1 / 83

Chapter 5 Forecasting

Introduction to Management Science 8th Edition by Bernard W. Taylor III. Chapter 5 Forecasting. Chapter Topics. Forecasting Components Time Series Methods Forecast Accuracy Time Series Forecasting Using Excel Time Series Forecasting Using QM for Windows Regression Methods.

dareh
Download Presentation

Chapter 5 Forecasting

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction to Management Science 8th Edition by Bernard W. Taylor III Chapter 5 Forecasting Chapter 5 - Forecasting

  2. Chapter Topics • Forecasting Components • Time Series Methods • Forecast Accuracy • Time Series Forecasting Using Excel • Time Series Forecasting Using QM for Windows • Regression Methods Chapter 5 - Forecasting

  3. Forecasting Components • A variety of forecasting methods are available for use depending on the time frame of the forecast and the existence of patterns. • Time Frames: • Short-range (one to two months) • Medium-range (two months to one or two years) • Long-range (more than one or two years) • Patterns: • Trend • Random variations • Cycles • Seasonal pattern Chapter 5 - Forecasting

  4. Forecasting Components Patterns (1 of 2) • Trend - A long-term movement of the item being forecast. • Random variations - movements that are not predictable and follow no pattern. • Cycle - A movement, up or down, that repeats itself over a lengthy time span. • Seasonal pattern - Oscillating movement in demand that occurs periodically in the short run and is repetitive. Chapter 5 - Forecasting

  5. trend-line Forecasting Components Patterns (2 of 2) Figure 5.1 Forms of Forecast Movement: (a) Trend, (b) Cycle, (c) Seasonal Pattern, (d) Trend with Seasonal Pattern Chapter 5 - Forecasting

  6. Forecasting Components Forecasting Methods • Times Series - Statistical techniques that use historical data to predict future behavior. • Regression Methods - Regression (or causal ) methods that attempt to develop a mathematical relationship between the item being forecast and factors that cause it to behave the way it does. • Qualitative Methods - Methods using judgment, expertise and opinion to make forecasts. Chapter 5 - Forecasting

  7. Forecasting Components Qualitative Methods • Qualitative methods, the “jury of executive opinion,” is the most common type of forecasting method for long-term strategic planning. • Performed by individuals or groups within an organization, sometimes assisted by consultants and other experts, whose judgments and opinion are considered valid for the forecasting issue. • Usually includes specialty functions such as marketing, engineering, purchasing, etc. in which individuals have experience and knowledge of the forecasted item. • Supporting techniques include the Delphi Method, market research, surveys, etc. Chapter 5 - Forecasting

  8. Time Series Methods Overview • Statistical techniques that make use of historical data collected over a long period of time. • Methods assume that what has occurred in the past will continue to occur in the future. • Forecasts based on only one factor - time. Chapter 5 - Forecasting

  9. Time Series Methods Moving Average (1 of 5) • Moving average uses values from the recent past to develop forecasts. • This dampensor smoothesout random increases and decreases. • Useful for forecasting relatively stable items that do not display any trend or seasonal pattern. • Formula for: Chapter 5 - Forecasting

  10. Time Series Methods Moving Average (2 of 5) • Example: Instant Paper Clip Supply Company forecast of orders for the next month. • Three-month moving average: • Five-month moving average: Chapter 5 - Forecasting

  11. Time Series Methods Moving Average (3 of 5) Figure 5.2 Three- and Five-Month Moving Averages Chapter 5 - Forecasting

  12. Time Series Methods Moving Average (4 of 5) Figure 5.2 Three- and Five-Month Moving Averages Chapter 5 - Forecasting

  13. Time Series Methods Moving Average (5 of 5) • Longer-period moving averages react more slowly to changes in demand than do shorter-period moving averages. • The appropriate number of periods to use often requires trial-and-error experimentation. • Moving average does not react well to changes (trends, seasonal effects, etc.) but is easy to use and inexpensive. • Good for short-term forecasting. Chapter 5 - Forecasting

  14. Time Series Methods Weighted Moving Average (1 of 2) • In a weighted moving average, weights are assigned to the most recent data. • Formula: Chapter 5 - Forecasting

  15. Time Series Methods Weighted Moving Average (2 of 2) • Determining precise weights and number of periods requires trial-and-error experimentation. Chapter 5 - Forecasting

  16. Time Series Methods Exponential Smoothing (1 of 11) • Exponential smoothing weights recent past data more strongly than more distant data. • Two forms: simpleexponential smoothing and adjusted exponential smoothing. • Simple exponential smoothing: Ft + 1 = Dt + (1 - )Ft where: Ft + 1 = the forecast for the next period Dt = actual demand in the present period Ft = the previously determined forecast for the present period  = a weighting factor (smoothing constant) use F1 = D1. Chapter 5 - Forecasting

  17. Time Series Methods Exponential Smoothing (2 of 11) • The most commonly used values of  are between.10 and .50. • Determination of  is usually judgmental and subjective and often based on trial-and -error experimentation. Chapter 5 - Forecasting

