05 Forecasting

Topics: Moving average, Time series, Time series analysis Pages: 36 (3988 words) Published: November 11, 2014
9/5/14

Chapter 5

Forecasting

To accompany
Quantitative Analysis for Management, Tenth Edition,
by Render, Stair, and Hanna
Power Point slides created by Jeff Heyl

© 2008 Prentice-Hall, Inc.
© 2009 Prentice-Hall, Inc.

Introduction
n  Managers are always trying to reduce

uncertainty and make better estimates of what
will happen in the future
n  This is the main purpose of forecasting
n  Some firms use subjective methods
n  Seat-of-the pants methods, intuition,
experience
n  There are also several quantitative techniques
n  Moving averages, exponential smoothing,
trend projections, least squares regression
analysis`

© 2009 Prentice-Hall, Inc.

5–2

1

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Introduction
n  Eight steps to forecasting :

1.  Determine the use of the forecast—what
objective are we trying to obtain?
2.  Select the items or quantities that are to be
forecasted
3.  Determine the time horizon of the forecast
4.  Select the forecasting model or models
5.  Gather the data needed to make the
forecast
6.  Validate the forecasting model
7.  Make the forecast
8.  Implement the results
© 2009 Prentice-Hall, Inc.

5–3

Introduction
n  These steps are a systematic way of initiating,

n 

n 
n 
n 
n 

designing, and implementing a forecasting
system
When used regularly over time, data is collected
routinely and calculations performed
automatically
There is seldom one superior forecasting system
Different organizations may use different
techniques
Whatever tool works best for a firm is the one
they should use
Assumptions
n  The future event is determined by the past.
n  Past data are available
© 2009 Prentice-Hall, Inc.

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2

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Forecasting Models
Forecasting
Techniques
Qualitative
Models

Time-Series
Methods

Causal
Methods

Delphi
Methods

Moving
Average

Regression
Analysis

Jury of Executive
Opinion

Exponential
Smoothing

Multiple
Regression

Sales Force
Composite

Trend
Projections

Consumer
Market Survey

Decomposition

Figure 5.1

© 2009 Prentice-Hall, Inc.

5–5

Time-Series Models
n  Time-series models attempt to predict the

future based on the past
n  Common time-series models are
n  Naïve

n  Simple moving average and weighted moving

average
n  Exponential smoothing
n  Trend projections
n  Decomposition

n  Regression analysis is used in trend

projections and one type of decomposition
model
© 2009 Prentice-Hall, Inc.

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3

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Causal Models
n  Causal models use variables or factors

that might influence the quantity being
forecasted
n  The objective is to build a model with
the best statistical relationship between
the variable being forecast and the
independent variables
n  Regression analysis is the most
common technique used in causal
modeling
© 2009 Prentice-Hall, Inc.

5–7

Qualitative Models
n  Qualitative models incorporate judgmental

or subjective factors
n  Useful when subjective factors are
thought to be important or when accurate
quantitative data is difficult to obtain
n  Common qualitative techniques are
n  Delphi method
n  Jury of executive opinion
n  Sales force composite
n  Consumer market surveys

© 2009 Prentice-Hall, Inc.

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4

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Qualitative Models
n  Delphi Method – an iterative group process where

(possibly geographically dispersed) respondents
provide input to decision makers
n  Jury of Executive Opinion – collects opinions of a small group of high-level managers, possibly
using statistical models for analysis
n  Sales Force Composite – individual salespersons
estimate the sales in their region and the data is
compiled at a district or national level
n  Consumer Market Survey – input is solicited from
customers or potential customers regarding their
purchasing plans

© 2009 Prentice-Hall, Inc.

5–9

Scatter Diagrams

Annual Sales

Scatter...
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