BIS Application
Chapter two
Forecasting
Forecasting
Forecasting is the process of extrapolating the past
into the future
Forecasting is something that organization have to
do if they are to plan for future. Many forecasts
attempt to use past date in order to identify short,
medium or long term trends, and to use these
patterns to project the current position into the
future.
Backcasting: method of evaluating forecasting
techniques by applying them to historical data and
comparing the forecast to the actual data
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Forecasting
Why Forecasting?
Characteristics of Forecasts
Forecasts are usually wrong or seldom correct
Aggregate forecasts are usually more accurate
Less accurate further into the future
Assumptions of Forecasting Models
Information (data) about the past is available
The pattern of the past will continue into the
future.
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The forecasting approach to
forecasting
•Starts with gathering and recording information about the
situation
•Entering the data into the worksheet,
•Creating graphs
•The data and graphs are examined visually to get some
understanding of the situation (judgmental phase)
•Developing hypotheses and models
•Trying alternative forecasting approaches and doing what if
analysis to check if the resulting forecast fits the data
Forecasting Models
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Demand Forecast A Deviation A
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
11.8
6.3
9.5
5.3
10.1
7
11.3
7.3
9.5
5
10.7
6
7.1
11.8
6.3
9.5
5.3
10.1
7
11.3
7.3
9.5
5
10.7
6
4.7
5.5
3.2
4.2
4.8
3.1
4.3
4
2.2
4.5
5.7
4.7
6
7
6
Deviations
5
4
3
2
1
0
Jan
Forecasting Models
Feb Mar
Apr May Jun
Jul
Aug Sep Oct
Nov Dec
Jan
5
Forecasting Approaches
1- Qualitative Forecasting
Forecasting based on experience, judgment,
and knowledge
2- Quantitative Forecasting
Forecasting based on data and models
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Forecasting Approaches
Judgmental/Qualitative
Market survey
Quantitative models
Time Series
Causal
Expert opinion
Decision conferencing
Data cleaning
Moving average
Exponential smoothing
Regression
Curve fitting
Trend projection
Econometric
Data adjustment
Seasonal indexes
Environmental factors
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Quantitative Forecasting
Time Series Models:
Sales1999
Sales1998
Sales1997
……
Time Series
Model
Year 2000
Sales
Casual Models:
Price
Population
Advertising
……
Forecasting Models
Causal
Model
Year 2000
Sales
8
Time series model
Is based on the hypothesis that the future can be predicted by
analyzing historical data samples. The time series model have the
following type , which can be classifies as shown below:
Forecasting directly from the data value (non seasonal)
-Moving average
-Exponential smoothing
Forecasting by identifying patterns in the past data (seasonal)
(Chapter 3)
-Trend projections
-Seasonal influences
-Cyclical influences
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Time series model
The Time series model can be also classified as
Non-seasonal Model

Trend

Moving average

Exponential smoothing
Seasonal Model

Seasonal Decomposition

Cyclical influence
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Causal Models
(Chapter 3)
Causal forecasting seeks to identify specific
cause-effect
relationships that will influence the pattern of future data. Causes
appear as independent variables, and effects as dependent ,
response variables in forecasting models.
Independent variable
Dependent, response variable
Price
demand
Decrease in population
decrease in demand
Number of teenager
demand for jeans
The issue is to determine the approximate functional
relationships, the model, and the parameter of the model that
relate the input(independent) and output(dependent) variables.
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Causal Models
(Chapter 3)
Regression analysis
Curve Fitting: Simple Linear Regression
One Independent Variable (X) is used to predict one
Dependent Variable (Y): Y = a + b X
Find the regression line with Excel



