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# 05 forecasting

Quantitative Analysis for Management, 11e (Render)

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### 05 forecasting

1. 1. © 2008 Prentice-Hall, Inc. Chapter 5 To accompany Quantitative Analysis for Management, Tenth Edition, by Render, Stair, and Hanna Power Point slides created by Jeff Heyl Forecasting © 2009 Prentice-Hall, Inc.
2. 2. © 2009 Prentice-Hall, Inc. 5 – 2 Introduction  Managers are always trying to reduce uncertainty and make better estimates of what will happen in the future  This is the main purpose of forecasting  Some firms use subjective methods  Seat-of-the pants methods, intuition, experience  There are also several quantitative techniques  Moving averages, exponential smoothing, trend projections, least squares regression analysis
3. 3. © 2009 Prentice-Hall, Inc. 5 – 3 Introduction  Eight steps to forecasting : 1. Determine the use of the forecast—what objective are we trying to obtain? 2. Select the items or quantities that are to be forecasted 3. Determine the time horizon of the forecast 4. Select the forecasting model or models 5. Gather the data needed to make the forecast 6. Validate the forecasting model 7. Make the forecast 8. Implement the results
4. 4. © 2009 Prentice-Hall, Inc. 5 – 4 Introduction  These steps are a systematic way of initiating, designing, and implementing a forecasting system  When used regularly over time, data is collected routinely and calculations performed automatically  There is seldom one superior forecasting system  Different organizations may use different techniques  Whatever tool works best for a firm is the one they should use
5. 5. © 2009 Prentice-Hall, Inc. 5 – 5 Regression Analysis Multiple Regression Moving Average Exponential Smoothing Trend Projections Decomposition Delphi Methods Jury of Executive Opinion Sales Force Composite Consumer Market Survey Time-Series Methods Qualitative Models Causal Methods Forecasting Models Forecasting Techniques Figure 5.1
6. 6. © 2009 Prentice-Hall, Inc. 5 – 6 Time-Series Models  Time-series models attempt to predict the future based on the past  Common time-series models are  Moving average  Exponential smoothing  Trend projections  Decomposition  Regression analysis is used in trend projections and one type of decomposition model
7. 7. © 2009 Prentice-Hall, Inc. 5 – 7 Causal Models  Causal modelsCausal models use variables or factors that might influence the quantity being forecasted  The objective is to build a model with the best statistical relationship between the variable being forecast and the independent variables  Regression analysis is the most common technique used in causal modeling
8. 8. © 2009 Prentice-Hall, Inc. 5 – 8 Qualitative Models  Qualitative modelsQualitative models incorporate judgmental or subjective factors  Useful when subjective factors are thought to be important or when accurate quantitative data is difficult to obtain  Common qualitative techniques are  Delphi method  Jury of executive opinion  Sales force composite  Consumer market surveys
9. 9. © 2009 Prentice-Hall, Inc. 5 – 9 Qualitative Models  Delphi MethodDelphi Method – an iterative group process where (possibly geographically dispersed) respondentsrespondents provide input to decision makersdecision makers  Jury of Executive OpinionJury of Executive Opinion – collects opinions of a small group of high-level managers, possibly using statistical models for analysis  Sales Force CompositeSales Force Composite – individual salespersons estimate the sales in their region and the data is compiled at a district or national level  Consumer Market SurveyConsumer Market Survey – input is solicited from customers or potential customers regarding their purchasing plans
10. 10. © 2009 Prentice-Hall, Inc. 5 – 10 Scatter Diagrams 0 50 100 150 200 250 300 350 400 450 0 2 4 6 8 10 12 Time (Years) AnnualSales Radios Televisions Compact Discs Scatter diagrams are helpful when forecasting time-series data because they depict the relationship between variables.
11. 11. © 2009 Prentice-Hall, Inc. 5 – 11 Scatter Diagrams  Wacker Distributors wants to forecast sales for three different products YEAR TELEVISION SETS RADIOS COMPACT DISC PLAYERS 1 250 300 110 2 250 310 100 3 250 320 120 4 250 330 140 5 250 340 170 6 250 350 150 7 250 360 160 8 250 370 190 9 250 380 200 10 250 390 190 Table 5.1
12. 12. © 2009 Prentice-Hall, Inc. 5 – 12 Scatter Diagrams Figure 5.2          330 – 250 – 200 – 150 – 100 – 50 – | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 Time (Years) AnnualSalesofTelevisions (a)  Sales appear to be constant over time Sales = 250  A good estimate of sales in year 11 is 250 televisions
