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- 1. 3-1 ForecastingCHAPTER 7 Forecasting Prepared by : Arya Wirabhuana, ST, M.Sc
- 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 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. 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. 3-5 Forecasting Elements of a Good Forecast Timely Reliable Accurate Written
- 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. 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. 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. 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. 3-10 Forecasting Forecast VariationsFigure 3.1 Irregular variatio n Trend Cycles 90 89 88 Seasonal variations
- 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. 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. 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. 3-14 Forecasting Techniques for Averaging Moving average Weighted moving average Exponential smoothing
- 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. 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. 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. 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. 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. 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. 3-21 Forecasting Common Nonlinear TrendsFigure 3.5 Parabolic Exponential Growth
- 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. 3-23 Forecasting Calculating a and b n (ty) - t y b = n t 2 - ( t) 2 y - b t a = n
- 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. 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. 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. 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. 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. 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. 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. 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. 3-32 Forecasting Sources of Forecast errors Model may be inadequate Irregular variations Incorrect use of forecasting technique
- 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. 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. 3-35 Forecasting Exponential Smoothing
- 36. 3-36 Forecasting Linear Trend Equation
- 37. 3-37 Forecasting Simple Linear Regression

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