1 / 49

Forecasting

Forecasting. FORECAST : A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design. Uses of Forecasts.

jerrysantos
Télécharger la présentation

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. Forecasting

  2. FORECAST: • A statement about the future value of a variable of interest such as demand. • Forecasts affect decisions and activities throughout an organization • Accounting, finance • Human resources • Marketing • MIS • Operations • Product / service design

  3. Uses of Forecasts

  4. I see that you willget an A this semester. • Assumes causal systempast ==> future • Forecasts rarely perfect because of randomness • Forecasts more accurate forgroups vs. individuals • Forecast accuracy decreases as time horizon increases

  5. Timely Accurate Reliable Easy to use Written Meaningful Elements of a Good Forecast

  6. “The forecast” Step 6 Monitor the forecast Step 5 Prepare the forecast Step 4 Gather and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast Steps in the Forecasting Process

  7. Types of Forecasts • Judgmental - uses subjective inputs • Time series - uses historical data assuming the future will be like the past • Associative models - uses explanatory variables to predict the future

  8. Judgmental Forecasts • Executive opinions • Sales force opinions • Consumer surveys • Outside opinion • Delphi method • Opinions of managers and staff • Achieves a consensus forecast

  9. Time Series Forecasts • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Cycle – wavelike variations of more than one year’s duration • Irregular variations - caused by unusual circumstances • Random variations - caused by chance

  10. Forecast Variations Figure 3.1 Irregularvariation Trend Cycles 90 89 88 Seasonal variations

  11. Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... Naive Forecasts The forecast for any period equals the previous period’s actual value.

  12. Naïve Forecasts • Simple to use • Virtually no cost • Quick and easy to prepare • Data analysis is nonexistent • Easily understandable • Cannot provide high accuracy • Can be a standard for accuracy

  13. Techniques for Averaging • Moving average • Weighted moving average • Exponential smoothing

  14. n Ai  MAn = i = 1 n Moving Averages • Moving average – A technique that averages a number of recent actual values, updated as new values become available. • Weighted moving average – More recent values in a series are given more weight in computing the forecast.

  15. n Ai  MAn = i = 1 n Moving Averages • Moving average –

  16. n Ai  MAn = i = 1 n Moving Averages • Moving average –

  17. n Ai  MAn = i = 1 n Moving Averages • Moving average –

  18. n Ai  MAn = i = 1 n Moving Averages • Moving average –

  19. n Ai  MAn = i = 1 n Moving Averages • Moving average –

  20. n Ai  MAn = i = 1 n Simple Moving Average Actual MA5 MA3

  21. n A*wi  WMAn = i = 1 Weighted Moving Averages • Weighted Moving average –

  22. n A*wi  WMAn = i = 1 Weighted Moving Averages • Weighted Moving average –

  23. n A*wi  WMAn = i = 1 Weighted Moving Averages • Weighted Moving average –

  24. n A*wi  WMAn = i = 1 Weighted Moving Averages • Weighted Moving average –

  25. n A*wi  WMAn = i = 1 Weighted Moving Averages • Weighted Moving average –

  26. Exponential Smoothing • Premise--The most recent observations might have the highest predictive value. • Therefore, we should give more weight to the more recent time periods when forecasting. Ft = Ft-1 + (At-1 - Ft-1)

  27. Exponential Smoothing • Weighted averaging method based on previous forecast plus a percentage of the forecast error • A-F is the error term,  is the % feedback Ft = Ft-1 + (At-1 - Ft-1)

  28. Exponential Smoothing • Ft = Next Period • Ft-1= Previous Period •  = Smoothing Constant • At-1 = Actual Result Previous Period Ft = Ft-1 + (At-1 - Ft-1)

  29. Exponential Smoothing - Problem • Ft = Result of formula • Ft-1= 42 •  = Smoothing = .10 • At-1 = 44 Ft = Ft-1 + (At-1 - Ft-1)

  30. Exponential Smoothing - Problem • Ft = 42 + .10(44-42) • Ft = 42 + .10(2) • Ft = 42 + .20 • Ft = 42.20 Ft = Ft-1 + (At-1 - Ft-1)

  31. Exponential Smoothing - Problem • Ft = 43 + .10(42.20-43) • Ft = 43 + .10(-.80) • Ft = 43 + -.080 • Ft = 42.92 Ft = Ft-1 + (At-1 - Ft-1)

  32. Example 3 - Exponential Smoothing

  33. Actual .4  .1 Picking a Smoothing Constant

  34. Parabolic Exponential Growth Common Nonlinear Trends Figure 3.5

  35. Ft Ft = a + bt 0 1 2 3 4 5 t Linear Trend Equation • Ft = Forecast for period t • t = Specified number of time periods • a = Value of Ft at t = 0 • b = Slope of the line

  36. n (ty) - t y    b = 2 2 n t - ( t)   y - b t   a = n Calculating a and b

  37. Linear Trend Equation Example

  38. 5 (2499) - 15(812) 12495 - 12180 b = = = 6.3 5(55) - 225 275 - 225 812 - 6.3(15) a = = 143.5 5 y = 143.5 + 6.3t Linear Trend Calculation

  39. Associative Forecasting • Predictor variables - used to predict values of variable interest • Regression - technique for fitting a line to a set of points • Least squares line - minimizes sum of squared deviations around the line

  40. Computedrelationship Linear Model Seems Reasonable A straight line is fitted to a set of sample points.

  41. Forecast Accuracy • Error - difference between actual value and predicted value • Mean Absolute Deviation (MAD) • Average absolute error • Mean Squared Error (MSE) • Average of squared error • Mean Absolute Percent Error (MAPE) • Average absolute percent error

  42. 2 ( Actual  forecast)  MSE = n - 1  ( Actual forecast / Actual*100) MAPE = n MAD, MSE, and MAPE   Actual forecast MAD = n

  43. Example 10

  44. Controlling the Forecast • Control chart • A visual tool for monitoring forecast errors • Used to detect non-randomness in errors • Forecasting errors are in control if • All errors are within the control limits • No patterns, such as trends or cycles, are present

  45. Sources of Forecast errors • Model may be inadequate • Irregular variations • Incorrect use of forecasting technique

  46. Choosing a Forecasting Technique • No single technique works in every situation • Two most important factors • Cost • Accuracy • Other factors include the availability of: • Historical data • Computers • Time needed to gather and analyze the data • Forecast horizon

  47. Exponential Smoothing

  48. Linear Trend Equation

  49. Simple Linear Regression

More Related