  18. Time Series Methods Exponential Smoothing (3 of 11) Example: PM Computer Services (see Table 5.4). • Exponential smoothing forecasts using smoothing constant of .30. • Forecast for period 2 (February): F2 =  D1 + (1- )F1 = (.30)(37) + (.70)(37) = 37 units • Forecast for period 3 (March): F3 =  D2 + (1- )F2 = (.30)(40) + (.70)(37) = 37.9 units Chapter 5 - Forecasting

  19. Time Series Methods Exponential Smoothing (4 of 11) Using F1 = D1 Table 5.4 Exponential Smoothing Forecasts,  = .30 and  = .50 Chapter 5 - Forecasting

  20. Time Series Methods Exponential Smoothing (5 of 11) • The forecast that uses the higher smoothing constant (.50) reacts more strongly to changes in demand than does the forecast with the lower constant (.30). • Both forecasts lag behind actual demand. • Both forecasts tend to be consistently lower than actual demand. • Low smoothing constants are appropriate for stable data without trend; higher constants appropriate for data with trends. Chapter 5 - Forecasting

  21. Time Series Methods Exponential Smoothing (6 of 11) Figure 5.3 Exponential Smoothing Forecasts Chapter 5 - Forecasting

  22. Time Series Methods Exponential Smoothing (7 of 11) • Adjusted exponential smoothing: exponential smoothing with a trend adjustment factor added. • Formula: AFt + 1 = Ft + 1 + Tt+1 where: Tt = an exponentially smoothed trend factor: Tt + 1 = (Ft + 1 - Ft) + (1 - )Tt Tt = the last (previous) period’s trend factor  = smoothing constant for trend ( a value between zero and one). • Reflects the weight given to the most recent trend data. • Determined subjectively. Chapter 5 - Forecasting

  23. Time Series Methods Exponential Smoothing (8 of 11) Example: PM Computer Services exponential smoothed forecasts with  = .50 and  = .30 (see Table 5.5). • Start with T2 = 0.00 • Adjusted forecast for period 3: T3 = (F3 - F2) + (1 - )T2 = (.30)(38.5 - 37.0) + (.70)(0) = 0.45 AF3 = F3 + T3 = 38.5 + 0.45 = 38.95 Chapter 5 - Forecasting

  24. Time Series Methods Exponential Smoothing (9 of 11) Tt + 1 = (Ft + 1 - Ft) + (1 - )Tt Table 5.5 Adjusted Exponentially Smoothed Forecast Values Chapter 5 - Forecasting

  25. Time Series Methods Exponential Smoothing (10 of 11) • Adjusted forecast is consistently higher than the simple exponentially smoothed forecast. • It is more reflective of the generally increasing trend of the data. Chapter 5 - Forecasting

  26. Time Series Methods Exponential Smoothing (11 of 11) Figure 5.4 Adjusted Exponentially Smoothed Forecast Chapter 5 - Forecasting

  27. Time Series Methods Linear Trend Line (1 of 5) • When demand displays an obvious trend over time, a least squares regression line , orlinear trend line, can be used to forecast. • Formula: Chapter 5 - Forecasting

  28. Time Series Methods Linear Trend Line (2 of 5) Example: PM Computer Services (see Table 5.6) Chapter 5 - Forecasting

  29. Time Series Methods Linear Trend Line (3 of 5) Table 5.6 Least Squares Calculations Chapter 5 - Forecasting

  30. Time Series Methods Linear Trend Line (4 of 5) • A trend line does not adjust to a change in the trend as does the exponential smoothing method. • This limits its use to shorter time frames in which trend will not change. Chapter 5 - Forecasting

  31. Time Series Methods Linear Trend Line (5 of 5) Figure 5.5 Linear Trend Line Chapter 5 - Forecasting

  32. Time Series Methods Seasonal Adjustments (1 of 4) • A seasonal pattern is a repetitive up-and-down movement in demand. • Seasonal patterns can occur on a monthly, weekly, or daily basis. • A seasonally adjusted forecast can be developed by multiplying the normal forecast by a seasonal factor. • A seasonal factor can be determined by dividing the actual demand for each seasonal period by total annual demand: Si =Di/D Chapter 5 - Forecasting

  33. Time Series Methods Seasonal Adjustments (2 of 4) • Seasonal factors lie between zero and one and represent the portion of total annual demand assigned to each season. • Seasonal factors are multiplied by annual demand to provide adjusted forecasts for each period. Chapter 5 - Forecasting

  34. Time Series Methods Seasonal Adjustments (3 of 4) • Example: Wishbone Farms Table 5.7 Demand for Turkeys at Wishbone Farms S1 = D1/ D = 42.0/148.7 = 0.28 S2 = D2/ D = 29.5/148.7 = 0.20 S3 = D3/ D = 21.9/148.7 = 0.15 S4 = D4/ D = 55.3/148.7 = 0.37 Chapter 5 - Forecasting

