Demand forecastingDemand estimates for products and services are the starting point for all the other planning in
operations management. Management teams develop sales forecasts based in part on demand
estimates. The sales forecasts become inputs to both business strategy and production resource
forecasts.
Demand forecasting means estimation for future demand by using past data.
It is a process of estimating a future event by casting forward past data
The past data are systematically combined in a predetermined way to obtain the estimate of future.
Significance of forecasting
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Production planning
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Manpower planning
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Helpful in providing base to the economic planning
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Helpful in stock maintaining
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Helpful in increasing sales
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Helpful in financial control
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Qualitative Approaches the summary of an interesting point.
Quantitative Approaches
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You can position the text box Linear Regression
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Survey Method
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Opinion Poll methodWeighted Moving Average
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Opinion Poll Method
Delphi method
Exponential Smoothing (exponentially weighted
moving average)
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Expert Opinion
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Market Experiment
Exponential Smoothing with Trend (double
exponential smoothing)
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Survey Method-
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Complete Enumeration
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Sample survey
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Simple Linear Regression
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Linear regression analysis establishes a relationship between a dependent variable and one
or more independent variables. In simple linear regression analysis there is only one
independent variable.
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If the data is a time series, the independent variable is the time period.
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The dependent variable is whatever we wish to forecast.
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Regression Equation
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This model is of the form:
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Y = a + bX
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Y = dependent variable
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X = independent variable
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a = y-axis intercept
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b = slope of regression line
Simple Moving Average
It is called “moving” because as new demand data becomes available, the oldest data is not
used. By increasing the AP, the forecast is less responsive to fluctuations in demand
By decreasing the AP, the forecast is more responsive to fluctuations in demand.
Weighted Moving Average
This is a variation on the simple moving average where the weights used to compute the
average are not equal. This allows more recent demand data to have a greater effect on the
moving average.
The weights must add to 1.0 and generally decrease in value with the age of the data. The
distribution of the weights determine the impulse response of the forecast.
Exponential Smoothing
The weights used to compute the forecast (moving average) are exponentially distributed.
The forecast is the sum of the old forecast and a portion (α) of the forecast error (A t-1 - Ft-1).
Ft = Ft-1+ α(A t-1 - Ft-1)
The smoothing constant, α, must be between 0.0 and 1.0.
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A large α provides a high impulse response forecast.
A small α provides a low impulse response forecast
Example: Central Call Centre
CCC wishes to forecast the number of incoming calls it receives in a day from the customers .
CCC schedules the appropriate number of telephone operators based on projected call
volumes. CCC believes that the most recent 12 days of call
volumes are representative of the near future call volumes.
Representative Historical Data
Day
Calls
1
159
2
217
3
186
4
161
5
173
6
157
7
203
8
195
9
188
10
168
11
198
12
159
Moving Average
Use the moving average method with an AP = 3 days to develop a forecast of the call volume
in Day13.
F13 = (168 + 198 + 159)/3 = 175.0 calls
Weighted Moving Average
Use the weighted moving average method with an AP = 3 days and weights of .1 (for oldest
datum), .3, and .6 to develop a forecast of the call volume in Day13.
F13 = .1(168) + .3(198) + .6(159) = 171.6 calls
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Note: The WMA forecast is lower than the MA forecast because Day 13’s relatively low call
volume carries almost twice as much weight in the WMA (.60) as it does in the MA (.33).
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