Lecture Operations and supply chain management: The Core (3/e) – Chapter 3: Forecasting

Forecasting
Chapter 03
McGraw­Hill/Irwin

        Copyright © 2013 by The McGraw­Hill Companies, Inc. All rights reserved.


Learning Objectives
1.
2.
3.

4.
5.
6.

Understand the role of forecasting as a basis for
supply chain planning
Identify the basic components of demand:
average, trend, seasonal, and random variation
Show how to make a time series forecast using
moving averages, exponential smoothing, and
regression
Use decomposition to forecast when trend and
seasonality is present
Show how to measure forecast error
Describe the common qualitative forecasting
techniques, such as the Delphi method and
collaborative forecasting
3­2

The Role of Forecasting


Forecasting is a vital function and impacts every
significant management decision







Finance and accounting use forecasts as the basis for
budgeting and cost control
Marketing relies on forecasts to make key decisions
such as new product planning and personnel
compensation
Production uses forecasts to select suppliers,
determine capacity requirements, and to drive
decisions about purchasing, staffing, and inventory

Different roles require different forecasting
approaches



Decisions about overall directions require strategic
forecasts
Tactical forecasts are used to guide day-to-day

decisions

3­3


Components of Demand

Excel: Components

3­4


Time Series Analysis


Using the past to predict the future

3­5


Forecasting Method Selection
Guide
Fo re c as ting  Me tho d

Amo unt o f His to ric al 
Data

Data Patte rn

Fo re c as t 

Ho rizo n

Simple moving
average

6 to 12 months; weekly
data are often used

Stationary (i.e. no
trend or
seasonality)

Short

Weighted moving
5 to 10 observations
average and simple
needed to start
exponential smoothing

Stationary

Short

Exponential smoothing 5 to 10 observations
with trend
needed to start

Stationary and
trend

Short

Linear regression

Stationary, trend,
and seasonality

Short to
Medium

10 to 20 observations

3­6


Forecast Error Measurements




Ideally, MAD will be zero
(no forecasting error)
Larger values of MAD
indicate a less accurate
model





MAPE scales the forecast error
to the magnitude of demand

Tracking signal indicates
whether forecast errors are
accumulating over time (either
positive or negative errors)

3­7


Computing Forecast Error

3­8


Causal Relationship
Forecasting


Causal relationship forecasting uses
independent variables other than time to
predict future demand




This independent variable must be a leading
indicator

Many apparently causal relationships are
actually just correlated events – care must be
taken when selecting causal variables

3­9


Multiple Regression Techniques




Often, more than one independent variable
may be a valid predictor of future demand
In this case, the forecast analyst may utilize
multiple regression




Analogous to linear regression analysis, but with
multiple independent variables
Multiple regression is supported by statistical
software packages

3­10


Qualitative Forecasting

Techniques






Generally used to take advantage of expert
knowledge
Useful when judgment is required, when
products are new, or if the firm has little
experience in a new market
Examples





Market research
Panel consensus
Historical analogy
Delphi method
3­11


Collaborative Planning,
Forecasting, and Replenishment
(CPFR)


A web-based process used to coordinate the
efforts of a supply chain








Demand forecasting
Production and purchasing
Inventory replenishment

Integrates all members of a supply chain –
manufacturers, distributors, and retailers
Depends upon the exchange of internal
information to provide a more reliable view of
demand
3­12


CPFR Steps

3­13


Principles








Forecasting is a fundamental step in any
planning process
Forecast effort should be proportional to the
magnitude of decisions being made
Web-based systems (CPFR) are growing in
importance and effectiveness
All forecasts have errors – understanding and
minimizing this error is the key to effective
forecasting processes
3­14