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AgendaMeaning of Sales ForecastingWhy Study forecasting ?Types of ForecastsCategorization of Sales ForecastingFacts i...
“Contrive”        “Before”                  [the fact]             ‫؟؟‬
‫؟؟‬
Sales Mantra“Hope is not a sales Strategy”
Meaning of Demand Forecasting
Meaning of Demand ForecastingDemand forecasting is the scientific and analytical estimation of demand for a product (serv...
Why Study      forecasting ?Setting Sales Targets, Pricing policies,establishing controls and incentives.Allows managers...
Why Study     forecasting ?Reduce the cost for purchasing raw   material , Increased revenue .Improved customer service ...
Why Study       forecasting ?The ability to plan for production avoid theproblem of over-production & problem of shortsup...
Types of ForecastsEconomic forecasts  – Address the future business conditions    (e.g., inflation rate, money supply, et...
Categorization of Demand Forecasting                     : (3 months to 2 years):for production planning, purchasing, andd...
Facts in ForecastingMain assumption: Past pattern repeats itself into the future.Forecasts are rarely perfect: Dont expe...
Facts in ForecastingForecasts for a group of products are usually more accurate than these for individual products.A goo...
Limitations of Demand Forecasting
Limitations of Demand Forecasting
Qualities of Good Forecasting1) Simple2) Economy of time3) Economy of money4) Accuracy5) Reliability
Steps in ForecastingDetermine the purpose of the forecastSelect the items to be forecastedGather the dataDetermine the...
Sales forecasting      ProcessSetting Goals    Gathering     Analysis of Forecasting       data          dataEvaluating of...
Forecasting TechniqueQualitative Analysis             Quantitative AnalysisCustomer    Sales Force Survey                 ...
Sales forecasting TechniqueIt is generally recommended to use a   combination of quantitative and        qualitative techn...
Forecasting TechniquesPrinciples of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.© 2005 Thomson ...
Qualitative (Subjective) Methods
1- Consumers’ Opinion Survey  (Buyer’s expectation Method )
1- Consumers’ Opinion Survey  (Buyer’s expectation Method )Advantages :Simple to administer and comprehend.Forecasting R...
1- Consumers’ Opinion Survey  (Buyer’s expectation Method )Advantages :Technique is very effective to determine demand fo...
1- Consumers’ Opinion Survey :(Buyer’s expectation Method)
2- Sales Force Composite Method Salespersons are                 in direct contact with the customers. Eachsalespersons ...
2- Sales Force Composite Method These forecasts are then reviewed to ensure they are realistic, then combined at the dist...
2- Sales Force Composite Method
2- Sales Force Composite Method
2- Sales Force Composite MethodPrinciples of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.© 2005...
2- Sales Force Composite Method
3- Jury of Executive Opinion:
3- Jury of Executive Opinion:
3- Jury of Executive Opinion:
3- Jury of Executive Opinion:
4- Experts’ Opinion MethodDelphi Technique:It includes successive sessions of brainstorming among highly specialized expe...
4- Experts’ Opinion MethodDelphi Technique:Conclusions, insights, and expectations ofthe experts are evaluated by the ent...
4- Delphi Technique: assist the decision makers by preparing, distributing, collecting, and summarizing a series of quest...
4- Experts’ Opinion MethodDelphi Technique:                               ?(Sales)                        Evaluate respons...
4- Experts’ Opinion MethodDelphi Technique:
4- Experts’ Opinion MethodDelphi Technique:
5- Test Marketing
5- Test Marketing
Forecasting TechniqueQualitative Analysis                 Quantitative AnalysisCustomer    Sales Force Survey             ...
Forecasting TechniquesQuantitative forecasting       Uses mathematical models and historical data       to make forecast...
Quantitative forecasting   Time Series               Casual     Models                  ModelsOnly independent      assu...
53                         s            IntroductionSale              Growth               Maturity                       ...
BCG Growth – Share Matrix
What is a Time Series?a collection of data recorded over a period of time (weekly, monthly, quarterly)an analysis of its...
Demand for product or service                                       Time Series                                2007   2008...
Time Series ComponentsSeasonal       Random
Time Series Pattern: Secular Trend change occurring  consistently over a long  time and is relatively  smooth in its path...
