Forecasting

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Forecasting
K.Prasanthi
2
Forecasting

• Predict the next number in the pattern
• a) 3.7, 3.7, 3.7, 3.7, 3.7, ?
• b) 2.5, 4.5, 6.5, 8.5, 10.5, ?
• c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5,
  8.0, 6.5, ?

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Forecasting

• Predict the next number in the pattern
• a) 3.7, 3.7, 3.7, 3.7, 3.7,
• b) 2.5, 4.5, 6.5, 8.5, 10.5,
• c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5,
  8.0, 6.5,
• Process of predicting a future event based on
  historical data

3.7
12.5
9.0
4
What is Forecasting?

• Process of predicting a future event based on
  historical data
• Educated Guessing
• On the basis of all business decisions
• Production
• Inventory
• Personnel
• Facilities

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Importance of Forecasting
Departments throughout the organization depend on
forecasts to formulate and execute their plans.
Finance needs forecasts to project cash flows
and capital requirements. Human resources need
forecasts to anticipate hiring needs.
Production needs forecasts to plan production
levels, workforce, material requirements,
inventories, etc.
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Period of forecasting

• Short-range forecast
• Usually lt 3 months
• Job scheduling, worker assignments
• Medium-range forecast
• 3 months to 2 years
• Sales/production planning
• Long-range forecast
• gt 2 years
• New product planning

Detailed use of system
Design of system
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Qualitative Forecasting Methods
Qualitative
Forecasting
Models
Sales Force Composite
Delphi Method
Executive Judgement
Market Research/ Survey
Smoothing
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Qualitative Methods

• Briefly, the qualitative methods are
• Executive Judgment Opinion of a group of high
  level experts or managers
• Sales Force Composite Each regional salesperson
  provides his/her sales estimates. Those
  forecasts are then reviewed to make sure they are
  realistic. All regional forecasts are then pooled
  at the district and national levels to obtain an
  overall forecast.
• Market Research/Survey Solicits input from
  customers pertaining to their future purchasing
  plans. It involves the use of questionnaires,
  consumer panels and tests of new products and
  services.
• .

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Qualitative Methods

• Delphi Method As opposed to regular panels
  where the individuals involved are in direct
  communication, this method eliminates the effects
  of group potential dominance of the most vocal
  members. The group involves individuals from
  inside as well as outside the organization.
• Typically, the procedure consists of the
  following steps
• Each expert in the group makes his/her own
  forecasts in form of statements
• The coordinator collects all group statements and
  summarizes them
• The coordinator provides this summary and gives
  another set of questions to each
• group member including feedback as to the
  input of other experts.
• The above steps are repeated until a consensus is
  reached.
• .

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Quantitative Forecasting Methods
Quantitative
Forecasting
Regression
Time Series
Models
Models
2. Moving
3. Exponential
1. Naive
Average
Smoothing
a) simple b) weighted
a) level b) trend c) seasonality
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Quantitative Forecasting Methods
Quantitative
Time Series
Models
Models
2. Moving
3. Exponential
1. Naive
Average
Smoothing
a) simple b) weighted
a) level b) trend c) seasonality
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Time Series Models

• Try to predict the future based on past data
• Assume that factors influencing the past will
  continue to influence the future

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1. Naive Approach

• Demand in next period is the same as demand in
  most recent period
• May sales 48 ?
• Usually not good

June forecast 48
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2a. Simple Moving Average

• Assumes an average is a good estimator of future
  behavior
• Used if little or no trend
• Used for smoothing

Ft1 Forecast for the upcoming period, t1 n
Number of periods to be averaged A t Actual
occurrence in period t
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2a. Simple Moving Average

• Youre manager in Amazons electronics
  department. You want to forecast ipod sales for
  months 4-6 using a 3-period moving average.

