Operations management 12th stevenson ch03 forecasting

Chapter 3
Forecasting

McGraw-Hill/Irwin

Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.


Chapter 3: Learning Objectives

 You should be able to:
1.
2.
3.
4.
5.
6.
7.
8.

Instructor Slides

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
Describe averaging techniques, trend and seasonal techniques, and regression analysis,
and solve typical problems
Explain three measures of forecast accuracy
Compare two ways of evaluating and controlling forecasts

Assess the major factors and trade-offs to consider when choosing a forecasting technique

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Forecast

Forecast – a statement about the future value of a variable of interest
 We make forecasts about such things as weather, demand, and resource
availability

 Forecasts are an important element in making informed decisions

Instructor Slides

3-3


Forecasts affect decisions and activities throughout an organization

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


Two Important Aspects of Forecasts

Expected level of demand
 The level of demand may be a function of some structural variation such as trend
or seasonal variation

Accuracy
 Related to the potential size of forecast error

Instructor Slides

3-5


Features Common to All Forecasts

1.

Techniques assume some underlying causal system that existed in the past will
persist into the future

2.

Forecasts are not perfect

3.

Forecasts for groups of items are more accurate than those for individual items

4.

Forecast accuracy decreases as the forecasting horizon increases

Instructor Slides

3-6


Elements of a Good Forecast
The forecast



should be timely



should be accurate



should be reliable



should be expressed in meaningful units



should be in writing



technique should be simple to understand and use



should be cost effective

Instructor Slides

3-7


Steps in the Forecasting Process

1.

Determine the purpose of the forecast

2.

Establish a time horizon

3.

Obtain, clean, and analyze appropriate data

4.

Select a forecasting technique

5.

Make the forecast

6.

Monitor the forecast

Instructor Slides

3-8


Forecasting Approaches
 Qualitative Forecasting


Qualitative techniques permit the inclusion of soft information such as:

 Human factors
 Personal opinions
 Hunches


These factors are difficult, or impossible, to quantify

 Quantitative Forecasting


Quantitative techniques involve either the projection of historical data or the development of associative
methods that attempt to use causal variables to make a forecast



These techniques rely on hard data


Judgmental Forecasts

Forecasts that use subjective inputs such as opinions from consumer
surveys, sales staff, managers, executives, and experts

 Executive opinions
 Salesforce opinions
 Consumer surveys
 Delphi method


Time-Series Forecasts

Forecasts that project patterns identified in recent time-series observations
 Time-series - a time-ordered sequence of observations taken at regular time
intervals

Assume that future values of the time-series can be estimated from past values
of the time-series

Instructor Slides

3-11


Time-Series Behaviors

Trend
Seasonality
Cycles
Irregular variations
Random variation

Instructor Slides

3-12


Trends and Seasonality

 Trend
 A long-term upward or downward movement in data
 Population shifts
 Changing income

 Seasonality
 Short-term, fairly regular variations related to the calendar or time of day
 Restaurants, service call centers, and theaters all experience seasonal demand


Cycles and Variations

 Cycle
 Wavelike variations lasting more than one year
 These are often related to a variety of economic, political, or even agricultural conditions

 Random Variation
 Residual variation that remains after all other behaviors have been accounted for

 Irregular variation
 Due to unusual circumstances that do not reflect typical behavior
 Labor strike

 Weather event


Time-Series Behaviors

Instructor Slides

3-15


Time-Series Forecasting - Naïve Forecast

Naïve Forecast
 Uses a single previous value of a time series as the basis for a forecast
The forecast for a time period is equal to the previous time period’s value
 Can be used with
a stable time series
seasonal variations
trend

Instructor Slides

3-16


Naïve Forecasts

Forecast for any period = previous period’s actual value

Ft = At-1

F: forecast

A: Actual

t: time period


Naïve Forecast Example

Week Sales (actual) Sales (forecast) Error
t
A
F
A-F
1
20
2
25
20
5
3
15
25
-10
4
30
15
15
5
27

30
-3


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


Uses for Naïve Forecasts


Time-Series Forecasting - Averaging

These Techniques work best when a series tends to vary about an average
 Averaging techniques smooth variations in the data
 They can handle step changes or gradual changes in the level of a series
 Techniques
1.
2.
3.

Instructor Slides

Moving average
Weighted moving average
Exponential smoothing

3-21


Moving Average

Technique that averages a number of the most recent actual values in
generating a forecast
n

Ft = MA n =

∑A

t −i

i =1

n

where
Ft = Forecast for time period t
MA n = n period moving average
At −1 = Actual value in period t − 1
n = Number of periods in the moving average
Instructor Slides

3-22


Moving Average

As new data become available, the forecast is updated by adding the newest
value and dropping the oldest and then re-computing the average

The number of data points included in the average determines the model’s
sensitivity

 Fewer data points used-- more responsive
 More data points used-- less responsive

Instructor Slides

3-23


Moving Average Example

Week

Sales (actual)

Sales (forecast)

Error

t

A

F = MA3

A-F

-

 

20
1

2

25

-

 

3

15

-

 

4

30

20

10

5

27

23.3333

3.66667

6

 

24

 


Simple Moving Average
Figure 3-4 Revised

Forecast (MA3)

Forecast (MA5)

Actual

47
45
43
41
39
37
35
1

2

3

4

5

6

7

8

9

10 11 12

Questions:

•
•
•

Why is MA3 longer than MA5?
Which curve fluctuate the most?
Which curve is the smoothest?

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