Forecasting-1.ppt.Brod Forecasting Methods

Broad Forecasting Methods used in Industrial ENGINEERING

1. Quantitative forecasting
These types of forecasting methods are based on
mathematical ( quantitative) models, and are objective in
nature. They rely heavily on mathematical computations.

3. Time series methods
Time series refers to a set of observation
measured at successive periods.
The objective of time series method is to
discover a pattern in the historical data
and then extrapolate trends into the
future

4. In the time series forecasting method, past times
are the best indicators of future trends. By
reviewing historical data over time period, we
can better under stand the pattern of past
behavior of a variable and better predict the
future behavior.

6. Components of time series demand

11. Simple Moving Average
A moving average is a technique to get an overall idea of the
trends in a data set; it is an average of any subset of numbers.
The moving average is extremely useful for forecasting
long-term trends.
We can calculate it for any period of time. For example, if
you have sales data for a twenty-year period, you can
calculate a five-year moving average, a four-year moving
average, a three-year moving average and so on.

14. Using the data given in the Illustration 1 forecast the demand for the
period 2015 to 2023 using
a. 3- year moving average and
b. 5- year moving average
If we want to check the error in our forecast as Error = Actual observed
value – Forecasted value
find which one gives a lower error in the forecast.

15. Year Demand
3 year MV 5 year MV
forecast error forecast error
2015 100 - - - -
2016 105 - - - -
2017 103 - - - -
2018 107 102.6 4.4 - -
2019 109 105.0 4.0 - -
2020 110 106.3 3.7 104.8 5.2
2021 115 108.6 6.4 106.8 8.2
2022 117 111.3 5.7 108.8 8.2
2023 - 114.0 - 111.6 -

16. Weighted Moving Average
Whereas the simple moving average gives equal weight to
each component of the moving average database, a weighted
moving average allows any weights to be placed on each
element, providing, of course, that the sum of all weights
equals 1.

18. Choosing Weights : Experience and trial and error are the
simplest ways to choose weights. As a general rule, the most recent
past is the most important indicator of what to expect in the future,
and, therefore, it should get higher weighting. The past month's
revenue or plant capacity, for example, would be a better estimate
for the coming month than the revenue or plant capacity of several
months ago.

19. However, if the data are seasonal, for example, weights should be
established accordingly. For example, sales of air conditioners in
May of last year should be weighted more heavily than sales of air
conditioners in December.

23. Exponential Smoothing
In the previous methods of forecasting (simple and weighted
moving average), the major drawback is the need to
continually carry a large amount of historical data.
As each new piece of data is added in these methods, the
oldest observation is dropped, and the new forecast is
calculated.

25. The Effect of the Parameter 
A smaller  makes the forecast more
stable
A larger  makes the forecast more
responsive

27. 1. The demand and forecast for February are 12000 and
10275 respectively. Using single exponential smoothening
with smoothening coefficient value 0.25. Find out the
forecast for the month of March.

28. 1.The sales of a product during last four
years were 860, 880, 870 and 890 units.
The forecast for fourth year was 876
units. If the forecast for the fifth year,
using simple exponential smoothing is
equal to the forecast using a three period
moving average, find the value of
exponential smoothing constant.