1. 3-1 Forecasting
CHAPTER
7
Forecasting
Prepared by :
Arya Wirabhuana, ST, M.Sc

2. 3-2 Forecasting
FORECAST:
 A statement about the future value of a variable of
interest such as demand.
 Forecasts affect decisions and activities throughout
an organization
 Accounting, finance
 Human resources
 Marketing
 MIS
 Operations
 Product / service design

3. 3-3 Forecasting
Uses of Forecasts
Accounting Cost/profit estimates
Finance Cash flow and funding
Human Resources Hiring/recruiting/training
Marketing Pricing, promotion, strategy
MIS IT/IS systems, services
Operations Schedules, MRP, workloads
Product/service design New products and services

4. 3-4 Forecasting
 Assumes causal system
past ==> future
 Forecasts rarely perfect because of
randomness
 Forecasts more accurate for
groups vs. individuals I see that you will
get an A this semester.
 Forecast accuracy decreases
as time horizon increases

5. 3-5 Forecasting
Elements of a Good Forecast
Timely
Reliable Accurate
Written

6. 3-6 Forecasting
Steps in the Forecasting Process
“The forecast”
Step 6 Monitor the forecast
Step 5 Prepare the forecast
Step 4 Gather and analyze data
Step 3 Select a forecasting technique
Step 2 Establish a time horizon
Step 1 Determine purpose of forecast

7. 3-7 Forecasting
Types of Forecasts
 Judgmental - uses subjective inputs
 Time series - uses historical data
assuming the future will be like the past
 Associative models - uses explanatory
variables to predict the future

8. 3-8 Forecasting
Judgmental Forecasts
 Executive opinions
 Sales force opinions
 Consumer surveys
 Outside opinion
 Delphi method
 Opinions of managers and staff
 Achieves a consensus forecast

9. 3-9 Forecasting
Time Series Forecasts
 Trend - long-term movement in data
 Seasonality - short-term regular variations in
data
 Cycle – wavelike variations of more than one
year’s duration
 Irregular variations - caused by unusual
circumstances
 Random variations - caused by chance

10. 3-10 Forecasting
Forecast Variations
Figure 3.1
Irregular
variatio
n
Trend
Cycles
90
89
88
Seasonal variations

11. 3-11 Forecasting
Naive Forecasts
Uh, give me a minute....
We sold 250 wheels last
week.... Now, next week
we should sell....
The forecast for any period equals
the previous period’s actual value.

12. 3-12 Forecasting
Naïve Forecasts
 Simple to use
 Virtually no cost
 Quick and easy to prepare
 Data analysis is nonexistent
 Easily understandable
 Cannot provide high accuracy
 Can be a standard for accuracy

13. 3-13 Forecasting
Uses for Naïve Forecasts
 Stable time series data
 F(t) = A(t-1)
 Seasonal variations
 F(t) = A(t-n)
 Data with trends
 F(t) = A(t-1) + (A(t-1) – A(t-2))

14. 3-14 Forecasting
Techniques for Averaging
 Moving average
 Weighted moving average
 Exponential smoothing

15. 3-15 Forecasting
Moving Averages
 Moving average – A technique that averages a
number of recent actual values, updated as new
values become available.
n
1 Ai
i=
MAn =
n
 Weighted moving average – More recent values in a
series are given more weight in computing the
forecast.

16. 3-16 Forecasting
Simple Moving Average
Actual
MA5
47
45
43
41
39
37 MA3
35
1 2 3 4 5 6 7 8 9 10 11 12
n
1 Ai
i=
MAn =
n

17. 3-17 Forecasting
Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
• Premise--The most recent observations might
have the highest predictive value.
 Therefore, we should give more weight to the
more recent time periods when forecasting.