Sales (000)
Month
1
4
2
6
3
5
4
?
5
?
6
?
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2a. Simple Moving Average
Youre manager in Amazons electronics
department. You want to forecast ipod sales for
months 4-6 using a 3-period moving average.
Sales (000)
Moving Average
Month
(n3)
NA
1
4
NA
2
6
3
5
NA
(465)/35
4
?
5
?
6
?
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What if ipod sales were actually 3 in month 4
2a. Simple Moving Average
Sales (000)
Moving Average
Month
(n3)
NA
1
4
NA
2
6
NA
3
5
5
4
3
?
5
?
6
?
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Forecast for Month 5?
2a. Simple Moving Average
Sales (000)
Moving Average
Month
(n3)
NA
1
4
NA
2
6
NA
3
5
5
4
3
(653)/34.667
5
?
6
?
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Actual Demand for Month 5 7
2a. Simple Moving Average
Sales (000)
Moving Average
Month
(n3)
NA
1
4
NA
2
6
NA
3
5
5
4
3
4.667
?
5
7
6
?
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Forecast for Month 6?
2a. Simple Moving Average
Sales (000)
Moving Average
Month
(n3)
NA
1
4
NA
2
6
NA
3
5
5
4
3
4.667
5
7
(537)/35
6
?
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2b. Weighted Moving Average

• Gives more emphasis to recent data
• Weights
• decrease for older data
• sum to 1.0

Simple moving average models weight all
previous periods equally
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2b. Weighted Moving Average 3/6, 2/6, 1/6
Weighted Moving Average
Month
Sales
(000)
NA
1
4
NA
2
6
NA
3
5
31/6 5.167
4
?
5
?
6
?
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2b. Weighted Moving Average 3/6, 2/6, 1/6
Weighted Moving Average
Month
Sales
(000)
NA
1
4
NA
2
6
NA
3
5
31/6 5.167
4
3
25/6 4.167
5
7
6
32/6 5.333
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3a. Exponential Smoothing

• Assumes the most recent observations have the
  highest predictive value
• gives more weight to recent time periods

Ft1 Ft a(At - Ft)
et

• Need initial
• forecast Ft
• to start.

Ft1 Forecast value for time t1 At
Actual value at time t ? Smoothing constant
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3a. Exponential Smoothing Example 1
Ft1 Ft a(At - Ft)
i
Ai

• Given the weekly demand
• data what are the exponential
• smoothing forecasts for
• periods 2-10 using a0.10?
• Assume F1D1

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3a. Exponential Smoothing Example 1
Ft1 Ft a(At - Ft)
i
Ai
Fi
a
F2 F1 a(A1F1)
820.1(820820)
820
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3a. Exponential Smoothing Example 1
Ft1 Ft a(At - Ft)
i
Ai
Fi
a
F3 F2 a(A2F2)
820.1(775820)
815.5
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3a. Exponential Smoothing Example 1
Ft1 Ft a(At - Ft)
i
Ai
Fi
a
This process continues through week 10
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3a. Exponential Smoothing Example 1
Ft1 Ft a(At - Ft)
i
Ai
Fi
a
a
What if the a constant equals 0.6
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3a. Exponential Smoothing Example 2
Ft1 Ft a(At - Ft)
i
Ai
Fi
a
a
What if the a constant equals 0.6
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3a. Exponential Smoothing Example 3
Company A, a personal computer producer purchases
generic parts and assembles them to final
product. Even though most of the orders require
customization, they have many common components.
Thus, managers of Company A need a good forecast
of demand so that they can purchase computer
parts accordingly to minimize inventory cost
while meeting acceptable service level. Demand
data for its computers for the past 5 months is
given in the following table.
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3a. Exponential Smoothing Example 3
Ft1 Ft a(At - Ft)
i
Ai
Fi
a
a
What if the a constant equals 0.5
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3a. Exponential Smoothing

• How to choose a
• depends on the emphasis you want to place on the
  most recent data
• Increasing a makes forecast more sensitive to
  recent data

