Forecasting Chapter Chapter Objectives Be able to: Discuss the importance of forecasting and identify the most appropriate type of forecasting approach, given different forecasting situations Apply a variety of time series forecasting models, including moving average, exponential smoothing, and linear regression models Develop causal forecasting models using linear regression and multiple regression Calculate measures of forecasting accuracy and interpret the results Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 9-2 Forecasting  Forecast – An estimate of the future level of some variable  Why Forecast?  Assess long-term capacity needs  Develop budgets, hiring plans, etc  Plan production or order materials Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 9-3 Types of Forecasts  Demand  Firm-level  Market-level  Supply  Number of current producers and suppliers  Projected aggregate supply levels  Technological and political trends  Price  Cost of supplies and services  Market price for firm’s product or service Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 9-4 Laws of Forecasting  Forecasts are almost always wrong by some amount (but they are still useful)  Forecasts for the near term tend to be more accurate  Forecasts for groups of products or services tend to be more accurate  Forecasts are no substitute for calculated values Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 9-5 Forecasting Methods  Qualitative forecasting techniques – Forecasting techniques based on intuition or informed opinion  Used when data are scarce, not available, or irrelevant  Quantitative forecasting models – Forecasting models that use measurable, historical data to generate forecasts  Time series and causal models Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 9-6 Selecting a Forecasting Method Figure 9.2 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 9-7 Qualitative Forecasting Methods  Market surveys  Build-up forecasts  Life-cycle analogy method  Panel consensus forecasting  Delphi method Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 9-8 Quantitative Forecasting Methods  Time series forecasting models – Models that use a series of observations in chronological order to develop forecasts  Causal forecasting models – Models in which forecasts are modeled as a function of something other than time Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 9-9 Demand movement  Randomness – Unpredictable movement from one time period to the next  Trend – Long-term movement up or down in a time series  Seasonality – A repeated pattern of spikes or drops in a time series associated with certain times of the year Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 10 Seasonality – Example 9.4 Note that the regression forecast now does reflect the seasonality Figure 9.16 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 32 Causal Forecasting Models  Linear Regression  Multiple Regression  Examples: Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 33 Multiple Regression  Multiple Regression – A generalized form of linear regression that allows for more than one independent variable Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 34 Forecast Accuracy How we know:  If a forecast model is “best”?  If a forecast model is still working?  What types of errors a particular forecasting model is prone to make? Need measures of forecast accuracy Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 35 Measures of Forecast Accuracy  Forecast error for period (i) =  Mean forecast error (MFE) =  Mean absolute deviation (MAD) = Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 36 Measures of Forecast Accuracy  Mean absolute percentage error (MAPE) =  Tracking Signal = Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 37 Forecast Accuracy – Example 9.7 Table 9.11 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 38 Forecast Accuracy – Example 9.7  Calculate the forecast error for each week, the absolute deviation of the forecast error, and absolute percent errors Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 39 Forecast Accuracy – Example 9.7 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 40 Forecast Accuracy – Example 9.7  Model has the lowest MFE so it is the least biased  Model also has the lowest MAD and MAPE values so it appears to be superior  Calculate the tracking signal for the first 10 weeks Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 41 Forecast Accuracy – Example 9.7 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 42 Forecast Accuracy – Example 9.7  The tracking signal for Model gets very low in week 5, however the model recovers  You need to continue to update the tracking signal in the future Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 43 Collaborative Planning, Forecasting, and Replenishment (CPFR)  CPFR – A set of business processes, backed up by information technology, in which members agree to mutual business objectives and measures, develop joint sales and operational plans, and collaborate electronically to generate and update sales forecasts and replenishment plans Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 44 Forecasting Case Study Top-Slice Drivers Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 45 All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher Printed in the United States of America Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 46 ... Demand  Firm-level  Market-level  Supply  Number of current producers and suppliers  Projected aggregate supply levels  Technological and political trends  Price  Cost of supplies and. .. exponential smoothing, and linear regression models Develop causal forecasting models using linear regression and multiple regression Calculate measures of forecasting accuracy and interpret the.. .Chapter Objectives Be able to: Discuss the importance of forecasting and identify the most appropriate type of forecasting approach,