Time Series Pattern: SeasonalPatterns of change in a time series within a year which tend to repeat each yearDue to weat...
have a tendency to recur in a few years usually repeat every two-five years. Repeating up & downmovementsDue to interac...
Time Series Pattern: Stationary or Irregular Variation or Random events have no trend of  occurrence hence they  create r...
Product Demand Charted over 4 Years     with Trend and Seasonality                        Seasonal peaks     Trend compone...
Forecasting TechniqueQualitative Analysis                 Quantitative AnalysisCustomer    Sales Force Survey             ...
1- Naive Approach
 It is convenient for long term periods Year Sales % change 2007   527 2008   639   0.2 2009   467   - 0.3               ...
3- Moving Average MethodCan be defined as the summation of demands of total periods divided by the total number of period...
3- Moving Average…… Example
3- Moving Average Solution                                    Moving            Actual   Moving TotalTime                 ...
3- Moving Average Solution                                    Moving            Actual   Moving TotalTime                 ...
3- Moving Average Solution                 Moving     Moving        ActualTime              Total     Average        sales...
4- Weighted Moving AverageWeights are used to give more values torecent valueThis makes the techniques more responsiveto...
4- Weighted Moving AverageLast month ago         3Two month ago          2           Sum of the weightsThree month ago    ...
4- Weighted Moving Average      ActualMonth        Three months moving average       sales  Jan   10  Feb   12March   13Ap...
5- Trend Projection Method
Linear Equations                       Ydependent variable value                             Y = bX + a                   ...
6- Exponential Smoothing Methodα = σ Smoothing constantα = σ Smoothing constant        At-1= Actual demand for            ...
6- Exponential Smoothing Method  α = σ Smoothing  α = σ Smoothing         At-1= Actual sales 2012                         ...
6- Exponential Smoothing……Example                     © 1995 Corel Corp.
6- Exponential Smoothing Solution          Ft = Ft-1 + α(At-1 - Ft-1)                           Forecast, F t             ...
6- Exponential Smoothing Solution          Ft = Ft-1 + α(At-1 - Ft-1)                         Forecast, F t               ...
6- Exponential Smoothing Solution                Ft = Ft-1 + α(At-1 - Ft-1)                                      Forecast,...
6- Exponential Smoothing Solution                       Ft = Ft-1 + α(At-1 - Ft-1)                                      Fo...
6- Exponential Smoothing Solution                       Ft = Ft-1 + α(At-1 - Ft-1)                                        ...
6- Exponential Smoothing Solution          Ft = Ft-1 + α(At-1 - Ft-1)                          Forecast, F t   Time Actual...
6- Exponential Smoothing Solution          Ft = Ft-1 + α(At-1 - Ft-1)                              Forecast, F t          ...
6- Exponential Smoothing Solution             Ft = Ft-1 + α(At-1 - Ft-1)                              Forecast, F t    Tim...
6- Exponential Smoothing Solution           Ft = Ft-1 + α(At-1 - Ft-1)                             Forecast, F t          ...
6- Exponential Smoothing Method
Forecasting TechniqueQualitative Analysis                  Quantitative AnalysisCustomer     Sales Force Survey           ...
Causal MethodUsually consider several variable that are related to the quantity being predicted once the related variabl...
Regression Analysis Method
Regression Analysis Method
forecast errordefined as the difference between actualquantity and the forecast                 et = A t - Ft et = foreca...
Values of Dependent Variable                forecast error                                  Actual                        ...
Forecast AccuracySeveral measures of forecasting accuracylarger the value the larger the forecast error Mean absolute d...
Forecast AccuracyMean squared error (MSE)  Average of the squared differences between the   forecasted and observed valu...
Forecast AccuracyPeriod )Sales(A Forecast E     [E]    E2     E]/A]  1      1600    1650    -50   50     2500   0.0313  2 ...
Forecast AccuracyPeriod )Sales(A Forecast E        [E]    E2     E]/A]  1       1600      1650   -50    50    2500    0.03...
Forecast AccuracyPeriod )Sales(A Forecast E       [E]    E2     E]/A]  1       1600     1650   -50    50     2500   0.0313...
Forecast AccuracyPeriod )Sales(A Forecast E        [E]    E2     E]/A]  1      1600       1650   -50    50     2500   0.03...
