1. McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting

2. 3-2 Learning Objectives  List the elements of a good forecast.  Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each.  Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems.  Describe two measures of forecast accuracy.

3. 3-3 FORECAST:  A statement about the future value of a variable of interest such as demand.  Forecasting is used to make informed decisions.  Long-range  Short-range

4. 3-4 Forecasts  Forecasts affect decisions and activities throughout an organization  Accounting, finance  Human resources  Marketing  MIS  Operations  Product / service design

5. 3-5  Assumes causal system past ==> future  Forecasts rarely perfect because of randomness  Forecasts more accurate for groups vs. individuals  Forecast accuracy decreases as time horizon increases I see that you will get an A this semester. Features of Forecasts

6. 3-6 Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use

7. 3-7 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. 3-8 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 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 Forecast Variations Trend Irregular variatio n Seasonal variations 90 89 88 Figure 3.1 Cycles

11. 3-11 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  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)) Uses for Naïve Forecasts

13. 3-13 Techniques for Averaging  Moving average  Weighted moving average  Exponential smoothing

14. 3-14 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. F t = MA n = n A t-n + … A t-2 + A t-1 F t = WMA n = n w n A t-n + … w n-1 A t-2 + w 1 A t-1

15. 3-15 Moving Average Example n = 3 tAtAt FtFt 110 215 312 41712.33333 51514.66667 62014.66667 71717.33333

16. 3-16 Weighted Moving Average Moving Average Example n = 3 w t-1 0.6 w t-2 0.3 w t-3 0.1 tAtAt FtFt 110 215 312 41712.7 51515.3 62015.3 71718.2

17. 3-17 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. F t = (1-  )F t-1 +  ( A t-1 ) F t = F t-1 +  ( A t-1 - F t-1 )

18. 3-18 Weighted Moving Average Exponential Smoothing Example  =.7 tAtAt FtFt 110 21510 31213.5 41712.45 51515.635 62015.1905 71718.55715

19. 3-19 Linear Trend Equation  F t = Forecast for period t  t = Specified number of time periods  a = Value of F t at t = 0  b = Slope of the line F t = a + bt 0 1 2 3 4 5 t FtFt

20. 3-20 Calculating a and b b = n(ty) - ty nt 2 - ( t) 2 a = y - bt n   

21. 3-21 Linear Trend Equation Example

22. 3-22 Linear Trend Calculation y = 143.5 + 6.3t a= 812- 6.3(15) 5 = b= 5 (2499)- 15(812) 5(55)- 225 = 12495-12180 275-225 = 6.3 143.5

23. 3-23 Techniques for Seasonality  Seasonal variations  Regularly repeating movements in series values that can be tied to recurring events.  Seasonal relative  Percentage of average or trend  Centered moving average  A moving average positioned at the center of the data that were used to compute it.

24. 3-24 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

25. 3-25 MAD, MSE, and MAPE MAD = Actualforecast   n MSE = Actualforecast ) - 1 2   n ( MAPE = Actualforecas t  n / Actual*100) 

26. 3-26 Example 10

27. 3-27 Sources of Forecast errors  Model may be inadequate  Irregular variations  Incorrect use of forecasting technique

28. 3-28 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