The document provides an overview of forecasting techniques. It defines a forecast as a statement about the future value of a variable of interest. Accurate forecasts are important for accounting, finance, human resources, marketing, operations and other business functions. The key types of forecasts discussed are judgmental forecasts, time series forecasts, and associative models. Time series techniques include naive methods, moving averages, weighted moving averages, and exponential smoothing. Accuracy is measured using metrics like mean absolute deviation, mean squared error and mean absolute percentage error. Choosing the right technique depends on factors like cost, required accuracy, available data and time horizon.

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