This document discusses sales forecasting and provides examples to illustrate key concepts like seasonality, time series patterns, and trend lines. It contains tests to predict sales based on trend line patterns. Key points made include: 1) Sales forecasts must consider seasonality, time series patterns from historical data, and trends; 2) Trend lines can help forecast future sales if the trend continues; and 3) The reliability of a trend-based forecast increases as the trend line's R-squared value approaches 1.

1. Sales Forecasting for all !

2. The Creation of Adam : a painting by Michelangelo,
on Sistine Chapel's ceiling

3. The God appeared to be an anatomically accurate picture of
the human brain.

4. Forecasting is Science as well as an Art

5. But, Forecasts can go wrong!

6. This is the reality!

7. 640K ought to be enough for anybody…
Bill Gates on memory, 1981
Even Bill Gates can go wrong!

8. Sometimes , the Forecast is always right…
all it needs is a bit of common sense

9. Here is a Forecast that is always right…

10. The Paradox of Forecasting
Month’s sale is
difficult to predict
Long range plan is
easy to make

11. So, What is a Forecast ?

12. It’s not Crystal gazing

13. The premise : The near future is going to be like the past

14. Now , a small test……

15. Test 1 : What is the Sales Forecast for Dec ?
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

16. Is your answer 1,200 ?
That’s Right…

17. Test 2 : What is the Sales Forecast for Dec?
2000
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1700
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500
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

18. Is your answer 900 ?
Right again! Good going…

19. Test 3: What is the Sales Forecast for Dec ?
100
200
300 300
400
500
600 600
700
800
900
0
100
200
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500
600
700
800
900
1000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

20. Is your answer 900 ?
Keep it up!

21. Test 4 : What is the Sales Forecast for Dec ?
100
50
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100
300
150
400
200
500
250
600
0
100
200
300
400
500
600
700
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

22. Is your answer 300 ?
That’s Right! Keep it up!

23. Test 5 : What is the Sales Forecast for July ?
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Jan-11
Mar-11
May-11
Jul-11
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Nov-11
Jan-12
Mar-12
May-12
Jul-12
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Nov-12
Jan-13
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Sep-13
Nov-13
Jan-14
Mar-14
May-14
Jul-14
Sep-14
Nov-14
Jan-15
Mar-15
May-15
Jul-15

24. Is your answer 600?
That’s Great!

25. Test 6 : What is the Sales Forecast for Dec ?
300
900
0
100
200
300
400
500
600
700
800
900
1000
Oct Nov Dec

26. Is your answer 600-700-800 ?
Not sure ….Difficult .. isn't it ?

27. What is the learning ?
More the data, its easy to forecast !
If there is a pattern…its easy to forecast !
If there is seasonality…its easy to forecast !

28. Forecasting : 3 Simple Factors
Use these 3 factors to
forecast the sales
Season
Time series
Trend

29. Time Series and Patterns in Time series
 Time Series: The repeated observations of demand for a
service or product in their order of occurrence.
 There are five basic patterns of most time series.
 Horizontal. The fluctuation of data around a constant mean.
 Trend. The systematic increase or decrease in the mean of the series
over time.
 Seasonal. A repeatable pattern of increases or decreases in demand,
depending on the time of day, week, month, or season.
 Cyclical. The less predictable gradual increases or decreases over
longer periods of time (years or decades).
 Random. The un-forecastable variation in demand.

30. Horizontal Trend
Seasonal Cyclical
Time Series and Patterns in Time series

31. Forecasting : 3 Simple Factors
Use these 3 factors to
forecast the sales
Season
Time series
Trend
Time Series: The repeated observations of demand for a
service or product in their order of occurrence.

32. Easy to Forecast !
More the data .. More predictability..
Better Forecasts
Not Sure!

33. Forecasting : 3 Simple Factors
Use these 3 factors to
forecast the sales
Season
Time series
Trend
A trend is a movement in a particular direction

34. Trend lines in charts
• A trend is a movement in a particular direction
• A trend line is a straight line connecting multiple
points on a chart.
• The magnitude of the slope of a trend line, or
steepness, indicates the strength of the trend.
• Trendline can be used to forecast future !

