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- 1. AgendaMeaning of Sales ForecastingWhy Study forecasting ?Types of ForecastsCategorization of Sales ForecastingFacts in ForecastingLimitations of Demand ForecastingSteps in ForecastingSales forecasting TechniqueForecast Accuracy
- 2. “Contrive” “Before” [the fact] ؟؟
- 3. ؟؟
- 4. Sales Mantra“Hope is not a sales Strategy”
- 5. Meaning of Demand Forecasting
- 6. Meaning of Demand ForecastingDemand forecasting is the scientific and analytical estimation of demand for a product (service) for a particular period of time.
- 7. Why Study forecasting ?Setting Sales Targets, Pricing policies,establishing controls and incentives.Allows managers to plan personnel,operations of purchasing & finance for bettercontrol over wastes inefficiency and conflicts.
- 8. 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 thefuture health of a company
- 9. Why Study forecasting ?The ability to plan for production avoid theproblem of over-production & problem of shortsupply…………. Sales MaximizationThe ability to identify the pattern or trend ofsales Knowing when and how much to buy…….…Better Market Positioning
- 10. Types of ForecastsEconomic 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 productsDemand forecasts – Predict sales of existing products
- 11. Categorization of Demand Forecasting : (3 months to 2 years):for production planning, purchasing, anddistribution. Sales & production planning, budgeting : (2 years and more)for capacity planning, and investment decisionsNew product planning, facility location
- 12. Facts in ForecastingMain assumption: Past pattern repeats itself into the future.Forecasts are rarely perfect: Dont 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.
- 13. Facts in ForecastingForecasts 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 .
- 14. Limitations of Demand Forecasting
- 15. Limitations of Demand Forecasting
- 16. Qualities of Good Forecasting1) Simple2) Economy of time3) Economy of money4) Accuracy5) Reliability
- 17. Steps in ForecastingDetermine the purpose of the forecastSelect the items to be forecastedGather the dataDetermine the time horizon of the forecastSelect the forecasting model(s)Make the forecastValidate and implement results
- 18. Sales forecasting ProcessSetting Goals Gathering Analysis of Forecasting data dataEvaluating of Choosing The forecasting Best Model Forecasting outcomes For Forecasting
- 19. Forecasting TechniqueQualitative Analysis Quantitative AnalysisCustomer Sales Force Survey Time Series Causal Composite Analysis AnalysisExecutive Naïve Delphi Opinion Method approach Moving Weighted Regression Test Average Moving Analysis Marketing Least Exponential squares Smoothing
- 20. Sales forecasting TechniqueIt is generally recommended to use a combination of quantitative and qualitative techniques.
- 21. Forecasting TechniquesPrinciples of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.© 2005 Thomson Business and Professional Publishing
- 22. Qualitative (Subjective) Methods
- 23. 1- Consumers’ Opinion Survey (Buyer’s expectation Method )
- 24. 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
- 25. 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
- 26. 1- Consumers’ Opinion Survey :(Buyer’s expectation Method)
- 27. 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.
- 28. 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.
- 29. 2- Sales Force Composite Method
- 30. 2- Sales Force Composite Method
- 31. 2- Sales Force Composite MethodPrinciples of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.© 2005 Thomson Business and Professional Publishing
- 32. 2- Sales Force Composite Method
- 33. 3- Jury of Executive Opinion:
- 34. 3- Jury of Executive Opinion:
- 35. 3- Jury of Executive Opinion:
- 36. 3- Jury of Executive Opinion:
- 37. 4- Experts’ Opinion MethodDelphi 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
- 38. 4- Experts’ Opinion MethodDelphi Technique:Conclusions, insights, and expectations ofthe experts are evaluated by the entire groupresulting in shared more structured and lessbiased estimate of the futureThere are three different types ofparticipants in the Delphi process: decision makers, staff personnel, and respondents.
- 39. 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.
- 40. 4- Experts’ Opinion MethodDelphi 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
- 41. 4- Experts’ Opinion MethodDelphi Technique:
- 42. 4- Experts’ Opinion MethodDelphi Technique:
- 43. 5- Test Marketing
- 44. 5- Test Marketing
- 45. Forecasting TechniqueQualitative Analysis Quantitative AnalysisCustomer Sales Force Survey Time Series Causal Composite Analysis AnalysisExecutive Naïve Delphi Opinion Method approach Moving Weighted Regression Test Average Moving Analysis Marketing Least Exponential squares Smoothing
- 46. Forecasting TechniquesQuantitative 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
- 47. Quantitative forecasting Time Series Casual Models ModelsOnly independent assumes thatvariable is the time one or morebased on the factors otherassumption that the than time predictfuture is an extension future demand.of the past .
