Forecasting Chapter 9 9 Forecasting Forecast An estimate
- Slides: 35
Forecasting Chapter 9
9 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
9 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
9 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
9 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
9 Selecting a Forecasting Method Figure 9. 2 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 -7
9 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
9 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
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 9 - 10
9 Time series with randomness Figure 9. 3 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 11
Time series with Trend and Seasonality 9 Figure 9. 4 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 12
9 Last Period Model § Last Period Model - The simplest time series model that uses demand for the current period as a forecast for the next period. Ft+1 = Dt where Ft+1= forecast for the next period, t+1 and Dt = demand for the current period, t Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 13
9 Table 9. 3 Last Period Model Figure 9. 5 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 14
9 Moving Average Model § Moving Average Model – A time series forecasting model that derives a forecast by taking an average of recent demand value. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 15
9 Moving Average Model Period 1 2 3 4 5 6 7 8 Demand 12 15 11 9 10 8 14 12 3 -period moving average forecast for Period 8: = = (14 + 8 + 10) / 3 10. 67 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 16
9 Weighted Moving Average Model § Weighted Moving Average Model – A form of the moving average model that allows the actual weights applied to past observations to differ. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 17
9 Weighted Moving Average Model Period 1 2 3 4 5 6 7 8 Demand 12 15 11 9 10 8 14 12 3 -period weighted moving average forecast for Period 8= [(0. 5 14) + (0. 3 8) + (0. 2 10)] / 1 = 11. 4 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 18
9 Exponential Smoothing Model § Exponential Smoothing Model – A form of the moving average model in which the forecast for the next period is calculated as the weighted average of the current period’s actual value and forecast. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 19
Exponential Smoothing Model a =. 3 9 Period Demand 1 50 Forecast 40 2 46 . 3 * 50 + (1 -. 3) * 40 = 43 3 52 . 3 * 46 + (1 -. 3) * 43 = 43. 9 4 48 . 3 * 52 + (1 -. 3) * 43. 9 = 46. 33 5 47 . 3 * 48 + (1 -. 3) * 46. 33 = 46. 83 6 . 3 * 47 + (1 -. 3) * 46. 83 = 46. 88 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 20
9 Linear Regression § Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 22
9 Linear Regression § How to calculate the a and b Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 23
9 Linear Regression – Example 9. 3 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 24
9 Linear Regression – Example 9. 3 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 25
9 Linear Regression – Example 9. 3 Figure 9. 12 The graph shows an upward trend of 7. 33 sales per month (increase in sales by 7. 33 unit per month). Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 26
9 Causal Forecasting Models § Linear Regression § Multiple Regression § Examples: Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 33
9 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 9 - 34
9 Forecast Accuracy How do 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 9 - 35
9 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 9 - 36
9 Measures of Forecast Accuracy § Mean absolute percentage error (MAPE) = § Tracking Signal = Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 37
9 Forecast Accuracy – Example 9. 7 Table 9. 11 Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 38
9 Forecast Accuracy – Example 9. 7 § Calculate the forecast error for each week, the absolute deviation of the forecast error (MAD), and absolute percent errors (MAPE). Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 39
9 Forecast Accuracy – Example 9. 7 MFE MAD MAPE MFE Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall MAD MAPE 9 - 40
9 Forecast Accuracy – Example 9. 7 § Model 2 has the lowest MFE so it is the least biased. § Model 2 also has the lowest MAD and MAPE values so it appears to be superior. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 - 41
Forecasting Case Study Top-Slice Drivers Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 9 -
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 9 - 44
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