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DEMAND FORECASTING
            -PRESEN   TED BY-
           2. Gautam Agarwal
             3. Hitesh Agarwal
            11. Kandarp Desai
         15. Vaibhav Gumaste
             26. Omkar Kelkar
           29. Aditya Krishnan
OBJECTIVES FOR DEMAND
FORECASTING
•   Understand the role of demand forecasting
•   Identify reasons for demand forecasting
•   Study of Forecasting methodologies
•   Selecting the right forecasting method.
•   Measurement of forecasting errors.
INTRODUCTION
   Predicting future demand of products/services
    of an organisation
   Forecast = To estimate/calculate in advance.
   Guiding factor- for deciding the capacity and
    location of new facility.
   The staffing decisions should be in line with
    the demand forecasts.
   It affects administrative plans and policies.
To minimize
              Maximize
                                 losses of
               gains for                         To offset
                               uncontrollabl
              actions of                        the actions
                                 e events
             organisation                            of
                                                competitor
 Maximize
 gains for
  external
                                                         Material
environmen             REASONS FOR                     requiremen
      t
                         DEMAND                         t planning

                       FORECASTING
     To
   develop                                      In decision
   policies                                     making for
                                   To provide    budgeting
                To develop         adequate
               administrativ        staff to
                 e plans            support
                                  requirement
                                        s
VARIOUS METHODS
Qualitative Analysis
1) Consumers Survey: Complete Enumeration Method
 The forecaster undertakes a complete survey of all

consumers whose demand he intends to forecast.
Once this information is collected, the sales forecasts

are obtained by simply adding the probable demands
of all consumers.
The principle merit of this method is that the

forecaster does not introduce any bias or value
judgment of his own.
But it is a very tedious and cumbersome process; it is

not feasible where a large number of consumers are
involved
2) Consumer Survey-Sample Survey Method

Under this method, the forecaster selects a few
consuming units out of the relevant population and then
collects data on their probable demands for the product
during the forecast period.
The total demand of sample units is finally blown up to

generate the total demand forecast.
Compared to the former survey, this method is less

tedious and less costly, and subject to less data error;
but the choice of sample is very critical.
 If the sample is properly chosen, then it will yield

dependable results; otherwise there may be sampling
error.
3) Sales Force Composite

The sales force composite method of forecasting
starts with the forecaster asking for opinions about
future sales from every member of the sales staff
currently working in the field.
Each sales force member states how many sales

she thinks she'll make during the given forecasting
period.
Department managers look over and adjust

salespeople's predictions before turning the
numbers over for forecasting.
Predictions are usually checked against historical
4) Executive Opinion Poll

 Forecasters using the executive opinion or expert
opinion method poll executives or experts from
within the company and ask their opinion on the
optional sales for the given forecasting time period.
The forecaster will then average the individual

judgments or try for a group consensus.
Executive opinion polls are often used to verify (or

invalidate) other qualitative methods, especially
sales force composites.
5) Delphi Method




   Dis-advantages: Biased , non-response situation , time consuming.
   Advantages: No pressure.
6) Past Analogies

Sometimes data on the exact time of a particular
event (or events) are available.
Experts use cases where similar events have

occurred at different times or in different geographic
areas and compare them to the issue at hand.
 If occurrence or no-occurrence of an event is on a

regular basis, then the data can be thought of as
having a repeated measurement structure.
 It helps to select a large number of similar situations,

rather than basing a decision on comparison with only
one case.
Quantitative analysis
   Forecast future demand by using quantitative data from
    the past and extrapolating it to make forecasts of future
    levels.

    Demand for existing products can be forecasted by
    using this method.

   They are used when historical data is available.

   There are of two types of techniques
      1. Time series analysis
      2. Causal analysis
Time series analysis
   Time series of historical demand data with respect to time intervals
    (periods) in the past is used to make predictions for the future demand.

Following are the five popular methods

   Simple moving average

   Simple exponential smoothing

   Holt’s double- exponential smoothing

   Winters’ triple- exponential smoothing

   Forecasting by Linear regression analysis
Simple moving average
   It is suitable under situations where there is neither a
    growth nor a decline trend shown by the actual past
    data for forecasting.

