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Slides to understand basic forecasting techniques

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  1. 1. Sales Forecasting for all !
  2. 2. The Creation of Adam : a painting by Michelangelo, on Sistine Chapel's ceiling
  3. 3. The God appeared to be an anatomically accurate picture of the human brain.
  4. 4. Forecasting is Science as well as an Art
  5. 5. But, Forecasts can go wrong!
  6. 6. This is the reality!
  7. 7. 640K ought to be enough for anybody… Bill Gates on memory, 1981 Even Bill Gates can go wrong!
  8. 8. Sometimes , the Forecast is always right… all it needs is a bit of common sense
  9. 9. Here is a Forecast that is always right…
  10. 10. The Paradox of Forecasting Month’s sale is difficult to predict Long range plan is easy to make
  11. 11. So, What is a Forecast ?
  12. 12. It’s not Crystal gazing
  13. 13. The premise : The near future is going to be like the past
  14. 14. Now , a small test……
  15. 15. Test 1 : What is the Sales Forecast for Dec ? 100 200 300 400 500 600 700 800 900 1000 1100 0 200 400 600 800 1000 1200 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  16. 16. Is your answer 1,200 ? That’s Right…
  17. 17. Test 2 : What is the Sales Forecast for Dec? 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 0 500 1000 1500 2000 2500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  18. 18. Is your answer 900 ? Right again! Good going…
  19. 19. Test 3: What is the Sales Forecast for Dec ? 100 200 300 300 400 500 600 600 700 800 900 0 100 200 300 400 500 600 700 800 900 1000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  20. 20. Is your answer 900 ? Keep it up!
  21. 21. Test 4 : What is the Sales Forecast for Dec ? 100 50 200 100 300 150 400 200 500 250 600 0 100 200 300 400 500 600 700 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  22. 22. Is your answer 300 ? That’s Right! Keep it up!
  23. 23. Test 5 : What is the Sales Forecast for July ? 0 0 200 300 400 300 200 0 0 0 0 0 0 0 300 400 500 400 300 0 0 0 0 0 0 0 400 500 600 500 400 0 0 0 0 0 0 0 500 600 700 600 500 0 0 0 0 0 0 0 600 700 800 700 - 100 200 300 400 500 600 700 800 900 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-13 Jul-13 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. 24. Is your answer 600? That’s Great!
  25. 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. 26. Is your answer 600-700-800 ? Not sure ….Difficult .. isn't it ?
  27. 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. 28. Forecasting : 3 Simple Factors Use these 3 factors to forecast the sales Season Time series Trend
  29. 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. 30. Horizontal Trend Seasonal Cyclical Time Series and Patterns in Time series
  31. 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. 32. Easy to Forecast ! More the data .. More predictability.. Better Forecasts Not Sure!
  33. 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. 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. 35. 100 200 300 400 500 600 700 800 900 1000 1100 0 200 400 600 800 1000 1200 1400 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec This is a Trend Line Trend lines in charts
  36. 36. 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 0 500 1000 1500 2000 2500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec This is also a Trend Line Trend lines in charts
  37. 37. Creating Trend line in excel : Step 1  Select the graph and right click  Select Add Trend line
  38. 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. 39. You can see the trend line now Creating Trend line in excel : Step 2(Contd.)
  40. 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. 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. 42. Forecast Options - Trend line in excel : Step 4
  43. 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. 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. 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. 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. 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. 48.  This is the R-Squared value for this trend line. R Squared value
  49. 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. 50. Take a look at the trend of sales and R Squared value
  51. 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. 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. 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. 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. 55. 100 200 300 400 500 600 700 800 900 1000 1100 0 200 400 600 800 1000 1200 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Relationships involving dependence Take a look at this trend…. Every one month the Increment is +100
  56. 56. 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 0 500 1000 1500 2000 2500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Relationships involving dependence Take a look at this trend…. Every one month the decrease is by minus 100
  57. 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. 58. This is also a perfect Correlation! The R Squared value , if measured must be 1 !!
  59. 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. 60. Those queries revealed that, between 5pm and 7pm, customers tended to co-purchase beer and diapers.
  61. 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. 62 Wal-Mart moved the beer next to the diapers and beer sales went up.
  63. 63. Trend line types 1. Logarithmic 2. Polynomial 3. Power 4. Exponential 5. Moving Average
  64. 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. 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. 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. 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. 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. 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. 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. 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. 72. Forecasting : 3 Simple Factors Use these 3 factors to forecast the sales Season Time series Trend
  73. 73. 0 0 200 300 400 300 200 0 0 0 0 0 0 0 300 400 500 400 300 0 0 0 0 0 0 0 400 500 600 500 400 0 0 0 0 0 0 0 500 600 700 600 500 0 0 0 0 0 0 0 600 700 800 700 - 100 200 300 400 500 600 700 800 900 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-13 Jul-13 Sep-13 Nov-13 Jan-14 Mar-14 May-14 Jul-14 Sep-14 Nov-14 Jan-15 Mar-15 May-15 Jul-15 Seasonality needs little explanation
  74. 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. 75. So, these three factors can help us forecast Use these 3 factors to forecast the sales Season Time series Trend
  76. 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. 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. 78. Easiest Forecasting Method with new Microsoft Excel
  79. 79. Select the data , click forecast and Create
  80. 80. Voilà! ,forecast values with graphs!
  81. 81. Thank you

