Forecasting

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Forecasting

  1. 1. 3-1 ForecastingCHAPTER 7 Forecasting Prepared by : Arya Wirabhuana, ST, M.Sc
  2. 2. 3-2 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
  3. 3. 3-3 Forecasting Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services
  4. 4. 3-4 Forecasting  Assumes causal system past ==> future  Forecasts rarely perfect because of randomness  Forecasts more accurate for groups vs. individuals I see that you will get an A this semester.  Forecast accuracy decreases as time horizon increases
  5. 5. 3-5 Forecasting Elements of a Good Forecast Timely Reliable Accurate Written
  6. 6. 3-6 Forecasting Steps in the Forecasting Process “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
  7. 7. 3-7 Forecasting 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. 8. 3-8 Forecasting Judgmental Forecasts  Executive opinions  Sales force opinions  Consumer surveys  Outside opinion  Delphi method  Opinions of managers and staff  Achieves a consensus forecast
  9. 9. 3-9 Forecasting 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. 10. 3-10 Forecasting Forecast VariationsFigure 3.1 Irregular variatio n Trend Cycles 90 89 88 Seasonal variations
  11. 11. 3-11 Forecasting Naive Forecasts Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... The forecast for any period equals the previous period’s actual value.
  12. 12. 3-12 Forecasting 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. 13. 3-13 Forecasting 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. 14. 3-14 Forecasting Techniques for Averaging  Moving average  Weighted moving average  Exponential smoothing
  15. 15. 3-15 Forecasting Moving Averages  Moving average – A technique that averages a number of recent actual values, updated as new values become available. n 1 Ai i= MAn = n  Weighted moving average – More recent values in a series are given more weight in computing the forecast.
  16. 16. 3-16 Forecasting Simple Moving Average Actual MA5 47 45 43 41 39 37 MA3 35 1 2 3 4 5 6 7 8 9 10 11 12 n 1 Ai i= MAn = n
  17. 17. 3-17 Forecasting Exponential Smoothing Ft = Ft-1 + (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.
  18. 18. 3-18 Forecasting Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1)  Weighted averaging method based on previous forecast plus a percentage of the forecast error  A-F is the error term,  is the % feedback
  19. 19. 3-19 Forecasting Example 3 - Exponential Smoothing Period Actual Alpha = 0.1 Error Alpha = 0.4 Error 1 42 2 40 42 -2.00 42 -2 3 43 41.8 1.20 41.2 1.8 4 40 41.92 -1.92 41.92 -1.92 5 41 41.73 -0.73 41.15 -0.15 6 39 41.66 -2.66 41.09 -2.09 7 46 41.39 4.61 40.25 5.75 8 44 41.85 2.15 42.55 1.45 9 45 42.07 2.93 43.13 1.87 10 38 42.36 -4.36 43.88 -5.88 11 40 41.92 -1.92 41.53 -1.53 12 41.73 40.92
  20. 20. 3-20 Forecasting Picking a Smoothing Constant Actual 50   .4   .1 Demand 45 40 35 1 2 3 4 5 6 7 8 9 10 11 12 Period
  21. 21. 3-21 Forecasting Common Nonlinear TrendsFigure 3.5 Parabolic Exponential Growth
  22. 22. 3-22 Forecasting Linear Trend Equation Ft Ft = a + bt  Ft = Forecast for period t 0 1 2 3 4 5 t  t = Specified number of time periods  a = Value of Ft at t = 0  b = Slope of the line
  23. 23. 3-23 Forecasting Calculating a and b n  (ty) -  t  y b = n t 2 - ( t) 2   y - b t a = n
  24. 24. 3-24 Forecasting Linear Trend Equation Example t y 2 Week t Sales ty 1 1 150 150 2 4 157 314 3 9 162 486 4 16 166 664 5 25 177 885  t = 15  t = 55  y = 812  ty = 2499 2 2 ( t) = 225
  25. 25. 3-25 Forecasting Linear Trend Calculation 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
  26. 26. 3-26 Forecasting 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
  27. 27. 3-27 Forecasting Linear Model Seems Reasonable X Y Computed 7 15 relationship 2 10 6 13 50 4 15 40 14 25 30 15 27 20 10 16 24 0 12 20 0 5 10 15 20 25 14 27 20 44 15 34 7 17 A straight line is fitted to a set of sample points.
  28. 28. 3-28 Forecasting 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
  29. 29. 3-29 Forecasting MAD, MSE, and MAPE  Actual  forecast MAD = n 2  ( Actual  forecast) MSE = n -1 ( Actual  forecas / Actual*100) MAPE = t n
  30. 30. 3-30 Forecasting Example 10 Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*100 1 217 215 2 2 4 0.92 2 213 216 -3 3 9 1.41 3 216 215 1 1 1 0.46 4 210 214 -4 4 16 1.90 5 213 211 2 2 4 0.94 6 219 214 5 5 25 2.28 7 216 217 -1 1 1 0.46 8 212 216 -4 4 16 1.89 -2 22 76 10.26 MAD= 2.75 MSE= 10.86 MAPE= 1.28
  31. 31. 3-31 Forecasting 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
  32. 32. 3-32 Forecasting Sources of Forecast errors  Model may be inadequate  Irregular variations  Incorrect use of forecasting technique
  33. 33. 3-33 Forecasting Tracking Signal •Tracking signal –Ratio of cumulative error to MAD Tracking signal = (Actual-forecast) MAD Bias – Persistent tendency for forecasts to be Greater or less than actual values.
  34. 34. 3-34 Forecasting 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
  35. 35. 3-35 Forecasting Exponential Smoothing
  36. 36. 3-36 Forecasting Linear Trend Equation
  37. 37. 3-37 Forecasting Simple Linear Regression

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