Excel Chart Methods
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
Forecating dr. sameh  mousa
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  • Contrive يخطط- يدبر casten يوزع الأدوار
  • Sales Mantra : is a Customer Relationship Management (CRM) tool through which any business can develop a lasting business relationship with his customers . Sales Mantra: involves all important areas of your business i.e. sales, marketing and customer service including control over your expenses and current assets. The software has been designed to enable the user to increase the sales and customer satisfaction manifolds within a short span of time .
  • intuition حدس – بديهه implies يدل على
  • Uncertainties غامض -- ) volatile متغير) --- dynamic ملئ بالقوة و النشاط incentives
  • slack مهمل ------ inventories المخزون revenue الدخل barometer
  • slack مهمل ------ inventories المخزون
  • One can use an example based upon one’s college or university. Students can be asked why each of these forecast types is important to the college. Once they begin to appreciate the importance, one can then begin to discuss the problems. For example, is predicting “demand” merely as simple as predicting the number of students who will graduate from high school next year (i.e., a simple counting exercise )?
  • Purchasing شراء capital
  • Pooling effect is to eliminate pure randomness .
  • Aggregate
  • Inevitable محتم
  • huge
  • Validate تقر
  • Relevant ذو علاقة ------- intuition بديهة - حدس
  • This slide outlines several qualitative methods of forecasting. Ask students to give examples of occasions when each might be appropriate . The next several slides elaborate on these qualitative methods .
  • purchase شراء ------------- Census يفحص - يكتشف
  • Merits ميزة عيب Demerits -- comprehend فهم –إدراك bias تحيز - ميل
  • Merits ميزة عيب Demerits -- comprehend فهم –إدراك bias تحيز - ميل
  • Merits ميزة عيب Demerits -- comprehend فهم –إدراك bias تحيز - ميل
  • pessimism تشائم Composite مركب من عدة عناصر respective خصوصى
  • pessimism consultation إستشارة
  • Notions فكرة - optimism تفاؤل -- pessimism تشاؤم incurred يسبب
  • Notions فكرة - optimism تفاؤل -- pessimism تشاؤم reliable يمكن الأعتماد علية
  • Notions فكرة - optimism تفاؤل -- pessimism تشاؤم implicit
  • Notions فكرة - optimism تفاؤل -- pessimism تشاؤم implicit
  • purchase Jury هيئة المحلفين
  • Solicited يستجدى
  • Solicited يستجدى
  • Tupperware consensus Executive solicited
  • Consolidated يثبت يدعم revised يعدل - يغير
  • Sought valued survey insights الفهم العميق
  • Sought valued
  • You might ask your students to consider whether there are special examples where this technique is required. ( Questions of technology transfer or assessment, for example; or other questions where information from many different disciplines is required .)
  • confidential firms شركة - مؤسسة rival المنافسة bias تحيز
  • Guess panel فريق
  • crucial فاصل – حاسم sold
  • Sunk بالوعة
  • Existing
  • Boston Consulting Group
  • Trend
  • Secular
  • Fortunately, cyclical pattern often is important for strategic decisions in longer term and is responsibility for executives. For most manager, even things went very wrong, you are not along .
  • Trend
  • Sense unmet demand inventory قائمة
  • At this point, you might discuss the impact of the number of periods included in the calculation. The more periods you include, the closer you come to the overall average; the fewer, the closer you come to the value in the previous period. What is the tradeoff ?
  • intercept slope coefficients
  • 28
  • You may wish to discuss several points : - this is just a moving average wherein every point in included in the forecast, but the weights of the points continuously decrease as they extend further back in time . - the equation actually used to calculate the forecast is convenient for programming on the computer since it requires as data only the actual and forecast values from the previous time point . - we need a formal process and criteria for choosing the “best” smoothing constant .
  • You may wish to discuss several points : - this is just a moving average wherein every point in included in the forecast, but the weights of the points continuously decrease as they extend further back in time . - the equation actually used to calculate the forecast is convenient for programming on the computer since it requires as data only the actual and forecast values from the previous time point . - we need a formal process and criteria for choosing the “best” smoothing constant .