Description

Slides to understand basic forecasting techniques

Transcript

  1. 1. Sales Forecasting for all !
  2. 2. The Creation of Adam : a painting by Michelangelo, on Sistine Chapel's ceiling
  3. 3. The God appeared to be an anatomically accurate picture of the human brain.
  4. 4. Forecasting is Science as well as an Art
  5. 5. But, Forecasts can go wrong!
  6. 6. This is the reality!
  7. 7. 640K ought to be enough for anybody… Bill Gates on memory, 1981 Even Bill Gates can go wrong!
  8. 8. Sometimes , the Forecast is always right… all it needs is a bit of common sense
  9. 9. Here is a Forecast that is always right…
  10. 10. The Paradox of Forecasting Month’s sale is difficult to predict Long range plan is easy to make
  11. 11. So, What is a Forecast ?
  12. 12. It’s not Crystal gazing
  13. 13. The premise : The near future is going to be like the past
  14. 14. Now , a small test……
  15. 15. Test 1 : What is the Sales Forecast for Dec ? 100 200 300 400 500 600 700 800 900 1000 1100 0 200 400 600 800 1000 1200 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  16. 16. Is your answer 1,200 ? That’s Right…
  17. 17. Test 2 : What is the Sales Forecast for Dec? 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 0 500 1000 1500 2000 2500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  18. 18. Is your answer 900 ? Right again! Good going…
  19. 19. Test 3: What is the Sales Forecast for Dec ? 100 200 300 300 400 500 600 600 700 800 900 0 100 200 300 400 500 600 700 800 900 1000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  20. 20. Is your answer 900 ? Keep it up!
  21. 21. Test 4 : What is the Sales Forecast for Dec ? 100 50 200 100 300 150 400 200 500 250 600 0 100 200 300 400 500 600 700 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  22. 22. Is your answer 300 ? That’s Right! Keep it up!
  23. 23. Test 5 : What is the Sales Forecast for July ? 0 0 200 300 400 300 200 0 0 0 0 0 0 0 300 400 500 400 300 0 0 0 0 0 0 0 400 500 600 500 400 0 0 0 0 0 0 0 500 600 700 600 500 0 0 0 0 0 0 0 600 700 800 700 - 100 200 300 400 500 600 700 800 900 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-13 Jul-13 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. 24. Is your answer 600? That’s Great!
  25. 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. 26. Is your answer 600-700-800 ? Not sure ….Difficult .. isn't it ?
  27. 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. 28. Forecasting : 3 Simple Factors Use these 3 factors to forecast the sales Season Time series Trend
  29. 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. 30. Horizontal Trend Seasonal Cyclical Time Series and Patterns in Time series
  31. 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. 32. Easy to Forecast ! More the data .. More predictability.. Better Forecasts Not Sure!
  33. 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. 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. 35. 100 200 300 400 500 600 700 800 900 1000 1100 0 200 400 600 800 1000 1200 1400 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec This is a Trend Line Trend lines in charts
  36. 36. 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 0 500 1000 1500 2000 2500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec This is also a Trend Line Trend lines in charts
  37. 37. Creating Trend line in excel : Step 1  Select the graph and right click  Select Add Trend line
  38. 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. 39. You can see the trend line now Creating Trend line in excel : Step 2(Contd.)
  40. 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. 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. 42. Forecast Options - Trend line in excel : Step 4
  43. 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. 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. 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. 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. 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. 48.  This is the R-Squared value for this trend line. R Squared value
  49. 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. 50. Take a look at the trend of sales and R Squared value
  51. 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. 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. 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. 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. 55. 100 200 300 400 500 600 700 800 900 1000 1100 0 200 400 600 800 1000 1200 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Relationships involving dependence Take a look at this trend…. Every one month the Increment is +100
  56. 56. 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 0 500 1000 1500 2000 2500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Relationships involving dependence Take a look at this trend…. Every one month the decrease is by minus 100
  57. 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. 58. This is also a perfect Correlation! The R Squared value , if measured must be 1 !!
  59. 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. 60. Those queries revealed that, between 5pm and 7pm, customers tended to co-purchase beer and diapers.
  61. 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. 62 Wal-Mart moved the beer next to the diapers and beer sales went up.
  63. 63. Trend line types 1. Logarithmic 2. Polynomial 3. Power 4. Exponential 5. Moving Average
  64. 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. 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. 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. 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. 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. 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. 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. 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. 72. Forecasting : 3 Simple Factors Use these 3 factors to forecast the sales Season Time series Trend
  73. 73. 0 0 200 300 400 300 200 0 0 0 0 0 0 0 300 400 500 400 300 0 0 0 0 0 0 0 400 500 600 500 400 0 0 0 0 0 0 0 500 600 700 600 500 0 0 0 0 0 0 0 600 700 800 700 - 100 200 300 400 500 600 700 800 900 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-13 Jul-13 Sep-13 Nov-13 Jan-14 Mar-14 May-14 Jul-14 Sep-14 Nov-14 Jan-15 Mar-15 May-15 Jul-15 Seasonality needs little explanation
  74. 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. 75. So, these three factors can help us forecast Use these 3 factors to forecast the sales Season Time series Trend
  76. 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. 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. 78. Easiest Forecasting Method with new Microsoft Excel
  79. 79. Select the data , click forecast and Create
  80. 80. Voilà! ,forecast values with graphs!
  81. 81. Thank you

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