Use Function:
a = INTERCEPT(Y range; X range)
b = SLOPE(Y range; X range)
Use Solver
Use Excel’s Tools | Data Analysis | Regression
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Causal Models
(Chapter 3)
Curve Fitting: Multiple Regression
Two or more independent variables are used to
predict the dependent variable:
Y = b0 + b1X1 + b2X2 + … + bpXp
Use Excel’s Tools | Data Analysis | Regression
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Evaluation of Forecasting Model
To judge how well a forecasting model, or indeed any forecast, fit the past
observation , both precision and bias must be considered.
a- Measuring the precision of a forecasting model:
There are four possible measures used to evaluate precision of forecasting
systems, each based on the error or deviation between the forecasted and
actual values: Average of the deviation, MAD, MAS, MAPE
b - Measuring the bias of a forecasting model:
The bias of a forecasting model is examined on the basis of the spread of a
set of data which can be measured by its variance, which depends on the
sum of squares of the differences between the values and their mean. The
more of the spread that is accounted for by the fitted model , the more
precise the fit of the model to the data.
R2 – used only for curve fitting model such as regression
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Evaluation of Forecasting Model
The arithmetic mean of the errors (the average
deviation )
BIAS 
 (Actual - Forecast)   Error
n
n
n is the number of forecast errors
Excel: =AVERAGE (error range)
Period
1
2
3
4
Demad Forecast
Deviation
33
36
3 C2-B2
37
29
-8 C3-B4
32
41
9 C4-B4
35
30
-5 C5-B4
-0.25 AVERAGE(E2:E5)
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Evaluation of Forecasting Model
Mean Absolute Deviation - MAD
BIAS 
 (Actual - Forecast)   Error
n
n
No direct Excel function to calculate MAD
Period
Demad
1
2
3
4
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37
32
35
Forecast
36
29
41
30
Absollute
Deviation
3
8
9
5
6.25
ABS(C2-B2)
ABS(C3-B4)
ABS(C4-B4)
ABS(C5-B4)
AVERAGE(E2:E5)
16
Evaluation of Forecasting Model
Mean Square Error - MSE
(Actual - Forecast)

MSE 
n
2
(Error)


2
n
Excel: =SUMSQ(error range)/COUNT(error range)
Period
Demad
1
2
3
4
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37
32
35
Forecast
36
29
41
30
Squared
Deviation
9
64
81
25
6.68954
(C2-B2)^2
(C3-B3)^3
(C4-B4)^4
(C5-B5)^5
SQRT(AVERAGE(E2:E5))
17
Evaluation of Forecasting Model
Mean Absolute Percentage Error - MAPE
| Actual - Forecast |
*100%

Actual
MAPE 
n
Period
Demad
1
2
3
4
Forecasting Models
33
37
32
35
Forecast
36
29
41
30
Squared
Deviation
9.09%
21.62%
28.13%
14.29%
18.28%
ABS((C2-B2)/B2)
ABS((C3-B3)/B3)
ABS((C4-B4)/B4)
ABS((C5-B5)/B5)
AVERAGE(E2:E5)
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Which of the measure of forecast accuracy
should be used?
 Straight average is not used because positive and
negative deviations cancel out.
 The most popular measures are MAD and MSE.
 The problem with the MAD is that it varies
according to how big the number are.
 MSE is preferred because it is supported by
theory, and because of its computational
efficiency.
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Which of the measure of forecast accuracy
should be used?
 The ratio of MAD or MSE to the average
demand which describes the relative percentage
of error, may be used
 MAPE is not often used.
 In general, the lower the error measure (BIAS,
MAD, MSE) or the higher the R2, the better the
forecasting model
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Good Fit – Bad Forecast
As its discussed that neither MAD nor MSE
gives an accurate indication of validity of
forecast. Thus, judgment must be used. Raw
data sample should always be subjected to
managerial judgment, and analyzed and adjusted
before formal quantitative techniques can be
applied.
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a- Dirty Data
Outlier: may result from simple data entry errors, or they may
be correct but atypical observed values (ex can occur in time
periods when the product was just introduced or about to be
phased out).
So experienced analyst are well aware that raw data sample
may not be clear.
Demand data with an outlier
P
100
90
80
70
60
50
40
30
20
10
0
Jan
Feb
Forecasting Models
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
22
b- Causal data adjustment
Before quantitative analysis is performed, the historical data
sample needs to be examined from the point of view of causeand-effect relationships.
A multitude of causes may affect the patterns in data sample :
-
The data sample before a particular year may not be applicable
because:
- Economic conditions have changed
- The product line was changed
-
Data for a particular year may not be applicable because:
- There was an extraordinary marketing effort
- A natural disaster prevented demand from occurring
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c- Illusory (misleading) patterns
The meaning of a :good fit” is subject to
interpretation, so before a forecast is accepted for
action, quantitative techniques must be augmented
by such judgmental approaches as decision
conferencing and expert consultations.
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To prepare a valid forecast, the following factors
that influence the forecasting model must be
examine:
-
Company actions
-
Competitors actions
-
Industry demand
-
Market share
-
Company sales
-
Company costs
-
Environmental factors
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Time series forecasting model
Time Series Model Building