   For eg : If we have past data of the actual sales of a
    product for the months of Jan, Feb and March, we
    take the simple average of these sales figures for
    the three months. This simple average becomes the
    forecast for the next month i.e April.
Simple Moving Average Method
    Example : four week moving average
Example: Three Period Moving average.
Given below are the actual sale of a toy for the past 5 weeks. We need to predict the sales for
the 6th week.
 W EEK      ACTUAL              FORECAST            CALCULATION
            SALES               (IN UNITS)
            (IN UNITS)
1           1634

2           1821

3           2069

4           1952

5           2178                1869                (1634+1821+2069+ 1952)/4

6                               2005                (1821+2069+1952+2178)/4
Weighted Moving Average
    Method
The data in the recent past periods should be given more weight or
importance compared to the data in the periods far off from the
current time.
W EEK ACTUAL              FOR  ECAST CALCULATION
          SALE            (IN
          (IN UNITS)      UNITS)
1        1634(0.1)
2        1821(0.2)
3        2069(0.3)
4        1952(0.4)
5                        1929          (1634*0.1+1821*0.2+20
                                       69*0.3+1952*0.4)/ 1
Linear Regression Analysis
   It is applied in situations where two variables are
    linearly correlated to each other.

   In time series analysis, the independent variable
    is time while the dependent variable is the actual
    demand in the past.

   A graph showing the points for the corresponding
    values of two variables is called scatter diagram.
    These points should display an approximately
    linear trend.
Example of linear regression




Y= 1060X + 440 is the regression equation
Interpretation: As the age of the car increase by 1 year
the maintenance cost is expected to increase by Rs1060.
How to choose a demand forecasting
technique
    Objectives of a forecast

    Cost involved

    Time perspective (short run or long run)

    Complexity of the technique

    Nature and quality of available data
QUANTITATIVE
ANALYSIS




   EXPONENTIAL SMOOTHING METHODS
The problem with Moving Averages
Methods

Forecast lags with increasing demand
Forecast leads with decreasing demand
Exponential Smoothing
Methods
 Single Exponential Smoothing
–– Similar to single Moving Average
 Double (Holt’s) Exponential Smoothing

–– Similar to double Moving Average
–– Estimates trend
 Triple (Winter’s) Exponential Smoothing

–– Estimates trend and seasonality
Single Exponential Smoothing
Holt’s Exponential smoothing
(Double Exponential Smoothing)
   Sometimes called exponential smoothing with
    trend.
   If trend exists, single exponential smoothing
    may need adjustment.
   There is a need to add a second smoothing
    constant to account for trend.
   It adds a growth factor (or trend factor) to the
    smoothing equation as a way of adjusting for the
    trend
Winter’s Exponential Smoothing
(Triple Exponential Smoothing)
   Winter’s exponential smoothing model is the
    second extension of the basic Exponential
    smoothing model.
   It is used for data that exhibit both trend and
    seasonality.
   It is a three parameter model that is an extension of
    Holt’s method.
   An additional equation adjusts the model for the
    seasonal component.
TREND ANALYSIS

   Forecasting method used in causal quantitative
    analysis based upon linear regression analysis.
    The dependent variable should have a causal
    relationship with the independent variable.
   For eg.
   Dependent variable : No. of units produced
   Independent variable : No. of labors present
Trend Analysis Chart
MEASUREMENT OF
FORECASTING ERRORS

   Running sum of forecast errors
   Mean forecast error
   Mean absolute deviation
   Mean squared error
   Mean absolute percentage error
   Tracking signal
Tracking signal
   Dynamic measure of forecasting errors as can be
    updated after every time new actual demand data
    is added.
   TS=RSFE/MAD
   In ideal forecast system ,TS should hover closely
    around zero.
   Region above centre zero line shows
      Actual demand > forecast
   Region below centre zero line shows
      Actual demand < forecast
Tracking signal plotted against number of
                   days
Forecast Control Limits

   Used in controlling the forecasting errors.
   Here assumed that forecasting errors follow a
    normal distribution curve and are randomly
    distributed around the mean(assumed,=0).
   Forecasting system is said to be performing well if
    all the forecast error points fall within the control
    limit.
   Upper control limit= 0+3s (s=(MSE)½)
   Lower control limit= 0-3s (s=(MSE)½)
   Any point not lying in the limit is a signal to
    forecaster to look for cause of variation.

