5. Agenda
Meaning of Sales Forecasting
Why Study forecasting ?
Types of Forecasts
Categorization of Sales Forecasting
Facts in Forecasting
Limitations of Demand Forecasting
Steps in Forecasting
Sales forecasting Technique
Forecast Accuracy

6. “Contrive” “Before”
[the fact]
‫؟؟‬

7. ‫؟؟‬

8. Sales Mantra
“Hope is not a sales Strategy”

9. Meaning of Demand Forecasting

10. Meaning of Demand Forecasting
Demand forecasting is the scientific and
analytical estimation of demand for a
product (service) for a particular period of
time.

12. Why Study
forecasting ?
Setting Sales Targets, Pricing policies,
establishing controls and incentives.
Allows managers to plan personnel,
operations of purchasing & finance for better
control over wastes inefficiency and conflicts.

13. Why Study
forecasting ?
Reduce the cost for purchasing raw
material , Increased revenue .
Improved customer service
(efficiency)
Effective forecasting builds stability
in operations.
Measure as a barometer of the
future health of a company

14. Why Study
forecasting ?
The ability to plan for production avoid the
problem of over-production & problem of short
supply…………. Sales Maximization
The ability to identify the pattern or trend of
sales Knowing when and how much to buy
…….…Better Market Positioning

15. Types of Forecasts
Economic forecasts
– Address the future business conditions
(e.g., inflation rate, money supply, etc.)
Technological forecasts
– Predict the rate of technological progress
– Predict acceptance of new products
Demand forecasts
– Predict sales of existing products

16. Categorization of Demand Forecasting
: (3 months to 2 years):
for production planning, purchasing, and
distribution. Sales & production planning, budgeting
: (2 years and more)
for capacity planning, and investment decisions
New product planning, facility location

17. Facts in Forecasting
Main assumption: Past pattern repeats itself
into the future.
Forecasts are rarely perfect: Don't expect
forecasts to be exactly equal to the actual
data.
The science and art of forecasting try to
minimize, but not to eliminate, forecast
errors.

18. Facts in Forecasting
Forecasts for a group of products are usually
more accurate than these for individual
products.
A good forecast is usually more than a single
number.
The longer the forecasting horizon, the less
accurate the forecasts will be .

19. Limitations of Demand Forecasting

20. Limitations of Demand Forecasting

21. Qualities of Good Forecasting
1) Simple
2) Economy of time
3) Economy of money
4) Accuracy
5) Reliability

22. Steps in Forecasting
Determine the purpose of the forecast
Select the items to be forecasted
Gather the data
Determine the time horizon of the
forecast
Select the forecasting model(s)
Make the forecast
Validate and implement results

23. Sales forecasting
Process
Setting Goals Gathering Analysis of
Forecasting data data
Evaluating of Choosing The
forecasting Best Model
Forecasting
outcomes For
Forecasting

24. Forecasting Technique
Qualitative Analysis Quantitative Analysis
Customer Sales Force
Survey Time Series Causal
Composite
Analysis Analysis
Executive
Naïve
Delphi
Opinion Method approach
Moving Weighted Regression
Test Average Moving Analysis
Marketing
Least Exponential
squares Smoothing

25. Sales forecasting Technique
It is generally recommended to use a
combination of quantitative and
qualitative techniques.

26. Forecasting Techniques
Principles of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.
© 2005 Thomson Business and Professional Publishing

27. Qualitative (Subjective) Methods

28. 1- Consumers’ Opinion Survey
(Buyer’s expectation Method )

29. 1- Consumers’ Opinion Survey
(Buyer’s expectation Method )
Advantages :
Simple to administer and comprehend.
Forecasting Reveals general attitude and
feeling about products from potential users

30. 1- Consumers’ Opinion Survey
(Buyer’s expectation Method )
Advantages :
Technique is very effective to
determine demand for a new
product when no past data
available.
Suitable for short term decisions
regarding product and promotion.
Principles of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.
© 2005 Thomson Business and Professional Publishing

31. 1- Consumers’ Opinion Survey :
(Buyer’s expectation Method)

32. 2- Sales Force Composite Method
 Salespersons are
in direct contact
with the customers.
 Eachsalespersons are asked about
estimated sales targets in their
respective sales territories in a given
period of time.