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Forecast Effects ofSmoothing Constant ?
Ft1 Ft ? (At - Ft)
Ft1 ? At ?(1- ?) At - 1 ?(1- ?)2At - 2
...
or
w1
w2
w3
8.1
10
9
90
9
0.9
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To Use a Forecasting Method

• Collect historical data
• Select a model
• Moving average methods
• Select n (number of periods)
• For weighted moving average select weights
• Exponential smoothing
• Select ?
• Selections should produce a good forecast

but what is a good forecast?
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A Good Forecast

• Has a small error
• Error Demand - Forecast

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Measures of Forecast Error
et

1. MAD Mean Absolute Deviation

1. MSE Mean Squared Error

1. RMSE Root Mean Squared Error

• Ideal values 0 (i.e., no forecasting error)

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MAD Example
40 4
10
What is the MAD value given the forecast values
in the table below?
Ft
At
At Ft
Month
Sales
Forecast
1
220
n/a
2
250
255
5
5
3
210
205
4
300
320
20
5
325
315
10
40
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MSE/RMSE Example
550 4
137.5
What is the MSE value?
RMSE
v137.5
11.73
Ft
At
At Ft
(At Ft)2
Month
Sales
Forecast
1
220
n/a
2
250
255
5
25
5
25
3
210
205
4
300
320
20
400
5
325
315
10
100
550
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Measures of Error
1. Mean Absolute Deviation (MAD)
t At Ft et et et2
Jan 120 100 20 20 400
Feb 90 106 256
Mar 101 102
April 91 101
May 115 98
June 83 103
84
14
6
-16
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2a. Mean Squared Error (MSE)
1
1
-1



100
-10
10
1,446
241
17
17
289
6
-20
20
400
2b. Root Mean Squared Error (RMSE)
-10
84
1,446
An accurate forecasting system will have small
MAD, MSE and RMSE ideally equal to zero. A
large error may indicate that either the
forecasting method used or the parameters such as
a used in the method are wrong. Note In the
above, n is the number of periods, which is 6 in
our example
SQRT(241) 15.52
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Forecast Bias

• How can we tell if a forecast has a positive or
  negative bias?
• TS Tracking Signal
• Good tracking signal has low values

MAD
30
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Quantitative Forecasting Methods
Quantitative
Forecasting
Regression
Time Series
Models
Models
2. Moving
3. Exponential
1. Naive
Average
Smoothing
a) simple b) weighted
a) level b) trend c) seasonality
43
Exponential Smoothing (continued)

• We looked at using exponential smoothing to
  forecast demand with only random variations

Ft1 Ft a (At - Ft)
Ft1 Ft a At a Ft
Ft1 a At (1-a) Ft
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Exponential Smoothing (continued)

• We looked at using exponential smoothing to
  forecast demand with only random variations
• What if demand varies due to randomness and
  trend?
• What if we have trend and seasonality in the
  data?

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Regression Analysis as a Method for Forecasting

• Regression analysis takes advantage of the
  relationship between two variables. Demand is
  then forecasted based on the knowledge of this
  relationship and for the given value of the
  related variable.
• Ex Sale of Tires (Y), Sale of Autos (X) are
  obviously related
• If we analyze the past data of these two
  variables and establish a relationship between
  them, we may use that relationship to forecast
  the sales of tires given the sales of
  automobiles.
• The simplest form of the relationship is, of
  course, linear, hence it is referred to as a
  regression line.

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Formulas
y a b x where,
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Regression Example
y a b X
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General Guiding Principles for Forecasting

• 1. Forecasts are more accurate for larger groups
  of items.
• 2. Forecasts are more accurate for shorter
  periods of time.
• 3. Every forecast should include an estimate of
  error.
• Before applying any forecasting method, the total
  system should be understood.
• Before applying any forecasting method, the
  method should be tested and evaluated.
• 6. Be aware of people they can prove you wrong
  very easily in forecasting

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FOR JULY 2nd MONDAY

• READ THE CHAPTERS ON
• Forecasting
• Product and service design