1 / 33

FORCASTING

PART TWO. FORCASTING. Chapter Three Forecasting. Chapter 3. Forecasting. FORECAST: A statement about the future Used to help managers Plan the system Plan the use of the system. Uses of Forecasts. I see that you will get an A this semester. Assumes causal system past ==> future

tino
Download Presentation

FORCASTING

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. PART TWO FORCASTING • Chapter Three • Forecasting

  2. Chapter 3 Forecasting

  3. FORECAST: • A statement about the future • Used to help managers • Plan the system • Plan the use of the system

  4. Uses of Forecasts

  5. 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

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

  7. “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

  8. 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

  9. Judgmental Forecasts • Executive opinions • Sales force composite • Consumer surveys • Outside opinion • Opinions of managers and staff • Delphi method

  10. Time Series Forecasts • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Irregular variations - caused by unusual circumstances • Random variations - caused by chance

  11. Irregularvariation Trend Cycles 90 89 88 Seasonal variations Forecast Variations Figure 3-1

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

  13. Uses for Naïve Forecasts • Stable time series data • F(t) = A(t-1) • Seasonal variations • F(t) = A(t-n) • Data with trends • F(t) = A(t-1) + (A(t-1) – A(t-2))

  14. Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... Naive Forecasts

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

  16. Figure 3-4 Actual MA5 MA3 n Ai  MAn = i = 1 n Simple Moving Average

  17. 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)

  18. Example of Exponential Smoothing

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

  20. Parabolic Exponential Growth Common Nonlinear Trends Figure 3-5

  21. Y Yt = a + bt 0 1 2 3 4 5 t Linear Trend Equation • 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.

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

  23. Linear Trend Equation Example

  24. 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

  25. 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

  26. Computedrelationship Linear Model Seems Reasonable

  27. 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 • Tracking signal • Ratio of cumulative error and MAD

  28.  Actual forecast MAD = n 2 ( Actual  forecast)  MSE = n - 1 MAD & MSE

  29. (Actual - forecast) Tracking signal = MAD  (Actual - forecast) Tracking signal =  Actual - forecast n Tracking Signal

  30. Exponential Smoothing T3-2

  31. Linear Trend Equation T3-3

  32. Trend Adjusted Exponential Smoothing T3-4

  33. Simple Linear Regression T3-5

More Related