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FORECASTING

CHAPTER. 3. FORECASTING. FORECASTING. Forecasts serve as a basis for planning--capacity, budgeting, sales, production, inventory, personnel Successful forecasting requires a skillful blending of both art and science Two uses of forecasts: Planning the system--Long Range

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FORECASTING

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  1. CHAPTER 3 FORECASTING

  2. FORECASTING • Forecasts serve as a basis for planning--capacity, budgeting, sales, production, inventory, personnel • Successful forecasting requires a skillful blending of both art and science • Two uses of forecasts: Planning the system--Long Range Planning the use of the system--Short Range

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

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

  5. 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 “The forecast”

  6. APPROACHES TO FORECASTING • QUALITATIVE--based on subjective inputs, soft data judgmental forecasts, opinions, hunches, experience, etc. • QUANTITATIVE--based on historical data --project past experience into the future --uncover relationships between variables that can be used to predict the future

  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 composite • Consumer surveys • Outside opinion • Opinions of managers and staff • Delphi technique

  9. QUANTITATIVE FORECASTS • Time-Series techniques --Naïve --Moving Average models --Exponential Smoothing models --Classical Decomposition --Box-Jenkins ARIMA models --Neural Networks

  10. QUANTITATIVE FORECASTS • Causal or Associative techniques --Simple linear regression --Multiple linear regression --Nonlinear regression

  11. FORECASTING DATA • “time-series” --time-ordered sequence of observations taken at regular intervals over a period of time Annual, Quarterly, Monthly, Weekly, Daily, Hourly, etc.

  12. UNDERLYING BEHAVIOR • Trend - long-term movement in data • Seasonality - short-term, regular, periodic variations in data • Cycles - wave-like variations of more than one year’s duration • Irregular variations - caused by unusual circumstances • Random variations - caused by chance

  13. Forecast Variations Irregularvariation Trend Cycles 90 89 88 Seasonal variations

  14. Naive Forecasts Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.… “the latest observation in a sequence is used as the forecast for the next period” Ft = At-1

  15. n Ai “an average that is repeatedly updated” å MAn Ft = i = 1 n Simple Moving Average

  16. Exponential Smoothing Ft = Ft-1 + a(At-1 - Ft-1) • 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.

  17. 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 • Tracking Signal - Ratio of cumulative error and MAD

  18. å - Actual forecast MAD = n 2 ( Actual - forecast) å MSE = n - 1 MAD,MSE, & MAPE Actual - forecast  X 100 MAPE = Actual n

  19. å (Actual - forecast) Tracking signal = MAD å (Actual - forecast) Tracking signal = å Actual - forecast n Tracking Signal

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