Operation management 9e stevenson mcgrwhill chap003

3

Forecasting

McGraw-Hill/Irwin

Copyright © 2007 by The McGraw-Hill Companies, Inc. All


Learning Objectives






List the elements of a good forecast.
Outline the steps in the forecasting process.
Describe at least three qualitative forecasting
techniques and the advantages and
disadvantages of each.
Compare and contrast qualitative and quantitative
approaches to forecasting.

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Learning Objectives







Briefly describe averaging techniques, trend and
seasonal techniques, and regression analysis,
and solve typical problems.
Describe two measures of forecast accuracy.
Describe two ways of evaluating and controlling
forecasts.
Identify the major factors to consider when
choosing a forecasting technique.

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FORECAST:
 A statement about the future value of a
variable of interest such as demand.
 Forecasting is used to make informed
decisions.
 Long-range
 Short-range

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Forecasts
 Forecasts affect decisions and activities throughout
an organization

 Accounting, finance
 Human resources
 Marketing
 MIS
 Operations
 Product / service design

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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

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Features of Forecasts
 Assumes causal system
past ==> future
 Forecasts rarely perfect because of randomness
 Forecasts more accurate for
groups vs. individuals
 Forecast accuracy decreases
as time horizon increases

I see that you will
get an A this semester.

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Elements of a Good Forecast
Timely

Reliable
f
g
n
i
n
a
e
M

ul

Accurate

Written

y
s
Ea

to

e
s
u

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Steps in the Forecasting Process

“The forecast”

Step 6 Monitor the forecast
Step 5 Make the forecast
Step 4 Obtain, clean and analyze data
Step 3 Select a forecasting technique
Step 2 Establish a time horizon
Step 1 Determine purpose of forecast

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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

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Judgmental Forecasts
 Executive opinions
 Sales force opinions
 Consumer surveys
 Outside opinion


Delphi method
 Opinions of managers and staff
 Achieves a consensus forecast

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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

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Figure 3.1

Forecast Variations
Irregular
variation

Trend

Cycles
90

89
88
Seasonal variations
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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.

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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

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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))

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Techniques for Averaging
 Moving average
 Weighted moving average
 Exponential smoothing

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Moving Averages
 Moving average – A technique that averages a
number of recent actual values, updated as
new values become available.

Ft = MAn=

At-n + … At-2 + At-1
n

 Weighted moving average – More recent
values in a series are given more weight in
computing the forecast.

Ft = WMAn=

wnAt-n + … wn-1At-2 + w1At-1
n
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Simple Moving Average
Actual

MA5

MA3

Ft = MAn=

At-n + … At-2 + At-1
n
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Exponential Smoothing

Ft = Ft-1 + α(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.

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Exponential Smoothing

Ft = Ft-1 + α(At-1 - Ft-1)
 Weighted averaging method based on previous
forecast plus a percentage of the forecast error
 A-F is the error term, α is the % feedback

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Example 3 - Exponential Smoothing
Period

Actual
1

2
3
4
5
6
7
8
9
10
11
12

Alpha = 0.1 Error
42
40
43
40
41
39
46
44
45
38
40

42
41.8
41.92
41.73
41.66

41.39
41.85
42.07
42.36
41.92
41.73

Alpha = 0.4 Error
-2.00
1.20
-1.92
-0.73
-2.66
4.61
2.15
2.93
-4.36
-1.92

42
41.2
41.92
41.15
41.09
40.25
42.55
43.13
43.88
41.53
40.92

-2
1.8
-1.92
-0.15
-2.09
5.75
1.45
1.87
-5.88
-1.53

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Picking a Smoothing Constant
Actual

Demand

50

α = .
4

45

α = .1

40

35
1

2

3

4

5

6

7

8

9 10 11 12

Period

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Common Nonlinear Trends
Figure 3.5

Parabolic

Exponential

Growth

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Linear Trend Equation
Ft

Ft = a + bt
0 1 2 3 4 5





t

Ft = Forecast for period t
t = Specified number of time periods
a = Value of Ft at t = 0
b = Slope of the line

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