1. Forecasting Chapter 9

2. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 2 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.

3. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 3 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

4. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 4 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

5. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 5 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.

6. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 6 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

7. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 7 Selecting a Forecasting Method Figure 9.2

8. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 8 Qualitative Forecasting Methods  Market surveys  Build-up forecasts  Life-cycle analogy method  Panel consensus forecasting  Delphi method

9. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 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.

10. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 10 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.

11. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 11 Time series with randomness Figure 9.3

12. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 12 Time series with Trend and Seasonality Figure 9.4

13. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 13 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. F t+1 = D t where F t+1 = forecast for the next period, t+1 and D t = demand for the current period, t

14. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 14 Last Period Model Table 9.3Figure 9.5

15. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 15 Moving Average Model  Moving Average Model – A time series forecasting model that derives a forecast by taking an average of recent demand value.

16. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 16 Moving Average Model 3-period moving average forecast for Period 8: =(14 + 8 + 10) / 3 =10.67 PeriodDemand 112 215 311 49 510 68 714 812

17. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 17 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.

18. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 18 Weighted Moving Average Model 3-period weighted moving average forecast for Period 8= [(0.5  14) + (0.3  8) + (0.2  10)] / 1 = 11.4 PeriodDemand 112 215 311 49 510 68 714 812

19. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 19 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.

20. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 20 Exponential Smoothing Model  =.3 PeriodDemandForecast 15040 246.3 * 50 + (1-.3) * 40 = 43 352.3 * 46 + (1-.3) * 43 = 43.9 448.3 * 52 + (1-.3) * 43.9 = 46.33 547.3 * 48 + (1-.3) * 46.33 = 46.83 6.3 * 47 + (1-.3) * 46.83 = 46.88

21. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 21 Adjusted Exponential Smoothing  Adjusted Exponential Smoothing Model – An expanded version of the exponential smoothing model that includes a trend adjustment factor. AF t+1 = F t+1 +T t+1 where AF t+1 = adjusted forecast for the next period F t+1 = unadjusted forecast for the next period =  D t + (1 –  ) F t T t+1 = trend factor for the next period =  (F t+1 – F t ) + (1 –  )T t T t = trend factor for the current period  smoothing constant for the trend adjustment factor

22. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 22 Linear Regression 

23. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 23 Linear Regression  How to calculate the a and b

24. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 24 Linear Regression – Example 9.3

25. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 25 Linear Regression – Example 9.3

26. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 26 Linear Regression – Example 9.3 The graph shows an upward trend of 7.33 sales per month. Figure 9.12

27. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 27 Seasonal Adjustments  Seasonality – Repeated patterns or drops in a time series associated with certain times of the year. Table 9.8

28. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 28 Seasonal Adjustments  Four-step procedure:  For each of the demand values in the time series, calculate the corresponding forecast using the unadjusted forecast model.  For each demand value, calculate (Demand/Forecast). If the ratio is less than 1, then the forecast model overforecasted; if it is greater than 1, then the model underforecasted.  If the time series covers multiple years, take the average (Demand/Forecast) for corresponding months or quarters to derive the seasonal index. Otherwise use (Demand/Forecast) calculated in Step 2 as the seasonal index.  Multiply the unadjusted forecast by the seasonal index to get the seasonally adjusted forecast value.

29. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 29 Seasonality – Example 9.4 Note that the regression forecast does not reflect the seasonality. Figure 9.15

30. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 30 Seasonality – Example 9.4

31. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 31 Seasonality – Example 9.4 Calculate the (Demand/Forecast) for each of the time periods: January 2012: (Demand/Forecast) = 51/106.9 =.477 January 2013: (Demand/Forecast) = 112/205.6 =.545 Calculate the monthly seasonal indices: Monthly seasonal index, January = (.477 +.545)/2 =.511 Calculate the seasonally adjusted forecasts Seasonally adjusted forecast = unadjusted forecast x seasonal index January 2012: 106.9 x.511 = 54.63 January 2013: 205.6 x.511 = 105.06

32. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 32 Seasonality – Example 9.4 Note that the regression forecast now does reflect the seasonality. Figure 9.16

33. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 33 Causal Forecasting Models  Linear Regression  Multiple Regression  Examples:

34. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 34 Multiple Regression  Multiple Regression – A generalized form of linear regression that allows for more than one independent variable.

35. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 35 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

36. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 36 Measures of Forecast Accuracy  Forecast error for period (i) =  Mean forecast error (MFE) =  Mean absolute deviation (MAD) =

37. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 37 Measures of Forecast Accuracy  Mean absolute percentage error (MAPE) =  Tracking Signal =

38. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 38 Forecast Accuracy – Example 9.7 Table 9.11

39. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 39 Forecast Accuracy – Example 9.7  Calculate the forecast error for each week, the absolute deviation of the forecast error, and absolute percent errors.

40. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 40 Forecast Accuracy – Example 9.7

41. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 41 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.  Calculate the tracking signal for the first 10 weeks.

42. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 42 Forecast Accuracy – Example 9.7

43. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 43 Forecast Accuracy – Example 9.7  The tracking signal for Model 2 gets very low in week 5, however the model recovers.  You need to continue to update the tracking signal in the future.

44. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 44 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.

45. 9 - 45Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Forecasting Case Study Top-Slice Drivers

46. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 46 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.