35. 100
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This is a
Trend Line
Trend lines in charts

36. 2000
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This is also a
Trend Line
Trend lines in charts

37. Creating Trend line in excel : Step 1
 Select the graph and
right click
 Select Add Trend line

38.  A window opens with types
of trend line to select from 6
Options
 Select the default option for
trend line – Linear
 Click OK
Creating Trend line in excel : Step 2

39. You can see the trend line now
Creating Trend line in excel : Step 2(Contd.)

40. Formatting Trend line in excel : Step 3
 Right click on the trend line to
format it.
 Select style, color and weight of
your choice

41. Formatting Trend line in excel : Step 3 (contd.)
 Right click on the trend line to
format it.
 Select style, color and weight of
your choice

42. Forecast Options - Trend line in excel : Step 4

43.  By using this forecast Option you
can extend the trend line
forward or backward to the
number of periods as desired
 Here, I selected 6 periods
(Months - in this case) forward
Forecast Options - Trend line in excel : Step 4(Contd.)

44.  You can Observe that the trend line got extended in to the future by 6 periods
 This denotes if this trend continues, the sales of the country are likely to be like
this
Forecast Options - Trend line in excel : Step 4(Contd.)

45.  By clicking “display equation on
Chart” and display r-Squared
value on chart, you will be able
to display them on the graph -
Next slide explains them
 You can Set Intercept to any
value by clicking it and adding
value (0 is default).
 As this Option is not used
often in simple trend lines ,
explanation is beyond the
scope of this slide set.
Options - Trend line in excel : Step 4(Contd.)

46.  This is the equation generated by excel that holds
the mathematical relationship of Sales and months.
Equation and R Squared values – Step 4 (Contd.)

47. Equation
 This is the equation generated by excel that holds
the mathematical relationship of Sales and months.
 This equation will be unique to each data set
 This equation can be used to compute sales of any
future month
 to put it simply , here “y” denotes the sale (which
is on Y axis) and x denotes the month
 So lets compute 13th month projection, using this
equation
 y= ((17.311)*13)+296.39 that equals 521.43
 So the likely sales projection for 13th month is
521.43

48.  This is the R-Squared value for this trend line.
R Squared value

49.  This is the R-Squared Value for this trend line.
 This value denotes the reliability of the sales
projections.
 R squared value will range between 0 and 1
 If the R squared value is 1, then the trend is most
predictable and reliable.
 The reliability of trend line goes up if the R-
Squared value is nearest to 1
 Let us see the next example to understand it
better.
R Squared value

50. Take a look at the trend of sales and R Squared value

51. R Squared value = 1
 Here the R Squared Value is 1.
 Just take a look at the sales progress.
 With every passing month, this territory is adding
$100 to the previous month.
 So, going by the trend, you can be almost sure, that
the 13th month sales are .
 Remember, Trend lines and Forecasts means you are
presuming the existing market conditions are not going
to change radically.

52. Take a look at the Equation.. It is y=100x
 This means Y, the next month sales (13 th Month
sales)
 y= ((100)*13) that equals 1,300
 So ,the likely sales projection for 13th month is 1,300
 Remember the Forecast Test 1 ?

53. Now that you know what is R Squared value,
It’s Time to understand Correlation
By the way, r is called Correlation Coefficient
and
R Squared value is called Ccoefficient of Determination.

54. You need not remember these Hi-Fi terminology
It’s good enough to understand that if R Squared value is
near value of 1, the forecast is more accurate!!

55. 100
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Relationships involving dependence
Take a look at this trend….
Every one month the Increment is +100

56. 2000
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Relationships involving dependence
Take a look at this trend….
Every one month the decrease is by minus 100

57. In both the examples, There is a very good correlation between the
months progress and sales progress
With every month increase , there is a gain or loss of 100
In both the cases, R Squared value is equal to 1

58. This is also a perfect Correlation!
The R Squared value , if measured must be 1 !!

59. Some time ago, Wal-Mart decided to combine the data
Once combined, the data was mined extensively and many
correlations appeared.
Some of these were obvious; people who buy gin are also likely to
buy tonic. They often also buy lemons.
However, one correlation stood out like a
sore thumb because it was so unexpected.