33. 2- Sales Force Composite Method
 These forecasts are then reviewed to
ensure they are realistic, then
combined at the district and national
levels to reach an overall forecast.
 In this method sales people put their
future sales estimate either alone or in
consultation with sales manager.

34. 2- Sales Force Composite Method

35. 2- Sales Force Composite Method

36. 2- Sales Force Composite Method
Principles of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.
© 2005 Thomson Business and Professional Publishing

37. 2- Sales Force Composite Method

38. 3- Jury of Executive Opinion:

39. 3- Jury of Executive Opinion:

40. 3- Jury of Executive Opinion:

41. 3- Jury of Executive Opinion:

42. 4- Experts’ Opinion Method
Delphi Technique:
It includes successive sessions of
brainstorming among highly specialized
experts.
Answers of questions in the first round are
summarized and form the base of the second
round

43. 4- Experts’ Opinion Method
Delphi Technique:
Conclusions, insights, and expectations of
the experts are evaluated by the entire group
resulting in shared more structured and less
biased estimate of the future
There are three different types of
participants in the Delphi process:
decision makers,
 staff personnel,
 and respondents.

44. 4- Delphi Technique:
assist the decision makers by preparing,
distributing, collecting, and summarizing a series
of questionnaires and survey results.
are a group of people whose judgments are
valued.
This group provides inputs to the decision
makers before the forecast is made.

45. 4- Experts’ Opinion Method
Delphi Technique:
?(Sales)
Evaluate responses
and make Decision
What will sales be?)
!(Sales will be 50)
(Administering survey
)People who can make
valuable judgments
(Sales will be 45, 50, 55

46. 4- Experts’ Opinion Method
Delphi Technique:

47. 4- Experts’ Opinion Method
Delphi Technique:

48. 5- Test Marketing

49. 5- Test Marketing

50. Forecasting Technique
Qualitative Analysis Quantitative Analysis
Customer Sales Force
Survey Time Series Causal
Composite
Analysis Analysis
Executive
Naïve
Delphi
Opinion Method approach
Moving Weighted Regression
Test Average Moving Analysis
Marketing
Least Exponential
squares Smoothing

51. Forecasting Techniques
Quantitative forecasting
 Uses mathematical models and historical data
to make forecasts.
 Used when situation is stable & historical data
exist
Existing products
Time series models are the most frequently used
among all the forecasting models.
Principles of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.
© 2005 Thomson Business and Professional Publishing

52. Quantitative forecasting
Time Series Casual
Models Models
Only independent assumes that
variable is the time one or more
based on the factors other
assumption that the than time predict
future is an extension future demand.
of the past .

53. 53
s
IntroductionSale
Growth
Maturity
Product Life Cycle
Decline
Time

54. BCG Growth – Share Matrix

55. What is a Time Series?
a collection of data recorded over a period of time
(weekly, monthly, quarterly)
an analysis of its history can be used by
management to make current decisions and plans
based on long-term forecasting
Forecast based only on past values Assumes that
factors influencing past and present will continue
influence in future
Example
Year: 2007 2008 2009 2010 2011
Sales: 78.7 63.5 89.7 93.2 92.1

56. Demand for product or service
Time Series
2007 2008 2009 2010
201 Time
1

57. Time Series Components
Seasonal Random

58. Time Series Pattern: Secular Trend
 change occurring
consistently over a long
time and is relatively
smooth in its path.
either increasing or
decreasing
 Forecasting methods:
linear trend projection,
exponential smoothing

59. Time Series Pattern: Seasonal
Patterns of change in
a time series within a
year which tend to
repeat each year
Due to weather,
customs, etc.
Occurs within 1 year
Forecasting methods:
exponential
smoothing with trend