18. 3-18 Forecasting
Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
 Weighted averaging method based on previous
forecast plus a percentage of the forecast error
 A-F is the error term,  is the % feedback

19. 3-19 Forecasting
Example 3 - Exponential Smoothing
Period Actual Alpha = 0.1 Error Alpha = 0.4 Error
1 42
2 40 42 -2.00 42 -2
3 43 41.8 1.20 41.2 1.8
4 40 41.92 -1.92 41.92 -1.92
5 41 41.73 -0.73 41.15 -0.15
6 39 41.66 -2.66 41.09 -2.09
7 46 41.39 4.61 40.25 5.75
8 44 41.85 2.15 42.55 1.45
9 45 42.07 2.93 43.13 1.87
10 38 42.36 -4.36 43.88 -5.88
11 40 41.92 -1.92 41.53 -1.53
12 41.73 40.92

20. 3-20 Forecasting
Picking a Smoothing Constant
Actual
50
  .4
  .1
Demand
45
40
35
1 2 3 4 5 6 7 8 9 10 11 12
Period

21. 3-21 Forecasting
Common Nonlinear Trends
Figure 3.5
Parabolic
Exponential
Growth

22. 3-22 Forecasting
Linear Trend Equation
Ft
Ft = a + bt
 Ft = Forecast for period t 0 1 2 3 4 5 t
 t = Specified number of time periods
 a = Value of Ft at t = 0
 b = Slope of the line

23. 3-23 Forecasting
Calculating a and b
n  (ty) -  t  y
b =
n t 2 - ( t) 2

 y - b t
a =
n

24. 3-24 Forecasting
Linear Trend Equation Example
t y
2
Week t Sales ty
1 1 150 150
2 4 157 314
3 9 162 486
4 16 166 664
5 25 177 885
 t = 15  t = 55  y = 812  ty = 2499
2
2
( t) = 225

25. 3-25 Forecasting
Linear Trend Calculation
5 (2499) - 15(812) 12495 -12180
b = = = 6.3
5(55) - 225 275 -225
812 - 6.3(15)
a = = 143.5
5
y = 143.5 + 6.3t

26. 3-26 Forecasting
Associative Forecasting
 Predictor variables - used to predict values of
variable interest
 Regression - technique for fitting a line to a set
of points
 Least squares line - minimizes sum of squared
deviations around the line

27. 3-27 Forecasting
Linear Model Seems Reasonable
X Y Computed
7 15
relationship
2 10
6 13 50
4 15 40
14 25 30
15 27 20
10
16 24
0
12 20 0 5 10 15 20 25
14 27
20 44
15 34
7 17
A straight line is fitted to a set of sample points.

28. 3-28 Forecasting
Forecast Accuracy
 Error - difference between actual value and predicted
value
 Mean Absolute Deviation (MAD)
 Average absolute error
 Mean Squared Error (MSE)
 Average of squared error
 Mean Absolute Percent Error (MAPE)
 Average absolute percent error

29. 3-29 Forecasting
MAD, MSE, and MAPE
 Actual  forecast
MAD =
n
2
 ( Actual  forecast)
MSE =
n -1
( Actual  forecas / Actual*100)
MAPE =
t
n

30. 3-30 Forecasting
Example 10
Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*100
1 217 215 2 2 4 0.92
2 213 216 -3 3 9 1.41
3 216 215 1 1 1 0.46
4 210 214 -4 4 16 1.90
5 213 211 2 2 4 0.94
6 219 214 5 5 25 2.28
7 216 217 -1 1 1 0.46
8 212 216 -4 4 16 1.89
-2 22 76 10.26
MAD= 2.75
MSE= 10.86
MAPE= 1.28

31. 3-31 Forecasting
Controlling the Forecast
 Control chart
 A visual tool for monitoring forecast errors
 Used to detect non-randomness in errors
 Forecasting errors are in control if
 All errors are within the control limits
 No patterns, such as trends or cycles, are
present

32. 3-32 Forecasting
Sources of Forecast errors
 Model may be inadequate
 Irregular variations
 Incorrect use of forecasting technique

33. 3-33 Forecasting
Tracking Signal
•Tracking signal
–Ratio of cumulative error to MAD
Tracking signal =
(Actual-forecast)
MAD
Bias – Persistent tendency for forecasts to be
Greater or less than actual values.

34. 3-34 Forecasting
Choosing a Forecasting Technique
 No single technique works in every situation
 Two most important factors
 Cost
 Accuracy
 Other factors include the availability of:
 Historical data
 Computers
 Time needed to gather and analyze the data
 Forecast horizon

35. 3-35 Forecasting
Exponential Smoothing

36. 3-36 Forecasting
Linear Trend Equation

37. 3-37 Forecasting
Simple Linear Regression