60. Those queries revealed that, between 5pm and 7pm,
customers tended to co-purchase beer and diapers.

61. It seems , they found out that parents who wants to babysit
and also watch football and drink beer do not want to be
disturbed by their babies!

62. 62
Wal-Mart moved
the beer next to
the diapers and
beer sales went
up.

63. Trend line types
1. Logarithmic
2. Polynomial
3. Power
4. Exponential
5. Moving Average

64.  Do Remember, that for
every trend line type you
select, you will get different
equation and R Squared
value
Equation and R Squared Values- various trend lines

65. Rule of thumb to use type of Trend line
1.Linear trend line : Use it if data values are increasing
or decreasing at a steady rate.

66. 2. Logarithmic trend line : Useful when the rate of
change in the data increases or decreases quickly
and then levels out.
Rule of thumb to use type of Trend line

67. 3. Polynomial trend line : Used when there are data
fluctuations like the sales following seasonal trends
Rule of thumb to use type of Trend line

68. 4. Power trend line : Use with data that has values that
increase at specific rate at regular intervals.
Rule of thumb to use type of Trend line

69. 5. Exponential trend line : Use when data values
increase or decrease rates that are constantly
increasing.
Rule of thumb to use type of Trend line

70. 6. Moving average trend line : Use it when uneven
fluctuations are in data values
Rule of thumb to use type of Trend line

71. Best fit Trend line
• We have learnt that if R2 Value is near to 1, the reliability of
trend line is better.
• So, now we need to use a trend line from the five available
trend lines in excel menu to arrive at the most appropriate
one to ensure that our forecast is most reliable.
• The other trend line left out is Moving average for which
,you will neither get the equation nor the R2 value.
• Simplest way to find the best fit trend line is to check every
trend line’s R2 Value and use the trend line with highest R2
value ( Which is nearest to 1- out of 5 types)

72. Forecasting : 3 Simple Factors
Use these 3 factors to
forecast the sales
Season
Time series
Trend

73. 0
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Seasonality needs little explanation

74. Measuring Seasonality is simple
Sales Year 1 Year 2 Year 3 Year 4 Year 5 Year 1 Year 2 Year 3 Year 4 Year 5 5 Year Avg.
Jan 205 293 380 468 565 6.2% 6.7% 7.0% 7.1% 7.2% 6.9%
Feb 296 387 482 571 663 8.9% 8.9% 8.8% 8.7% 8.5% 8.7%
Mar 368 453 544 635 795 11.1% 10.4% 10.0% 9.7% 10.2% 10.2%
Apr 396 483 568 657 770 11.9% 11.1% 10.4% 10.0% 9.9% 10.4%
May 388 480 571 669 789 11.7% 11.0% 10.5% 10.2% 10.1% 10.5%
Jun 248 338 425 524 613 7.5% 7.7% 7.8% 8.0% 7.9% 7.8%
Jul 214 301 387 484 578 6.4% 6.9% 7.1% 7.4% 7.4% 7.1%
Aug 245 332 426 525 621 7.4% 7.6% 7.8% 8.0% 8.0% 7.8%
Sep 241 322 407 505 597 7.2% 7.4% 7.5% 7.7% 7.7% 7.5%
Oct 244 334 422 515 611 7.3% 7.6% 7.7% 7.8% 7.8% 7.7%
Nov 265 348 446 542 629 8.0% 8.0% 8.2% 8.2% 8.1% 8.1%
Dec 217 299 393 483 564 6.5% 6.8% 7.2% 7.3% 7.2% 7.1%
Year 3,327 4,370 5,451 6,578 7,795 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

75. So, these three factors can help us forecast
Use these 3 factors to
forecast the sales
Season
Time series
Trend

76. Here is a tool I created for you… using all 3 factors
Please download it in the downloads section or click the link below
Forecasting tool

77. Pharma Factors to consider
• Price variations – Better to forecast on units than
value
• Sales Closings and Incentives – Cyclical patterns ?
• Campaigns – Special campaigns ?
• Trade offers - Seasonality?
• Market – Localized Seasonality ? ( Local festivals in
zones e.g. Diwali, Puja etc.)

78. Easiest Forecasting Method
with new Microsoft Excel

79. Select the data , click forecast and Create

80. Voilà! ,forecast values with graphs!

81. Thank you