60. have a tendency to
recur in a few years
usually repeat every
two-five years.
 Repeating up & down
movements
Due to interactions of
factors influencing
economy

61. Time Series Pattern: Stationary
or Irregular Variation or Random events
 have no trend of
occurrence hence they
create random variation
in the series.
(due to unexpected or
unpredictable events)
 Short duration & non-
repeating
 Forecasting methods:
naive, moving average,
exponential smoothing

62. Product Demand Charted over 4 Years
with Trend and Seasonality
Seasonal peaks Trend component
Demand for product or
Actual
demand line
service
Average
Random demand over
variation four years
Year Year Year Year
1 2 3 4

63. Forecasting Technique
Qualitative Analysis Quantitative Analysis
Customer Sales Force
Survey Time Series Causal
Composite
Analysis Analysis
Executive
Naïve
Delphi
Opinion Method approach
Moving Weighted Regression
Test Average Moving Analysis
Marketing
Least Exponential
squares Smoothing

64. 1- Naive Approach

65.  It is convenient for long term periods
Year Sales % change
2007 527
2008 639 0.2
2009 467 - 0.3
Actual Sales
2010 795 0.7 Previous
2011 853 0.1 Sales
Forecast for 2013
2012 985 0.2 = 985 × 1.2 = 1182

66. 3- Moving Average Method
Can be defined as the summation of
demands of total periods divided by the total
number of periods.
Useful if we can assume that market
demands will stay fairly steady over time.
It is convenient for short term periods
MA =
∑ Demand in Previous n Periods
n

67. 3- Moving Average…… Example

68. 3- Moving Average Solution
Moving
Actual Moving Total
Time Average
Sales (n=3) (n=3)
Jan. 4
Feb. 6
Mar. 5
April 3 4+6+5=15 15/3 = 5
May 7
Jun.

69. 3- Moving Average Solution
Moving
Actual Moving Total
Time Average
Sales (n=3)
(n=3)
Jan. 4
Feb. 6
Mar. 5
April 3 4+6+5=15 15/3 = 5
May 7 6+5+3=14 14/3 = 4.7
Jun.

70. 3- Moving Average Solution
Moving Moving
Actual
Time Total Average
sales
(n=3) (n=3)
Jan. 4
Feb. 6
Mar. 5
April 3 4+6+5=15 15/3=5.0
May 7 6+5+3=14 14/3=4.7
Jun 5+3+7=15 15/3=5.0

71. 4- Weighted Moving Average
Weights are used to give more values to
recent value
This makes the techniques more responsive
to changes because latter periods may be
more heavily waited
Most recent observation receives the most
weight, and the weight decreases for older
data values

72. 4- Weighted Moving Average
Last month ago 3
Two month ago 2 Sum of the weights
Three month ago 1 6
Month Actual sales Three month moving average
Jan 10
Feb 12
March 13
× (13 + ×(12
April
1) +×(10 12.1
16 3) 2) =
6 7

73. 4- Weighted Moving Average
Actual
Month Three months moving average
sales
Jan 10
Feb 12
March 13
April 16 (3× 13)+) 2× 12)+) 1× 10)/6 = 12.17
May 19 (3× 16)+) 2× 13)+) 1× 12)/6 = 14.33
June 23 (3× 19)+) 2× 16)+) 1× 13)/6 = 17
July 26 (3× 23)+) 2× 19)+) 1× 16)/6 =20.5

74. 5- Trend Projection Method

75. Linear Equations
Y
dependent variable value
Y = bX + a
Change
b = S lo p e in Y
C h a n g e in X
a == value rofe (Y) when (X) equals zero
a Y -in te c p t
independent variable X