Historical data collection

Data plotting (time series plot)

Forecasting model building

Evaluation and selection of model

Forecasting with the final selected model
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Components of A Time Series

Trend: long term overall up or down
movement

Seasonality: periodic pattern repeating every
year

Cycles: up & down movement repeating over
long time frame

Random Variations: random movements follow no
pattern
Forecasting
Models
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Components of A Time Series
Cycle
Trend
Random
movement
Time
Seasonal
pattern
Time
Forecasting Models
Demand
Time
Trend with
seasonal pattern
Time
29
First : Forecasting directly from the data
value : Moving average
-the forecast is the mean of the last n observation. The
choice of n is up to the manager making the forecast
-If n is too large then the forecast is slow to respond to
change
-If n is too small then the forecast will be overinfluenced by chance variations
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First : Forecasting directly from the
data value : Moving average
-This approach is considered as a “quick and dirty”
approach for forecasting
-This approach can be used where a large number
of forecasting needed to be made quickly, for
example in a stock control system where next
week’s demand for every item needs to be forecast
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Mounth
Demand
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Moving
Avarage
Forecast
6
5
5
1.63
1.95
7.5
2.49
6.18
9.18
5.24
8.3
2.72
7.43
7.49
9.58
8.02
4.13
12
5.33
3.88
2.86
3.69
3.98
5.39
5.95
6.87
7.57
5.42
6.15
5.88
8.17
8.36
AVARAGE(B3:B5)
11
13
AVARAGE(B4:B6)
AVARAGE(B5:B7)
AVARAGE(B6:B8)
AVARAGE(B7:B9)
=
=
=
=
=
=
=
=
=
Demand
10
8
6
4
2
Forecast
0
1
2
Forecasting Models
3
4
5
6
7
8
9
10
12
14
15
16
17
32
Longer-period moving averages (larger n)
react to actual changes more slowly
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First : Forecasting directly from the
data value : Exponentional smoothing
-it gives weight to all past observations, in such a way that the
most resent observation has the most influence on the forecast,
and older observation always has less influence than the more
recent one.
-It is only necessary to store two values (the last actual
observation and the last forecast, plus the value of the
smoothing constant) in order to make the next period’s forecast.
-Smoothing constant () the proportion of the different between
the actual value and the forecast.
-F2 = *D1 +(1- )*F1
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First : Forecasting directly from the
data value : Exponentional smoothing
•Alpha (smoothing constant) must set between 0 and 1.
Normally the value of the smoothing constant is chosen to
lie in the range 0.1 to 0.3.
•Typically, a value closer to 0 is used for demand that is
changing slowly, and a value closer to 1 for demand that
is changing more rapidly.
•There is no way to calculate F1 because each forecast is
based on the previous forecasts.
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First : Forecasting directly from the
data value : Exponential smoothing
How to select smoothing constant 
•Sensitivity analysis is an analysis used to test how
sensitive the forecast is to the change in alpha or
smoothing constant.
•A general rule for selecting alpha is to perform
scenario analysis and pick the value that produces a
reasonable value for the MAD and a forecast that is
reasonably close to the actual demand.
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Trend-Adjusted Exponential
Smoothing
With trend-adjusted exponential smoothing, the trend is
calculated and included in the forecast. This allows the forecast
to be smoothed without losing the trend.
Trend-adjusted exponential smoothing requires two parameters:
the alpha value used by exponential smoothing and beta value
used to control how the trend component enters the model. Both
values must be between 0 and 1.
The formula to calculate the forecast component is :
F2 = FiT1+ *(D1-FiT1)
The formula to calculate the trend component is
T2 = T1 +  *  *(D1-FiT1)
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Optimizing Trend-Adjust Exponential
Smoothing
Optimizing alpha and beta with trend-adjusted
exponential smoothing has a marginal impact.
To find the optimum value for alpha and beta:
First the original value of alpha and beta will be used
in the forecasting model. Once the spreadsheet is
ready, Solver is used to vary alpha and beta in order to
minimize the MAD.
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