1. Chapter 9 Forecasting

2. 1. Define Forecast.

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. 2. Define Planning.

5. 10-5  Planning - Determining what is needed, and making arrangements to get it, in order to achieve objectives. PLANNING

6. 3. What are the financial benefits of effective planning?

7. 10-7  Increasing Alternatives  Management has more options if it plans ahead.  Profitability Enhancement  Planning can both reduce costs and increase sales.  Uncertain future The further ahead we plan, however, the less we know about future conditions. There is a tradeoff between increasing alternatives and increasing uncertainty. Financial Benefits of Effective Planning

8. 10-8 Planning Horizon The distance into the future one plans. Looking into the Future: The Planning Horizon

9. 4. What is a contingency plan?

10. 10-10  Contingency Plans – Alternative or back-up plans to be used if an unexpected event makes the normal plans infeasible. Contingency Plan

11. 5. What are three common types of forecasts?

12. 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

13. 6. What are the four laws of forecasting?

14. 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.

15. 7. What are the two types of forecasting models?

16. Forecasting Models  Qualitative forecasting techniques – Forecasting techniques based on intuition or informed opinion.  Used when data are scarce, not available, or irrelevant. Do not use past data. Usually used when such data is not available (such as planning for a new product).  Customer surveys, expert opinions, etc

17. Qualitative Forecasting Models  Market surveys  Build-up forecasts  Life-cycle analogy method  Panel consensus forecasting  Delphi method

18. Forecasting Models  Quantitative forecasting models – Forecasting models that use measurable, historical data to generate forecasts.  Time series and causal models

19. 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.

20. Selecting a Forecasting Method Figure 9.2

21. 7. What are components of a time series?

22. 10-22  There are four potential components of a time series:  Cycles A pattern that repeats over a long period of time (such as 20 years). Cycles are less important for demand forecasting, since we rarely have 20 years’ worth of data.  Trend  Seasonality  Randomness Components of a Time Series

23. 10-23 Components of a Time Series Trend – Component of a time series that causes demand to increase or decrease. Exhibit 10.6 Example of a Time Series with Trend

24. 10-24 Seasonality – A pattern in a time series that repeats itself at least once a year. Exhibit 10.7 Time Series with Seasonality Components of a Time Series

25. Time series with Trend and Seasonality Figure 9.4

26. 10-26 Random Fluctuation – Unpredictable variation in demand that is not due to trend, seasonality, or cycle. Exhibit 10.8 Time Series with Random Fluctuation Components of a Time Series

27. Time series with randomness Figure 9.3

28. 9. What are the various time series techniques?

29. 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

30. Last Period Model Table 9.3Figure 9.5

31. Moving Average Model  Moving Average Model – A time series forecasting model that derives a forecast by taking an average of recent demand value.

32. 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

33. Example 9.1 – Flavio’s Pizza  Flavio’s Pizza has recorded the following demand history for each Friday night for the past five weeks.  Develop forecasts for week 6 using a two-period moving average and a 3-period weighted moving average using the following demands and weights (0.4, 0.35, and 0.25, starting with the most recent observation. The three-period weighted moving average forecast would be: The two-period moving average forecast would be: Copyright © 2016 Pearson Education, Inc. 9-33

34. 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.

35. 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

36. 10. What is the formula for simple exponential smoothing using a sophisticated weighted moving average?

37. 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.

38. Exponential Smoothing Model a =.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

39. Causal Forecasting Models  Linear Regression  Multiple Regression  Examples:

40. Causal - Linear Regression 

41. Linear Regression - Problem  Trevecca Nazarene University’s new campus bookstore tries to predict the number of books it should stock based on the number of classes requiring the book (this is especially important during the two weeks of advising). A regression analysis provided the following equation for the Financial Stewardship textbook used by Prof. Philip:  Y = 11.5441 + 19.6324x  For the Fall 2013 semester, five classes will require the book as reading. What is forecast for the demand?

42. Example 9.6 – Lance’s BBQ  Lance’s BBQ Catering Service is a favorite of sports teams in the Raleigh, North Carolina area.  By counting and weighing the guests arriving at a party, they captured the following data: Copyright © 2016 Pearson Education, Inc. 9-42

43. Example 9.6 – Lance’s BBQ  Lance has a party coming up and is expecting 60 guests each with an average weight of around 240 lbs.  Use multiple regression to estimate how much barbecue these guests will eat based on the number of guests and average weight. Copyright © 2016 Pearson Education, Inc. 9-43

44. Example 9.6 – Lance’s BBQ Copyright © 2016 Pearson Education, Inc. 9-44 Intercept term = 12.52 Slope coefficient for number of guests = 0.15 Slope coefficient for average weight = 0.15 Barbecue eaten (lbs.) = 12.52 + 0.15 (no. of guests) + 0.15 (average weight) If expecting 60 guests with average weight of 240 lbs. Barbecue eaten (lbs.) = 12.52 + 0.15 (60) + 0.15 (240) = 57.52 lbs. of barbecue

45. 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

46. 11. What are the two kinds of forecasting errors?

47. 10-47 Forecast Accuracy  Forecast error is the actual demand minus the forecast demand.  Absolute Error: how far “off” are we, in absolute terms?  Measured by mean absolute deviation (MAD) or mean squared error (MSE)  Forecast Bias: Are we consistently high or low?  A forecast should be unbiased (low forecasts are as frequent as high forecasts)  Bias is measured by mean forecast error (MFE) or running sum of forecast error (RSFE)

48. 10-48  The ideal value for both is zero, which would mean there is no forecasting error  The larger the MAD or MSE, the less the accurate the model Forecast Accuracy Two similar approaches are used to measure absolute forecast error MAD is the mean of the absolute values of the forecast errors MSE is the mean of the squared values of the forecast errors

49. Calculating MAD

50. Calculating MSE

51. 10-51 Forecast Bias  Forecast Bias: Tendency of a forecast to be too high or too low.  Mean forecast error (MFE)  The mean of the forecast errors  Running sum of forecast errors (RSFE)  The sum of forecast error, updated as each new error is calculated.  Ideal measure is zero which indicates no bias.  Positive means forecast tends to low  Negative means forecast tends to high

52. Forecast Bias: Calculating MFE and RSFE Mean Forecast Error = 1.00 RSFE (period 8) = 8

53. Example 9.7 – Wolf State University Table 9.11 Demand and Forecast Results for Walk-In Advising at Wolf State University Copyright © 2016 Pearson Education, Inc. 9-53

54. Example 9.7 – Wolf State University Copyright © 2016 Pearson Education, Inc. 9-54

55. Example 9.7 – Wolf State University  Model 2 has the MFE value closer to 0, it appears to be the least biased.  On average, Model 2 overforecasted by 0.20 walk-ins, while Model 1 overforecasted by 0.70.  Model 2 has the lower MAD and MAPE values.  Model 2 is the superior model. Copyright © 2016 Pearson Education, Inc. 9-55

56. Forecasting Case Study Top-Slice Drivers Copyright © 2016 Pearson Education, Inc. 9-56

57. 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 © 2016 Pearson Education, Inc. 9-57