79. 6- Exponential Smoothing Method
α = σ Smoothing constant
α = σ Smoothing constant At-1= Actual demand for
At-1= Actual demand for
(0 to 1)
(0 to 1) the previous period
the previous period
Ftt = Ft-1 + α(At-1 -- Ft-1))
F = Ft-1 + α(At-1 Ft-1
Ftt= forecast for this period
F = forecast for this period Ft-1 = forecast for the
Ft-1 = forecast for the
previous period
previous period

80. 6- Exponential Smoothing Method
α = σ Smoothing
α = σ Smoothing At-1= Actual sales 2012
At-1= Actual sales 2012
constant (0 to 1)
constant (0 to 1)
Ftt = Ft-1 + α(At-1 -- Ft-1))
F = Ft-1 + α(At-1 Ft-1
Ftt= forecast for 2013 Ft-1 = forecast 2012
F = forecast for 2013 Ft-1 = forecast 2012

81. 6- Exponential Smoothing……Example
© 1995 Corel Corp.

82. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, F t
Forecast
Time Actual ( α = .10)
2008 180 175.00 (Given)
2009 168 175.00 +
2010 159
2011 175
2012 190
2013 NA

83. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, F t
Forecast
Time Actual ( α = .10)
2008 180 175.00 (Given)
2009 168 175.00 + .10(
2010 159
2011 175
2012 190
2013 NA

84. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, Ft
Time Actual (α = .10)
2008 180 175.00 (Given)
2009 168 175.00 + .10(180 -
2010 159
2011 175
2012 190
2013 NA
MGMT 6020 Forecast

85. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, Ft
Time Actual (α = .10)
2008 180 175.00 (Given)
2009 168 175.00 + .10(180 - 175.00)
2010 159
2011 175
2012 190
2013 NA
MGMT 6020 Forecast

86. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, Ft
Time Actual (α = .10)
2008 180 175.00 (Given)
2009 168 175.00 + .10(180 - 175.00) = 175.50
2010 159
2011 175
2012 190
2013 NA
MGMT 6020 Forecast

87. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, F t
Time Actual (α = .10)
2008 180 175.00 (Given)
2009 168 175.00 + .10(180 - 175.00) = 175.50
2010 159 175.50 + .10(168 - 175.50) = 174.75
2011 175
2012 190
2013 NA

88. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, F t
Forecast
Time Actual
(α = .10)
2008 180 175.00 (Given)
2009 168 175.00 + .10(180 - 175.00) = 175.50
2010 159 175.50 + .10(168 - 175.50) = 174.75
2011 175 174.75 + .10(159 - 174.75) = 173.18
2012 190
2013 NA

89. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, F t
Time Actual
(α = .10)
2008 180 175.00 (Given)
2009 168 175.00 + .10(180 - 175.00) = 175.50
2010 159 175.50 + .10(168 - 175.50) = 174.75
2011 175 174.75 + .10(159 - 174.75) = 173.18
2012 190 173.18 + .10(175 - 173.18) = 173.36
2013 NA

90. 6- Exponential Smoothing Solution
Ft = Ft-1 + α(At-1 - Ft-1)
Forecast, F t
Forecast
Time Actual
( α = .10)
2008 180 175.00 (Given)
2009 168 175.00 + .10(180 - 175.00) = 175.50
2010 159 175.50 + .10(168 - 175.50) = 174.75
2011 175 174.75 + .10(159 - 174.75) = 173.18
2012 190 173.18 + .10(175 - 173.18) = 173.36
2013 NA 173.36 + .10(190 - 173.36) = 175.02

91. 6- Exponential Smoothing Method

92. Forecasting Technique
Qualitative Analysis Quantitative Analysis
Customer Sales Force
Survey Time Series Causal
Composite
Analysis Analysis
Executive
Naïve
Delphi
Opinion Method approach
Moving Weighted Regression
Test Average Moving Analysis
Marketing
Least Exponential
squares Smoothing

93. Causal Method
Usually consider several variable that are related
to the quantity being predicted
 once the related variable are found, statistical
models are then built and used to forecast
Example: PC sales forecasts (dependent variable)
could be correlated to advertising budget,
promotions, prices, competitors prices
(independent variables)

94. Regression Analysis Method

95. Regression Analysis Method

96. forecast error
defined as the difference between actual
quantity and the forecast
et = A t - Ft
et = forecast At = actual Ft = forecast
error for demand for
Period t for Period t
Period t
The smaller the forecast error, the more
accurate the forecast.