  • exponential الدليل - الأس
  • Exponential
  • dampening
  • Regression
  • penalized
  • penalized
  • Transcript of "Forecating dr. sameh mousa"

    1. 1. 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
    2. 2. “Contrive” “Before” [the fact] ‫؟؟‬
    3. 3. ‫؟؟‬
    4. 4. Sales Mantra“Hope is not a sales Strategy”
    5. 5. Meaning of Demand Forecasting
    6. 6. Meaning of Demand ForecastingDemand forecasting is the scientific and analytical estimation of demand for a product (service) for a particular period of time.
    7. 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. 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. 9. Why Study forecasting ?The ability to plan for production avoid theproblem of over-production & problem of shortsupply…………. Sales MaximizationThe ability to identify the pattern or trend ofsales Knowing when and how much to buy…….…Better Market Positioning
    10. 10. 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
    11. 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. 12. Facts in ForecastingMain 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. 13. 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 .
    14. 14. Limitations of Demand Forecasting
    15. 15. Limitations of Demand Forecasting
    16. 16. Qualities of Good Forecasting1) Simple2) Economy of time3) Economy of money4) Accuracy5) Reliability
    17. 17. 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
    18. 18. Sales forecasting ProcessSetting Goals Gathering Analysis of Forecasting data dataEvaluating of Choosing The forecasting Best Model Forecasting outcomes For Forecasting
    19. 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. 20. Sales forecasting TechniqueIt is generally recommended to use a combination of quantitative and qualitative techniques.
    21. 21. Forecasting TechniquesPrinciples of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan.© 2005 Thomson Business and Professional Publishing
    22. 22. Qualitative (Subjective) Methods
    23. 23. 1- Consumers’ Opinion Survey (Buyer’s expectation Method )
    24. 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. 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. 26. 1- Consumers’ Opinion Survey :(Buyer’s expectation Method)
    27. 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. 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. 29. 2- Sales Force Composite Method
    30. 30. 2- Sales Force Composite Method
    31. 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. 32. 2- Sales Force Composite Method
    33. 33. 3- Jury of Executive Opinion:
    34. 34. 3- Jury of Executive Opinion:
    35. 35. 3- Jury of Executive Opinion:
    36. 36. 3- Jury of Executive Opinion:
    37. 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. 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 futureThere are three different types ofparticipants in the Delphi process: decision makers,  staff personnel,  and respondents.
    39. 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. 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. 41. 4- Experts’ Opinion MethodDelphi Technique:
    42. 42. 4- Experts’ Opinion MethodDelphi Technique:
    43. 43. 5- Test Marketing
    44. 44. 5- Test Marketing
    45. 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. 46. 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
    47. 47. Quantitative forecasting Time Series Casual Models ModelsOnly independent assumes thatvariable is the time one or morebased on the factors otherassumption that the than time predictfuture is an extension future demand.of the past .
    48. 48. 53 s IntroductionSale Growth Maturity Product Life Cycle Decline Time
    49. 49. BCG Growth – Share Matrix
    50. 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 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
    51. 51. Demand for product or service Time Series 2007 2008 2009 2010 201 Time 1
    52. 52. Time Series ComponentsSeasonal Random
    53. 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. 54. 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
    55. 55. have a tendency to recur in a few years usually repeat every two-five years. Repeating up & downmovementsDue to interactions of factors influencing economy
    56. 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. 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. 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. 59. 1- Naive Approach
    60. 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. 61. 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
    62. 62. 3- Moving Average…… Example
    63. 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. 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. 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. 66. 4- Weighted Moving AverageWeights are used to give more values torecent valueThis makes the techniques more responsiveto changes because latter periods may bemore heavily waitedMost recent observation receives the most weight, and the weight decreases for older data values
    67. 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. 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. 69. 5- Trend Projection Method
    70. 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. 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. 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. 73. 6- Exponential Smoothing……Example © 1995 Corel Corp.
    74. 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. 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. 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. 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. 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. 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. 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. 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. 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. 83. 6- Exponential Smoothing Method
    84. 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. 85. 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)
    86. 86. Regression Analysis Method
    87. 87. Regression Analysis Method
    88. 88. forecast errordefined 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 tThe smaller the forecast error, the moreaccurate the forecast.
    89. 89. Values of Dependent Variable forecast error Actual Deviation7 observation Deviation5 Deviation6 Deviation3 Deviation4 Deviation1 )error( Deviation2 Trend line Time period
    90. 90. 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.
    91. 91. 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
    92. 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. 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. 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. 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. 96. Excel Chart Methods
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