  35. Time Series Methods Seasonal Adjustments (4 of 4) • Multiply forecasted demand for entire year by seasonal factors to determine quarterly demand. • Forecast for entire year (trend line for data in Table 5.7): y = 40.97 + 4.30x = 40.97 + 4.30(4) = 58.17 • Seasonally adjusted forecasts: SF1 = (S1)(F5) = (.28)(58.17) = 16.28 SF2 = (S2)(F5) = (.20)(58.17) = 11.63 SF3 = (S3)(F5) = (.15)(58.17) = 8.73 SF4 = (S4)(F5) = (.37)(58.17) = 21.53 Note the potential for confusion: the Si are “seasonal factors” (fractions), whereas the SFi are “seasonally adjusted forecasts” (commodity values)! Chapter 5 - Forecasting

  36. Forecast Accuracy Overview • Forecasts will always deviate from actual values. • Difference between forecasts and actual values referred to as forecast error. • Would like forecast error to be as small as possible. • If error is large, either technique being used is the wrong one, or parameters need adjusting. • Measures of forecast errors: • Mean Absolute deviation (MAD) • Mean absolute percentage deviation (MAPD) • Cumulative error (E-bar) • Average error, or bias (E) Chapter 5 - Forecasting

  37. Forecast Accuracy Mean Absolute Deviation (1 of 7) • MAD is the average absolute difference between the forecast and actual demand. • Most popular and simplest-to-use measures of forecast error. • Formula: Chapter 5 - Forecasting

  38. Forecast Accuracy Mean Absolute Deviation (2 of 7) Example: PM Computer Services (see Table 5.8). • Compare accuracies of different forecasts using MAD: Chapter 5 - Forecasting

  39. Forecast Accuracy Mean Absolute Deviation (3 of 7) Table 5.8 Computational Values for MAD Chapter 5 - Forecasting

  40. Forecast Accuracy Mean Absolute Deviation (4 of 7) • The lower the value of MAD relative to the magnitude of the data, the more accurate the forecast. • When viewed alone, MAD is difficult to assess. • Must be considered in light of magnitude of the data. Chapter 5 - Forecasting

  41. Forecast Accuracy Mean Absolute Deviation (5 of 7) • Can be used to compare accuracy of different forecasting techniques working on the same set of demand data (PM Computer Services): • Exponential smoothing ( = .50): MAD = 4.04 • Adjusted exponential smoothing ( = .50,  = .30): MAD = 3.81 • Linear trend line: MAD = 2.29 • Linear trend line has lowest MAD; increasing  from .30 to .50 improved smoothed forecast. Chapter 5 - Forecasting

  42. Forecast Accuracy Mean Absolute Deviation (6 of 7) • A variation on MAD is the mean absolute percent deviation(MAPD). • Measures absolute error as a percentage of demand rather than per period. • Eliminates problem of interpreting the measure of accuracy relative to the magnitude of the demand and forecast values. • Formula: Chapter 5 - Forecasting

  43. Forecast Accuracy Mean Absolute Deviation (7 of 7) MAPD for other three forecasts: Exponential smoothing ( = .50): MAPD = 8.5% Adjusted exponential smoothing ( = .50,  = .30): MAPD = 8.1% Linear trend: MAPD = 4.9% Chapter 5 - Forecasting

  44. Forecast Accuracy Cumulative Error (1 of 2) • Cumulative error is the sum of the forecast errors (E =et). • A relatively large positive value indicates forecast is biased low, a large negative value indicates forecast is biased high. • If preponderance of errors are positive, forecast is consistently low; and vice versa. • Cumulative error for trend line is always almost zero, and is therefore not a good measure for this method. • Cumulative error for PM Computer Services can be read directly from Table 5.8. • E =  et = 49.31 indicating forecasts are frequently below actual demand. Chapter 5 - Forecasting

  45. Forecast Accuracy Cumulative Error (2 of 2) • Cumulative error for other forecasts: Exponential smoothing ( = .50): E = 33.21 Adjusted exponential smoothing ( = .50,  =.30): E = 21.14 • Average error (bias) is the per period average of cumulative error. • Average error for exponential smoothing forecast: • A large positive value of average error indicates a forecast is biased low; a large negative error indicates it is biased high. Chapter 5 - Forecasting

  46. Forecast Accuracy Example Forecasts by Different Measures Table 5.9 Comparison of Forecasts for PM Computer Services • Results consistent for all forecasts: • Larger value of alpha is preferable. • Adjusted forecast is more accurate than exponential smoothing forecasts. • Linear trend is more accurate than all the others. Chapter 5 - Forecasting

  47. Time Series Forecasting Using Excel (1 of 4) Exhibit 5.1 Chapter 5 - Forecasting

  48. Time Series Forecasting Using Excel (2 of 4) Exhibit 5.2 Chapter 5 - Forecasting

  49. Time Series Forecasting Using Excel (3 of 4) Exhibit 5.3 Chapter 5 - Forecasting

  50. Time Series Forecasting Using Excel (4 of 4) Exhibit 5.4 Chapter 5 - Forecasting

More Related