BUS 173: Lecture 10

```BUS 173: Lecture 10
Outline
 What is a forecast?
 Why do we need forecasting?
 What are the common tools of forecasting?
 Basic Tools
 Plain Average
 Regression
 Assessing Forecasts
Forecasts – Example
Suppose you want to start a new garments factory. Your
product will be woolen sweaters, which you will be
exporting to Sweden and Norway. After deciding on the
capital expenditure, loans, where to establish the factory
you about the possible future demands of the woolen
already been established garments manufacturers, and
they provide you with the demand for sweaters for the past
16 months, shown in the next slide:
Months
Demand (in thousand)
December ‘12
25
January ’13
29
February ’13
31
March ’13
30
April ’13
26
May ’13
24
June ’13
20
July ’13
17
August ’13
16
September ’13
20
October ’13
23
November ’13
28
December ’13
31
January ’14
34
February ’14
37
March ‘14
33
Some Questions
 How do you use this data to forecast for the next seven
periods?
 How do you determine that your forecasting method is
accurate?
 How do you make sure that you are accounting for data
seasonality and cyclical data?
Forecast - Logic
 The logic behind forecasting
 No model is the ideal model.
 Each model will depend on the situation.
 The accuracy of each model will vary from time to time.
 There will always be a certain degree of error involved.
Simple Forecasting Tools (1)
 The Average
 Take all the past data and find out the average value from
them
 For our example, average is:
 26.5
 This means that for the next 7 months, the demand will be
26.5 on average
Assessing Forecasts
 Step 1
 Looking into the trend of data
 Step 2
 Understanding the forecast method to use
 Checking the reliability of the method used
 Step 3
 Generating forecasts for existing data and checking for
deviation
Step 1 – Looking at the data
40
37
35
34
31
30
31
30
28
26
25
24
Axis Title
25
29
33
20
23
20
20
17
15
16
10
5
0
1
2
3
4
5
6
7
Axis
8 Title
9
10
11
12
13
14
15
16
 Mean absolute deviation - MAD
 Step 1 – Difference: Forecasted Data – Actual Data
 Step 2 – Absolute Difference: Convert ALL values to
positive values
 Step 3 – Average of the Absolute Differences
Deviation Checks - MAPE
 Mean absolute percentage error - MAPE
 Step 1 – Percentage Difference: (Actual Data –
Forecasted Data)/Actual Data
 Step 2 – Absolute Percentage Difference: Convert ALL
values to positive values
 Step 3 – Average of the Percentage Differences
Deviation Checks – MSE
 Also called Mean Standard Error - MSE
 Step 1 – Difference: Forecasted Data – Actual Data
 Step 2 – Difference Square: (Forecasted Data – Actual
Data)^2
 Step 3 – Average of Difference Squares
Decision Rule
 Whichever forecasting method has the lowest MAD/
MAPE/ MSE is the most appropriate forecasting for a
particular scenario
 Keep in mind
 NO ONE FORECASTING METHOD IS THE BEST
End of Presentation
THANK YOU
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