Reid & Sanders, Operations Management © Wiley 2002 Forecasting 8 C H A P T E R.

Reid & Sanders, Operations Management© Wiley 2002

Forecasting 8C H A P T E R

Page 2Reid & Sanders, Operations Management© Wiley 2002

Learning Objectives

• Identify the principles of forecasting• Explain the forecasting process• Describe forecasting methods:

– Time series and casual models – Incorporating trends, seasonality and cycles

• Describe casual modeling using linear regression• Compute forecast accuracy• Explain factors to consider when selecting a

forecasting method


Principles of Forecasting

• Forecasts are rarely perfect

• Grouped forecasts are more accurate than individual items

• Forecast accuracy is higher for shorter time horizons


Step-by-Step

• Decide what to forecast:– Level of detail, units of analysis & time horizon

required

• Evaluate & analyze appropriate data– Identify needed data & whether it’s available

• Select & test the forecasting model– Cost, ease of use & accuracy

• Generate the forecast• Monitor forecast accuracy over time


Types of Forecasting Methods

• Qualitative methods:– Forecasts generated subjectively by the

forecaster

• Quantitative methods:– Forecasts generated through mathematical

modeling


Qualitative Methods

• Strengths:– Incorporates inside information– Particularly useful when the future is

expected to be very different than the past

• Weaknesses:– Forecaster bias can reduce the accuracy of

the forecast


Types of Qualitative Models

Type Characteristics Strengths WeaknessesExecutive opinion

A group of managers meet & come up with a forecast

Good for strategic or new-product forecasting

One person's opinion can dominate the forecast

Market research

Uses surveys & interviews to identify customer preferences

Good determinant of customer preferences

It can be difficult to develop a good questionnaire

Delphi method

Seeks to develop a consensus among a group of experts

Excellent for forecasting long-term product demand, technological changes, and

Time consuming to develop


Quantitative Methods

• Strengths:– Consistent and objective– Can consider a lot of data at once

• Weaknesses:– Necessary data isn’t always available– Forecast quality is dependent upon data

quality


Types of Quantitative Methods

• Time Series Models:– Assumes the future will follow same

patterns as the past

• Causal Models:– Explores cause-and-effect relationships– Uses leading indicators to predict the

future


Patterns in Time Series Data


Logic of Time Series Models

• Data = historic pattern + random variation

• Historic pattern may include: – Level (long-term average) – Trend – Seasonality – Cycle


Time Series Models

• Naive:– The forecast is equal to the actual value observed

during the last period

• Simple Mean:– The average of all available data

• Moving Average:– The average value over a set time period (e.g.: the

last four weeks)– Each new forecast drops the oldest data point &

adds a new observation


Weighted Moving Average

• All weights must add to 100% or 1.00

• Allows the forecaster to emphasize one period over others

• Differs from the simple moving average that weights all periods equally

ttt ACF 1


Exponential Smoothing

• Forecast quality is highly dependent on selection of alpha:– Low alpha values generate more stable forecasts– High alpha values generate forecasts that respond

quickly to recent data

• Issue is whether recent changes reflect random variation or real change in long-term demand

ttt FAF 11


Forecasting Trends

• Trend-adjusted exponential smoothing

• Three step process:– Smooth the level of the series:

– Smooth the trend:

– Calculate the forecast including trend:

))(1( 11 tttt TSAS

11 )1()( tttt TSST

ttt TSFIT 1


Adjusting for Seasonality

• Calculate the average demand per season– E.g.: average quarterly demand

• Calculate a seasonal index for each season of each year:– Divide the actual demand of each season by the

average demand per season for that year

• Average the indexes by season– E.g.: take the average of all Spring indexes, then

of all Summer indexes, ...


Adjusting for Seasonality

• Forecast demand for the next year & divide by the number of seasons– Use regular forecasting method & divide by four

for average quarterly demand

• Multiply next year’s average seasonal demand by each average seasonal index– Result is a forecast of demand for each season of

next year


Casual Models

• Often, leading indicators hint can help predict changes in demand

• Causal models build on these cause-and-effect relationships

• A common tool of causal modeling is linear regression:

bxaY


Linear Regression


Forecast Accuracy

• Forecasts are rarely perfect• Need to know how much we should rely on

our chosen forecasting method• Measuring forecast error:

• Note that over-forecasts = negative errors and under-forecasts = positive errors

ttt FAE


Tracking Forecast ErrorOver Time

• Mean Absolute Deviation (MAD):– A good measure of the actual error

in a forecast

• Mean Square Error (MSE):– Penalizes extreme errors

• Tracking Signal– Exposes bias (positive or negative)

1

forecast - actual2

nMSE

MAD

TS forecast - actual

nMAD

forecastactual


Factors for Selecting a Forecasting Model

• The amount & type of available data

• Degree of accuracy required

• Length of forecast horizon

• Presence of data patterns


The End

Copyright © 2002 John Wiley & Sons, Inc. All rights reserved. Reproduction or translation of this work beyond that permitted in Section 117 of the 1976 United State Copyright Act without the express written permission of the copyright owner is unlawful. Request for further information should be addressed to the Permissions Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages, caused by the use of these programs or from the use of the information contained herein.

Reid & Sanders, Operations Management © Wiley 2002 Forecasting 8 C H A P T E R.

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