecasts:
– What if we have them build stuff based on an
erroneously high forecast?
Forecasting 3
Characteristics of forecasts
• Short term: Less than a year
– quantitative
– can be very accurate
– dis-aggregated
• Long term: More than a year
– often very qualitative
– much harder to be accurate
– generally aggregated
Forecasting 4
Types of forecasts
• Economic: What is happening in the world,
country, state, and locality. Aggregated across
companies and usually industries.
– ISM index
– The federal reserve
• Technological: changes in technology that may
change products and / or processes
– BW survey of research labs
• Demand: Sales of our company’s products - often
driven (partially) by economic and technological.
Forecasting 5
Quantitative verses Qualitative
• When numbers do not exist and or are inaccurate
we can use qualitative methods (long term
forecasts especially)
– delphi methods
– market research
– the “gut”
• Most people want to use numbers
– why?
– is this always best?
• See readings on methods people do choose- and
what would be best.
Forecasting 6
Forecasting demand
• There are 5 components of demand:
–
–
–
–
–
Average demand – not in book
Trends
Cyclicality
Seasonality
Random factors
• What should we be able to forecast ?
Forecasting 7
Trend
Sales of Dallas Cowboys Paraphernalia
Volume
Year
1999
2000
2001
2002 2003 projected
Forecasting 8
Seasonality
Beverage sales at the 6 pac shop
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Forecasting 9
Seasonality 2
Umbrella sales
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Forecasting 10
Cyclicality
The business cycle
– Where are we in the business cycle?
– to forecast the end of a period of growth what
signs would you look for?
• What do you think Greenspan looks for?
Forecasting 11
Determining the quality of a forecast
Forecast error = demand - forecast
negative errors indicate ?
Mean Absolute Deviation (MAD)
errors

MAD 
n
Mean percentage deviation (MAPE)
 n  errori 




*
100%
 i 1  demand 

i


MAPE   
n
Forecasting 12
Determining the quality of a forecast 2
• Why don’t we use the average deviation?
• What does the MAPE tell us that the MAD
does not ?
– can we compare the MADS for two different
products ?
– can we use MAD to compare the same
forecasting method in a variety of situations ?
• We also want to examine Bias
Forecasting 13
MAD / MAPE example
Forecasting 14
A quick aside
• The forecasting tools we are going to use
are generally basic and fairly simple.
– See the articles I placed on the web- this is what
people use
– Regression is “to fancy” for many managers
• Our goal is to find the method that best fits
our pattern of demand- no one right tool
Forecasting 15
Actual forecasting tools
• The simplest method: the naive forecast
– this period’s demand = last period’s demand
– when is this acceptable ?
• Time series methods: future demand is
predicted from past (historical) demand.
– moving averages
• simple and weighted
– exponential smoothing
Forecasting 16
Moving averages
• A simple tool to predict demand when it is safe to
assume that over time demand is fairly stable
(change is slow).
• A 3 period moving average:
• A five period moving average:

Dt 1  Dt 2  Dt 3  Dt 4  Dt 5 
F
t
5
Forecasting 17
Moving average example
Period
Demand
3 period
MA
5 period
MA
1
15
2
12
3
13
4
17
13.33
5
19
14
6
18
16.33
15.2
7
20
18
16.2
Forecasting 18
Weighted moving averages
• Moving averages work fine when the world
is fairly stable - but what if our world is
changing ?
• Weighted moving averages (WMA) - place
more weight on recent events (why) .
• WMA = (Σ (weight period n) (demand in period
n)) / Σ weights
• Determining weights is an art - generally do
not weight most recent period more than
50%.
Forecasting 19
AWMA example:
Weights 5,3,2
Period
1
2
3
4
5
6
7
8
Demand
15
18
26
35
32
36
38
40
Forecast
((15*2)+(18*3)+(26*5)) / 10 =21.4
28.9
31.7
34.6
36.2
Forecasting 20
Exponential smoothing
• Exponential smoothing is a very popular
(and simple) form of the weighted moving
average.
• Basic form:
Ft  Ft 1   ( Et 1 )
• What happens as the smoothing constant 
increases ?
Forecasting 21
Exponential smoothing: examples
• smoothing constant = .2
period
1
2
3
demand
25
24
forecast
21
21+.2(4) = 21.8
21.8+.2(2.2) = 22.24
• smoothing constant = .5
period
1
2
3
demand
25
24
forecast
21
21+.5(4) = 23
23+.5(1) = 23.5
Forecasting 22
Seasonality
• Because seasonality is a pattern we can predict it
using indices.
• For example:
yearly demand = 800 units
indices:
–
–
–
–
spring = .85 summer = 1.46 fall = .76 winter = .93
F spring = 200 * .85 = 170
F summer = 200 * 1.46 = 292
f fall = 200 * .76 = 152
f winter = 200 * .93 = 186
Forecasting 23
Determining Indices
Quarter
Sales
95
Sales
96
Sales
97
quarter average index
average quarter
1 400
375
425
400
500
.80
2 300
280
320
300
500
.60
3 575
600
625
600
500
1.2
4 700
650
750
700
500
1.4
Forecasting 24
More indices stuff
• The sum of the indices should = the number
of seasons.
• Formula for the index :
average demand specific season
average demand all seasons
Forecasting 25
Regression Models
• The basic regression model
– F = constant + b1X1 + b2X2
– b1 is a constant
– X1 is an independent variable
– You can of course use only 1 independent
variable in your model- or more than 2
(sometimes many more than 2)
Forecasting 26
Some obvious uses of regression
• We can use regression to forecast when we have a trend in
the data.
– If the trend is the major source of change in the data
might be able to use a simple regression model where
time is our only independent variable
• Ft = constant + b (time period)
• We might also make the season an independent variable
• We can obviously include just about anything else in the
model that makes sense
• demand for ice cream - might include temperature
• demand for MBA classes - unemployment rate
Forecasting 27
Other issues
• Double exponential smoothing
– Fancy way to try and cope with trends – works
well when there is a one time change – not as
well when the trend is always going up / down
• Focused forecasting
– Economist
Forecasting 28
Book problems you should be able
to do
• 4.2, 4.4, 4.6, 4.8, 4.10, 4.26, 4.28
Forecasting 29