- 48. 53 s IntroductionSale Growth Maturity Product Life Cycle Decline Time
- 49. BCG Growth – Share Matrix
- 50. 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 forecastingForecast 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
- 51. Demand for product or service Time Series 2007 2008 2009 2010 201 Time 1
- 52. Time Series ComponentsSeasonal Random
- 53. 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
- 54. Time Series Pattern: SeasonalPatterns of change in a time series within a year which tend to repeat each yearDue to weather, customs, etc.Occurs within 1 yearForecasting methods: exponential smoothing with trend
- 55. have a tendency to recur in a few years usually repeat every two-five years. Repeating up & downmovementsDue to interactions of factors influencing economy
- 56. 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
- 57. Product Demand Charted over 4 Years with Trend and Seasonality Seasonal peaks Trend componentDemand for product or Actual demand line service Average Random demand over variation four years Year Year Year Year 1 2 3 4
- 58. Forecasting TechniqueQualitative Analysis Quantitative AnalysisCustomer Sales Force Survey Time Series Causal Composite Analysis AnalysisExecutive Naïve Delphi Opinion Method approach Moving Weighted Regression Test Average Moving Analysis Marketing Least Exponential squares Smoothing
- 59. 1- Naive Approach
- 60. 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
- 61. 3- Moving Average MethodCan 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
- 62. 3- Moving Average…… Example
- 63. 3- Moving Average Solution Moving Actual Moving TotalTime Average Sales (n=3) (n=3)Jan. 4Feb. 6Mar. 5April 3 4+6+5=15 15/3 = 5May 7Jun.
- 64. 3- Moving Average Solution Moving Actual Moving TotalTime Average Sales (n=3) (n=3)Jan. 4Feb. 6Mar. 5April 3 4+6+5=15 15/3 = 5May 7 6+5+3=14 14/3 = 4.7Jun.
- 65. 3- Moving Average Solution Moving Moving ActualTime Total Average sales (n=3) (n=3)Jan. 4Feb. 6Mar. 5April 3 4+6+5=15 15/3=5.0May 7 6+5+3=14 14/3=4.7Jun 5+3+7=15 15/3=5.0
- 66. 4- Weighted Moving AverageWeights are used to give more values torecent valueThis makes the techniques more responsiveto changes because latter periods may bemore heavily waitedMost recent observation receives the most weight, and the weight decreases for older data values
- 67. 4- Weighted Moving AverageLast month ago 3Two month ago 2 Sum of the weightsThree month ago 1 6Month Actual sales Three month moving average Jan 10 Feb 12March 13 × (13 + ×(12April 1) +×(10 12.1 16 3) 2) = 6 7
- 68. 4- Weighted Moving Average ActualMonth Three months moving average sales Jan 10 Feb 12March 13April 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
- 69. 5- Trend Projection Method
- 70. Linear Equations Ydependent 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
- 71. 6- Exponential Smoothing Methodα = σ Smoothing constantα = 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-1Ftt= forecast for this periodF = forecast for this period Ft-1 = forecast for the Ft-1 = forecast for the previous period previous period
- 72. 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-1Ftt= forecast for 2013 Ft-1 = forecast 2012F = forecast for 2013 Ft-1 = forecast 2012
- 73. 6- Exponential Smoothing……Example © 1995 Corel Corp.
- 74. 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
- 75. 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
- 76. 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
- 77. 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
- 78. 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
- 79. 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
- 80. 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
- 81. 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
- 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 + .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
- 83. 6- Exponential Smoothing Method
- 84. Forecasting TechniqueQualitative Analysis Quantitative AnalysisCustomer Sales Force Survey Time Series Causal Composite Analysis AnalysisExecutive Naïve Delphi Opinion Method approach Moving Weighted Regression Test Average Moving Analysis Marketing Least Exponential squares Smoothing
- 85. Causal MethodUsually 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 forecastExample: PC sales forecasts (dependent variable) could be correlated to advertising budget, promotions, prices, competitors prices (independent variables)
- 86. Regression Analysis Method
- 87. Regression Analysis Method
- 88. forecast errordefined as the difference between actualquantity and the forecast et = A t - Ft et = forecast At = actual Ft = forecast error for demand for Period t for Period t Period tThe smaller the forecast error, the moreaccurate the forecast.
- 89. Values of Dependent Variable forecast error Actual Deviation7 observation Deviation5 Deviation6 Deviation3 Deviation4 Deviation1 )error( Deviation2 Trend line Time period
- 90. Forecast AccuracySeveral measures of forecasting accuracylarger 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.
- 91. Forecast AccuracyMean squared error (MSE) Average of the squared differences between the forecasted and observed valuesMean absolute percentage error (MAPE) How many Percent the forecast is off from the actual data
- 92. Forecast AccuracyPeriod )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 0.2639MAD=58 & MSE=8280 & MAPE=2.64%
- 93. Forecast AccuracyPeriod )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
- 94. Forecast AccuracyPeriod )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
- 95. Forecast AccuracyPeriod )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%
- 96. Excel Chart Methods

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