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
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Maximize
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FORECASTING
To
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To provide budgeting
To develop adequate
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s

5. VARIOUS METHODS

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

23. Single Exponential Smoothing

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

27. Trend Analysis Chart

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.