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

Reid & Sanders, Operations Management © Wiley 2002 Page 2 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

Reid & Sanders, Operations Management © Wiley 2002 Page 3 Principles of Forecasting Forecasts are rarely perfect Grouped forecasts are more accurate than individual items Forecast accuracy is higher for shorter time horizons

Reid & Sanders, Operations Management © Wiley 2002 Page 4 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

Reid & Sanders, Operations Management © Wiley 2002 Page 5 Types of Forecasting Methods Qualitative methods: –Forecasts generated subjectively by the forecaster Quantitative methods: –Forecasts generated through mathematical modeling

Reid & Sanders, Operations Management © Wiley 2002 Page 6 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

Reid & Sanders, Operations Management © Wiley 2002 Page 7 Types of Qualitative Models

Reid & Sanders, Operations Management © Wiley 2002 Page 8 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

Reid & Sanders, Operations Management © Wiley 2002 Page 9 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

Reid & Sanders, Operations Management © Wiley 2002 Page 10 Patterns in Time Series Data

Reid & Sanders, Operations Management © Wiley 2002 Page 11 Logic of Time Series Models Data = historic pattern + random variation Historic pattern may include: –Level (long-term average) –Trend –Seasonality –Cycle

Reid & Sanders, Operations Management © Wiley 2002 Page 12 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

Reid & Sanders, Operations Management © Wiley 2002 Page 13 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

Reid & Sanders, Operations Management © Wiley 2002 Page 14 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

Reid & Sanders, Operations Management © Wiley 2002 Page 15 Forecasting Trends Trend-adjusted exponential smoothing Three step process: –Smooth the level of the series: –Smooth the trend: –Calculate the forecast including trend:

Reid & Sanders, Operations Management © Wiley 2002 Page 16 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,...

Reid & Sanders, Operations Management © Wiley 2002 Page 17 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

Reid & Sanders, Operations Management © Wiley 2002 Page 18 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:

Reid & Sanders, Operations Management © Wiley 2002 Page 19 Linear Regression

Reid & Sanders, Operations Management © Wiley 2002 Page 20 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

Reid & Sanders, Operations Management © Wiley 2002 Page 21 Tracking Forecast Error Over 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)

Reid & Sanders, Operations Management © Wiley 2002 Page 22 Factors for Selecting a Forecasting Model The amount & type of available data Degree of accuracy required Length of forecast horizon Presence of data patterns

Reid & Sanders, Operations Management © Wiley 2002 Page 23 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.