97. Values of Dependent Variable forecast error
Actual Deviation7
observation
Deviation5 Deviation6
Deviation3
Deviation4
Deviation1
)error( Deviation2
Trend line
Time period

98. Forecast Accuracy
Several measures of forecasting accuracy
larger the value the larger the forecast error
Mean absolute deviation (MAD)
Sum of absolute values of individual
forecast errors / number of periods of
data
The larger the MAD, the less the
accurate the resulting model
MAD of 0 indicates the forecast exactly
predicted demand.

99. Forecast Accuracy
Mean squared error (MSE)
Average of the squared differences between the
forecasted and observed values
Mean absolute percentage error (MAPE)
How many Percent the forecast is off from the
actual data

100. Forecast Accuracy
Period )Sales(A Forecast E [E] E2 E]/A]
1 1600 1650 -50 50 2500 0.0313
2 2200 2010 190 190 36100 0.0864
3 2000 2200 -200 200 40000 0.1000
4 1600 1580 20 20 400 0.0125
5 2500 2480 20 20 400 0.0080
6 3500 3520 -20 20 400 0.0057
7 3300 3310 -10 10 100 0.0030
8 3200 3200 0 0 0 0.0000
9 3900 3850 50 50 2500 0.0128
10 4700 4720 -20 20 400 0.0043
10 -20 580 82800 0.2639
MAD=58 & MSE=8280 & MAPE=2.64%

101. Forecast Accuracy
Period )Sales(A Forecast E [E] E2 E]/A]
1 1600 1650 -50 50 2500 0.0313
2 2200 2010 190 190 36100 0.0864
3 2000 2200 -200 200 40000 0.1000
4 1600 1580 20 20 400 0.0125
5 2500 ∑ [E ]
2480 20 20 400 0.0080
MAD=
n
6 3500 3520 -20 20 400 0.0057
7 3300 3310 -10 10 100 0.0030
8 3200 3200 0 0 0 0.0000
9 3900 3850 50 50 2500 0.0128
10 4700 4720 -20 20 400 0.0043
10 -20 580 8280 0.2639
0
MAD=58

102. Forecast Accuracy
Period )Sales(A Forecast E [E] E2 E]/A]
1 1600 1650 -50 50 2500 0.0313
2 2200 2010 190 190 36100 0.0864
3 2000 2200 -200 200 40000 0.1000
4 1600 1580 20 20 400 0.0125
5 2500 ∑ [E2]
2480 20 20 400 0.0080
MSE =
n
6 3500 3520 -20 20 400 0.0057
7 3300 3310 -10 10 100 0.0030
8 3200 3200 0 0 0 0.0000
9 3900 3850 50 50 2500 0.0128
10 4700 4720 -20 20 400 0.0043
10 -20 580 82800 0.2639
MSE=8280

103. Forecast Accuracy
Period )Sales(A Forecast E [E] E2 E]/A]
1 1600 1650 -50 50 2500 0.0313
2 2200 2010 190 190 36100 0.0864
3 2000 2200 -200 200 40000 0.1000
4 1600 1580 20 20 400 0.0125
5 2500 ∑ 2480 20
100
20 400 0.0080
6 MAPE=
3500
E]/A] ×
n
3520 -20 20 400 0.0057
7
8
3300
3200
3310
3200
-10
0
10
0
% 100
0
0.0030
0.0000
9 3900 3850 50 50 2500 0.0128
10 4700 4720 -20 20 400 0.0043
10 -20 580 82800 0.2639
MAPE=2.64%

104. Excel Chart Methods