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Demand forecasting

  • 1. DEMAND FORECASTING -PRESEN TED BY- 2. Gautam Agarwal 3. Hitesh Agarwal 11. Kandarp Desai 15. Vaibhav Gumaste 26. Omkar Kelkar 29. Aditya Krishnan
  • 2. OBJECTIVES FOR DEMAND FORECASTING • Understand the role of demand forecasting • Identify reasons for demand forecasting • Study of Forecasting methodologies • Selecting the right forecasting method. • Measurement of forecasting errors.
  • 3. INTRODUCTION  Predicting future demand of products/services of an organisation  Forecast = To estimate/calculate in advance.  Guiding factor- for deciding the capacity and location of new facility.  The staffing decisions should be in line with the demand forecasts.  It affects administrative plans and policies.
  • 4. To minimize Maximize losses of gains for To offset uncontrollabl actions of the actions e events organisation of competitor Maximize gains for external Material environmen REASONS FOR requiremen t DEMAND t planning FORECASTING To develop In decision policies making for To provide budgeting To develop adequate administrativ staff to e plans support requirement s
  • 6. Qualitative Analysis 1) Consumers Survey: Complete Enumeration Method  The forecaster undertakes a complete survey of all consumers whose demand he intends to forecast. Once this information is collected, the sales forecasts are obtained by simply adding the probable demands of all consumers. The principle merit of this method is that the forecaster does not introduce any bias or value judgment of his own. But it is a very tedious and cumbersome process; it is not feasible where a large number of consumers are involved
  • 7. 2) Consumer Survey-Sample Survey Method Under this method, the forecaster selects a few consuming units out of the relevant population and then collects data on their probable demands for the product during the forecast period. The total demand of sample units is finally blown up to generate the total demand forecast. Compared to the former survey, this method is less tedious and less costly, and subject to less data error; but the choice of sample is very critical.  If the sample is properly chosen, then it will yield dependable results; otherwise there may be sampling error.
  • 8. 3) Sales Force Composite The sales force composite method of forecasting starts with the forecaster asking for opinions about future sales from every member of the sales staff currently working in the field. Each sales force member states how many sales she thinks she'll make during the given forecasting period. Department managers look over and adjust salespeople's predictions before turning the numbers over for forecasting. Predictions are usually checked against historical
  • 9. 4) Executive Opinion Poll  Forecasters using the executive opinion or expert opinion method poll executives or experts from within the company and ask their opinion on the optional sales for the given forecasting time period. The forecaster will then average the individual judgments or try for a group consensus. Executive opinion polls are often used to verify (or invalidate) other qualitative methods, especially sales force composites.
  • 10. 5) Delphi Method  Dis-advantages: Biased , non-response situation , time consuming.  Advantages: No pressure.
  • 11. 6) Past Analogies Sometimes data on the exact time of a particular event (or events) are available. Experts use cases where similar events have occurred at different times or in different geographic areas and compare them to the issue at hand.  If occurrence or no-occurrence of an event is on a regular basis, then the data can be thought of as having a repeated measurement structure.  It helps to select a large number of similar situations, rather than basing a decision on comparison with only one case.
  • 12. Quantitative analysis  Forecast future demand by using quantitative data from the past and extrapolating it to make forecasts of future levels.  Demand for existing products can be forecasted by using this method.  They are used when historical data is available.  There are of two types of techniques 1. Time series analysis 2. Causal analysis
  • 13. Time series analysis  Time series of historical demand data with respect to time intervals (periods) in the past is used to make predictions for the future demand. Following are the five popular methods  Simple moving average  Simple exponential smoothing  Holt’s double- exponential smoothing  Winters’ triple- exponential smoothing  Forecasting by Linear regression analysis
  • 14. Simple moving average  It is suitable under situations where there is neither a growth nor a decline trend shown by the actual past data for forecasting.  For eg : If we have past data of the actual sales of a product for the months of Jan, Feb and March, we take the simple average of these sales figures for the three months. This simple average becomes the forecast for the next month i.e April.
  • 15. Simple Moving Average Method Example : four week moving average Example: Three Period Moving average. Given below are the actual sale of a toy for the past 5 weeks. We need to predict the sales for the 6th week. W EEK ACTUAL FORECAST CALCULATION SALES (IN UNITS) (IN UNITS) 1 1634 2 1821 3 2069 4 1952 5 2178 1869 (1634+1821+2069+ 1952)/4 6 2005 (1821+2069+1952+2178)/4
