a prediction or estimate of future events, especially coming weather or a financial trend.

1. 7/22/2014 1Group 6

2. Forecasting & Methods
Group 6: Bajji Reddy
Kruttika
Vinodh
Mukesh

3. “Predict or estimate (a future event or trend)”.
Or
“Estimating the MAGNITUDE & TIMING of occurrence of
future events.”
What is Forecasting ?
7/22/2014 3Group 6

4. Why Forecasting?
 Forecasting lays a ground for reducing the risk
in all decision making because many of the
decisions need to be made under uncertainty.
 In business applications, forecasting serves as
a starting point of major decisions in finance,
marketing, productions, and purchasing.
7/22/2014 4Group 6

5. Decisions Requiring Forecasting in
Operations Management
 Predicting demands of new and existing products
 Predicting results of new product research and
development
 Projecting quality improvement
 Anticipating customer’s needs
 Predicting cost of materials
7/22/2014 5Group 6

6. Decisions Relevant to Demand Forecasts
 Predicting new facility location.
 Anticipating capacity needs.
 Identifying labor requirements.
 Projecting material requirements.
 Developing production schedules.
 Creating maintenance schedules.
7/22/2014 6Group 6

7.  Simple Moving Average
 Weighted Moving Average
 Exponentially Weighted Moving Average
(Exponential Smoothening)
Forecasting Methods for random demand
7/22/2014 7Group 6

8.  MA is a series of arithmetic means
 Used if little or no trend, seasonal, and cyclical patterns.
 Used often for smoothing
 Provides overall impression of data over time
 Equation
Moving Average Method
MA
n
n

 Demand in Previous Periods
7/22/2014 8Group 6

9. Moving Average Solution
Time Response
Yi
Moving
Total
(n=3)
Moving
Average
(n=3)
1995 4 NA NA
1996 6 NA NA
1997 5 NA NA
1998 3 4+6+5=15 15/3 = 5
1999 7
2000 NA
7/22/2014 9Group 6

10. Time Response
Yi
Moving
Total
(n=3)
Moving
Average
(n=3)
1995 4 NA NA
1996 6 NA NA
1997 5 NA NA
1998 3 4+6+5=15 15/3 = 5
1999 7 6+5+3=14 14/3=4 2/3
2000 NA
Moving Average Solution
7/22/2014 10Group 6

11. Time Response
Yi
Moving
Total
(n=3)
Moving
Average
(n=3)
1995 4 NA NA
1996 6 NA NA
1997 5 NA NA
1998 3 4+6+5=15 15/3=5.0
1999 7 6+5+3=14 14/3=4.7
2000 NA 5+3+7=15 15/3=5.0
Moving Average Solution
7/22/2014 11Group 6

12.  Used when trend is present
 Older data usually less important
 Weights based on intuition
 Often lay between 0 & 1, & sum to 1.0
 Equation
WMA =
Σ(Weight for period n) (Demand in period n)
Σ Weights
Weighted Moving Average Method
7/22/2014 12Group 6

13. Example
Week Actual Data Weight
7 85
8 100
9 110
Calculate the forecast for 10th week?
Weights of 3 weeks are 0.50,0.30 & 0.20.
7/22/2014 13Group 6

14. Form of weighted moving average
Weights decline exponentially
Most recent data weighted most
Requires smoothing constant (α)
Ranges from 0 to 1
Subjectively chosen
Involves little record keeping of past data
Exponential Smoothing Method
7/22/2014 14Group 6

15.  Ft = Ft-1 + (At-1 - Ft-1)
= At-1 + (1 - ) Ft-1
Ft = Forecast value
At = Actual value
 = Smoothing constant
Exponential Smoothing Equations
7/22/2014 15Group 6

16. You’re organizing a Kwanza meeting. You want to forecast attendance for
year 2000 using exponential smoothing ( = 0.10). In1995 (made in 1994)
forecast was 175.
Exponential Smoothing Example
Year Actual Data
1995 180
1996 168
1997 159
1998 175
1999 190
7/22/2014 16Group 6

17. Ft = Ft-1 +  · (At-1 - Ft-1)
Time Actual
Forecast, F t
(α = .10)
1995 180 175.00 (Given)
1996 168
1997 159
1998 175
1999 190
2000 NA
175.00 +
Exponential Smoothing Solution
7/22/2014 17Group 6

18. Time Actual
Forecast, F t
(α = .10)
1995 180 175.00 (Given)
1996 168 175.00 + .10(180 -
1997 159
1998 175
1999 190
2000 NA
Ft = Ft-1 +  · (At-1 - Ft-1)
7/22/2014 18Group 6

19. Time Actual
Forecast, F t
(α = .10)
1995 180 175.00 (Given)
1996 168 175.00 + .10(180 - 175.00)
1997 159
1998 175
1999 190
2000 NA
Ft = Ft-1 +  · (At-1 - Ft-1)
7/22/2014 19Group 6

20. Time Actual
Forecast, F t
(α = .10)
1995 180 175.00 (Given)
1996 168 175.00 + .10(180 - 175.00) = 175.50
1997 159
1998 175
1999 190
2000 NA
Ft = Ft-1 +  · (At-1 - Ft-1)
7/22/2014 20Group 6

21. Time Actual
Forecast, F t
(α = .10)
1995 180 175.00 (Given)
1996 168 175.00 + .10(180 - 175.00) = 175.50
1997 159 175.50 + .10(168 - 175.50) = 174.75
1998 175
1999 190
2000 NA
Ft = Ft-1 +  · (At-1 - Ft-1)
7/22/2014 21Group 6

22. Time Actual
Forecast, F t
(α = .10)
1995 180 175.00 (Given)
1996 168 175.00 + .10(180 - 175.00) = 175.50
1997 159 175.50 + .10(168 - 175.50) = 174.75
1998 175
1999 190
2000 NA
174.75 + .10(159 - 174.75)= 173.18
Ft = Ft-1 +  · (At-1 - Ft-1)
7/22/2014 22Group 6

23. Time Actual
Forecast, F t
(α = .10)
1995 180 175.00 (Given)
1996 168 175.00 + .10(180 - 175.00) = 175.50
1997 159 175.50 + .10(168 - 175.50) = 174.75
1998 175 174.75 + .10(159 - 174.75) = 173.18
1999 190 173.18 + .10(175 - 173.18) = 173.36
2000 NA
Ft = Ft-1 +  · (At-1 - Ft-1)
7/22/2014 23Group 6

24. Time Actual
Forecast, F t
(α = .10)
1995 180 175.00 (Given)
1996 168 175.00 + .10(180 - 175.00) = 175.50
1997 159 175.50 + .10(168 - 175.50) = 174.75
1998 175 174.75 + .10(159 - 174.75) = 173.18
1999 190 173.18 + .10(175 - 173.18) = 173.36
2000 NA 173.36 + .10(190 - 173.36) = 175.02
Ft = Ft-1 +  · (At-1 - Ft-1)
7/22/2014 24Group 6

25. Any Queries ?
7/22/2014 25Group 6

26. Thank you
7/22/2014 26Group 6