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Chapter 3

Chapter 3. Forecasting in POM: The Starting Point for All Planning. Overview. Introduction Qualitative Forecasting Methods Quantitative Forecasting Models How to Have a Successful Forecasting System Computer Software for Forecasting Forecasting in Small Businesses and Start-Up Ventures

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Chapter 3

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  1. Chapter 3 Forecasting in POM: The Starting Point for All Planning

  2. Overview • Introduction • Qualitative Forecasting Methods • Quantitative Forecasting Models • How to Have a Successful Forecasting System • Computer Software for Forecasting • Forecasting in Small Businesses and Start-Up Ventures • Wrap-Up: What World-Class Producers Do

  3. Demand Management • Independent demand items are the only items demand for which needs to be forecast • These items include: • Finished goods and • Spare parts

  4. Independent Demand(finished goods and spare parts) Dependent Demand(components) A C(2) B(4) D(2) E(1) D(3) F(2) Demand Management

  5. Introduction • Demand estimates for independent demand products and services are the starting point for all the other forecasts in POM. • Management teams develop sales forecasts based in part on demand estimates. • Sales forecasts become inputs to both business strategy and production resource forecasts.

  6. Forecasting is an Integral Part of Business Planning Inputs: Market, Economic, Other Demand Estimates Forecast Method(s) Sales Forecast Management Team Business Strategy Production Resource Forecasts

  7. Examples of Production Resource Forecasts

  8. Forecasting Methods • Qualitative Approaches • Quantitative Approaches

  9. Qualitative Forecasting ApplicationsSmall and Large Firms Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100. Note: More than one response was permitted.

  10. Qualitative Approaches • Usually based on judgments about causal factors that underlie the demand of particular products or services • Do not require a demand history for the product or service, therefore are useful for new products/services • Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events

  11. Qualitative Methods • Executive committee consensus • Delphi method • Survey of sales force • Survey of customers • Historical analogy • Market research

  12. Quantitative Forecasting Approaches • Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself • Analysis of the past demand pattern provides a good basis for forecasting future demand • Majority of quantitative approaches fall in the category of time series analysis

  13. Quantitative Forecasting ApplicationsSmall and Large Firms Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100. Note: More than one response was permitted.

  14. Time Series Analysis • A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand • Analysis of the time series identifies patterns • Once the patterns are identified, they can be used to develop a forecast

  15. x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Components of Time Series What’s going on here? x Sales 1 2 3 4 Year

  16. Components of Time Series • Trends are noted by an upward or downward sloping line • Seasonality is a data pattern that repeats itself over the period of one year or less • Cycle is a data pattern that repeats itself... may take years • Irregular variations are jumps in the level of the series due to extraordinary events • Random fluctuation from random variation or unexplained causes

  17. Actual Data & the Regression Line

  18. Seasonality Length of TimeNumber of Before PatternLength ofSeasons Is RepeatedSeasonin Pattern Year Quarter 4 Year Month 12 Year Week 52 Month Week 4 Month Day 28-31 Week Day 7

  19. Eight Steps to Forecasting • Determining the use of the forecast--what are the objectives? • Select the items to be forecast • Determine the time horizon of the forecast • Select the forecasting model(s) • Collect the data • Validate the forecasting model • Make the forecast • Implement the results

  20. Quantitative Forecasting Approaches • Linear Regression • Simple Moving Average • Weighted Moving Average • Exponential Smoothing (exponentially weighted moving average) • Exponential Smoothing with Trend (double smoothing)

  21. Simple Linear Regression • Relationship between one independent variable, X, and a dependent variable, Y. • Assumed to be linear (a straight line) • Form: Y = a + bX • Y = dependent variable • X = independent variable • a = y-axis intercept • b = slope of regression line

  22. Simple Linear Regression Model • b is similar to the slope. However, since it is calculated with the variability of the data in mind, its formulation is not as straight-forward as our usual notion of slope Yt = a + bx Y 0 1 2 3 4 5 x (weeks)

  23. Calculating a and b

  24. Regression Equation Example Develop a regression equation to predict sales based on these five points.

  25. Regression Equation Example Slide 24 of 55

  26. Regression Equation Example y = 143.5 + 6.3t 180 175 170 165 Sales 160 155 Forecast Sales 150 145 140 135 Period 1 2 3 4 5 Slide 25 of 55

  27. Forecast Accuracy • Accuracy is the typical criterion for judging the performance of a forecasting approach • Accuracy is how well the forecasted values match the actual values

  28. Monitoring Accuracy • Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach • Accuracy can be measured in several ways • Mean absolute deviation (MAD) • Mean squared error (MSE)

  29. Mean Absolute Deviation (MAD)

  30. Mean Squared Error (MSE) MSE = (Syx)2 Small value for Syx means data points tightly grouped around the line and error range is small. The smaller the standard error the more accurate the forecast. MSE = 1.25(MAD) When the forecast errors are normally distributed

  31. Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300 320 5 325 315 Example--MAD Determine the MAD for the four forecast periods

  32. Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210 205 5 4 300 320 20 5 325 315 10 40 Solution

  33. Simple Moving Average • An averaging period (AP) is given or selected • The forecast for the next period is the arithmetic average of the AP most recent actual demands • It is called a “simple” average because each period used to compute the average is equally weighted • . . . more

  34. Simple Moving Average • It is called “moving” because as new demand data becomes available, the oldest data is not used • By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response) • By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response)

  35. Simple Moving Average • Let’s develop 3-week and 6-week moving average forecasts for demand. • Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

  36. Simple Moving Average Slide 35 of 55

  37. Simple Moving Average Slide 36 of 55

  38. Weighted Moving Average • This is a variation on the simple moving average where instead of the weights used to compute the average being equal, they are not equal • This allows more recent demand data to have a greater effect on the moving average, therefore the forecast • . . . more

  39. Weighted Moving Average • The weights must add to 1.0 and generally decrease in value with the age of the data • The distribution of the weights determine impulse response of the forecast

  40. Weighted Moving Average Determine the 3-period weighted moving average forecast for period 4 Weights (adding up to 1.0): t-1: .5 t-2: .3 t-3: .2

  41. Solution

  42. Exponential Smoothing • The weights used to compute the forecast (moving average) are exponentially distributed • The forecast is the sum of the old forecast and a portion of the forecast error Ft = Ft-1 + a(At-1-Ft-1) • . . . more

  43. Exponential Smoothing • The smoothing constant, , must be between 0.0 and 1.0 (excluding 0.0 and 1.0) • A large  provides a high impulse response forecast • A small  provides a low impulse response forecast

  44. Exponential Smoothing Example • Determine exponential smoothing forecasts for periods 2 through 10 using =.10 and =.60. • Let F1=D1

  45. Exponential Smoothing Example Slide 44 of 55

  46. Effect of  on Forecast

  47. Criteria for Selectinga Forecasting Method • Cost • Accuracy • Data available • Time span • Nature of products and services • Impulse response and noise dampening

  48. Reasons for Ineffective Forecasting • Not involving a broad cross section of people • Not recognizing that forecasting is integral to business planning • Not recognizing that forecasts will always be wrong (think in terms of interval rather than point forecasts) • Not forecasting the right things (forecast independent demand only) • Not selecting an appropriate forecasting method (use MAD to evaluate goodness of fit) • Not tracking the accuracy of the forecasting models

  49. How to Monitor andControl a Forecasting Model • Tracking Signal Tracking signal = =

  50. Tracking Signal: What do you notice?

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