  • 16. Weighted Moving Average Method The data in the recent past periods should be given more weight or importance compared to the data in the periods far off from the current time. W EEK ACTUAL FOR ECAST CALCULATION SALE (IN (IN UNITS) UNITS) 1 1634(0.1) 2 1821(0.2) 3 2069(0.3) 4 1952(0.4) 5 1929 (1634*0.1+1821*0.2+20 69*0.3+1952*0.4)/ 1
  • 17. Linear Regression Analysis  It is applied in situations where two variables are linearly correlated to each other.  In time series analysis, the independent variable is time while the dependent variable is the actual demand in the past.  A graph showing the points for the corresponding values of two variables is called scatter diagram. These points should display an approximately linear trend.
  • 18. Example of linear regression Y= 1060X + 440 is the regression equation Interpretation: As the age of the car increase by 1 year the maintenance cost is expected to increase by Rs1060.
  • 19. How to choose a demand forecasting technique  Objectives of a forecast  Cost involved  Time perspective (short run or long run)  Complexity of the technique  Nature and quality of available data
  • 20. QUANTITATIVE ANALYSIS EXPONENTIAL SMOOTHING METHODS
  • 21. The problem with Moving Averages Methods Forecast lags with increasing demand Forecast leads with decreasing demand
  • 22. Exponential Smoothing Methods  Single Exponential Smoothing –– Similar to single Moving Average  Double (Holt’s) Exponential Smoothing –– Similar to double Moving Average –– Estimates trend  Triple (Winter’s) Exponential Smoothing –– Estimates trend and seasonality
  • 24. Holt’s Exponential smoothing (Double Exponential Smoothing)  Sometimes called exponential smoothing with trend.  If trend exists, single exponential smoothing may need adjustment.  There is a need to add a second smoothing constant to account for trend.  It adds a growth factor (or trend factor) to the smoothing equation as a way of adjusting for the trend
  • 25. Winter’s Exponential Smoothing (Triple Exponential Smoothing)  Winter’s exponential smoothing model is the second extension of the basic Exponential smoothing model.  It is used for data that exhibit both trend and seasonality.  It is a three parameter model that is an extension of Holt’s method.  An additional equation adjusts the model for the seasonal component.
  • 26. TREND ANALYSIS  Forecasting method used in causal quantitative analysis based upon linear regression analysis.  The dependent variable should have a causal relationship with the independent variable.  For eg.  Dependent variable : No. of units produced  Independent variable : No. of labors present
  • 28. MEASUREMENT OF FORECASTING ERRORS  Running sum of forecast errors  Mean forecast error  Mean absolute deviation  Mean squared error  Mean absolute percentage error  Tracking signal
  • 29. Tracking signal  Dynamic measure of forecasting errors as can be updated after every time new actual demand data is added.  TS=RSFE/MAD  In ideal forecast system ,TS should hover closely around zero.  Region above centre zero line shows Actual demand > forecast  Region below centre zero line shows Actual demand < forecast
  • 30. Tracking signal plotted against number of days
  • 31. Forecast Control Limits  Used in controlling the forecasting errors.  Here assumed that forecasting errors follow a normal distribution curve and are randomly distributed around the mean(assumed,=0).  Forecasting system is said to be performing well if all the forecast error points fall within the control limit.  Upper control limit= 0+3s (s=(MSE)½)  Lower control limit= 0-3s (s=(MSE)½)  Any point not lying in the limit is a signal to forecaster to look for cause of variation.

Editor's Notes

  1. In this method, the forecast is the average of the last “x” number of observations, where “x” is some suitable number. Suppose a forecaster wants to generate three-period moving averages. In the three-period example, the moving averages method would use the average of the most recent three observations of data in the time series as the forecast for the next period. This forecasted value for the next period, in conjunction with the last two observations of the historical time series, would yield an average that can be used as the forecast for the second period in the future. The calculation of a three-period moving average is illustrated in following table. In calculating moving averages to generate forecasts, the forecaster may experiment with different-length moving averages. The forecaster will choose the length that yields the highest accuracy for the forecasts generated.
  2. The difference between trend analysis and linear regression is that the independent variables can be any other variable except time.
  3. Day 21,22,23 form a straight line which is best fit line
  4. It is always desired that demand forecast value should be as close as possible to the actual demand . But some forecasting errors do take place and we need to measure them so that steps to minimize them can be taken.
  5. Mean forecast error assumed to be =0. The causes may be temporary shortage, natural phenomena such as change in weather conditions, mistake in calculation etc.