13. 13. © 2009 Prentice-Hall, Inc. 5 – 13 Scatter Diagrams            Sales appear to be increasing at a constant rate of 10 radios per year Sales = 290 + 10(Year)  A reasonable estimate of sales in year 11 is 400 televisions 420 – 400 – 380 – 360 – 340 – 320 – 300 – 280 – | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 Time (Years) AnnualSalesofRadios (b) Figure 5.2
14. 14. © 2009 Prentice-Hall, Inc. 5 – 14 Scatter Diagrams           This trend line may not be perfectly accurate because of variation from year to year  Sales appear to be increasing  A forecast would probably be a larger figure each year 200 – 180 – 160 – 140 – 120 – 100 – | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 Time (Years) AnnualSalesofCDPlayers (c) Figure 5.2
15. 15. © 2009 Prentice-Hall, Inc. 5 – 15 Measures of Forecast Accuracy  We compare forecasted values with actual values to see how well one model works or to compare models Forecast error = Actual value – Forecast value  One measure of accuracy is the mean absolutemean absolute deviationdeviation (MADMAD) n ∑= errorforecast MAD
16. 16. © 2009 Prentice-Hall, Inc. 5 – 16 Measures of Forecast Accuracy  Using a naïvenaïve forecasting model YEAR ACTUAL SALES OF CD PLAYERS FORECAST SALES ABSOLUTE VALUE OF ERRORS (DEVIATION), (ACTUAL – FORECAST) 1 110 — — 2 100 110 |100 – 110| = 10 3 120 100 |120 – 110| = 20 4 140 120 |140 – 120| = 20 5 170 140 |170 – 140| = 30 6 150 170 |150 – 170| = 20 7 160 150 |160 – 150| = 10 8 190 160 |190 – 160| = 30 9 200 190 |200 – 190| = 10 10 190 200 |190 – 200| = 10 11 — 190 — Sum of |errors| = 160 MAD = 160/9 = 17.8 Table 5.2
17. 17. © 2009 Prentice-Hall, Inc. 5 – 17 Measures of Forecast Accuracy  Using a naïvenaïve forecasting model YEAR ACTUAL SALES OF CD PLAYERS FORECAST SALES ABSOLUTE VALUE OF ERRORS (DEVIATION), (ACTUAL – FORECAST) 1 110 — — 2 100 110 |100 – 110| = 10 3 120 100 |120 – 110| = 20 4 140 120 |140 – 120| = 20 5 170 140 |170 – 140| = 30 6 150 170 |150 – 170| = 20 7 160 150 |160 – 150| = 10 8 190 160 |190 – 160| = 30 9 200 190 |200 – 190| = 10 10 190 200 |190 – 200| = 10 11 — 190 — Sum of |errors| = 160 MAD = 160/9 = 17.8 Table 5.2 817 9 160errorforecast .MAD === ∑ n
18. 18. © 2009 Prentice-Hall, Inc. 5 – 18 Measures of Forecast Accuracy  There are other popular measures of forecast accuracy  The mean squared errormean squared error n ∑= 2 error)( MSE  The mean absolute percent errormean absolute percent error %MAPE 100 actual error n ∑ =  And biasbias is the average error and tells whether the forecast tends to be too high or too low and by how much. Thus, it can be negative or positive.
19. 19. © 2009 Prentice-Hall, Inc. 5 – 19 Measures of Forecast Accuracy Year Actual CD Sales Forecast Sales |Actual -Forecast| 1 110 2 100 110 10 3 120 100 20 4 140 120 20 5 170 140 30 6 150 170 20 7 160 150 10 8 190 160 30 9 200 190 10 10 190 200 10 11 190 Sum of |errors| 160 MAD 17.8
20. 20. © 2009 Prentice-Hall, Inc. 5 – 20 Hospital Days Forecast Error Example Ms. Smith forecasted total hospital inpatient days last year. Now that the actual data are known, she is reevaluating her forecasting model. Compute the MAD, MSE, and MAPE for her forecast. Month Forecast Actual JAN 250 243 FEB 320 315 MAR 275 286 APR 260 256 MAY 250 241 JUN 275 298 JUL 300 292 AUG 325 333 SEP 320 326 OCT 350 378 NOV 365 382 DEC 380 396
21. 21. © 2009 Prentice-Hall, Inc. 5 – 21 Hospital Days Forecast Error Example Forecast Actual |error| error2 |error/actual| JAN 250 243 7 49 0.03 FEB 320 315 5 25 0.02 MAR 275 286 11 121 0.04 APR 260 256 4 16 0.02 MAY 250 241 9 81 0.04 JUN 275 298 23 529 0.08 JUL 300 292 8 64 0.03 AUG 325 333 8 64 0.02 SEP 320 326 6 36 0.02 OCT 350 378 28 784 0.07 NOV 365 382 17 289 0.04 DEC 380 396 16 256 0.04 AVERAGE MAD= 11.83 MSE= 192.83 MAPE= .0381*100 = 3.81
22. 22. © 2009 Prentice-Hall, Inc. 5 – 22 Time-Series Forecasting Models  A time series is a sequence of evenly spaced events (weekly, monthly, quarterly, etc.)  Time-series forecasts predict the future based solely of the past values of the variable  Other variables, no matter how potentially valuable, are ignored
23. 23. © 2009 Prentice-Hall, Inc. 5 – 23 Decomposition of a Time-Series  A time series typically has four components 1.1. TrendTrend (TT) is the gradual upward or downward movement of the data over time 2.2. SeasonalitySeasonality (SS) is a pattern of demand fluctuations above or below trend line that repeats at regular intervals 3.3. CyclesCycles (CC) are patterns in annual data that occur every several years 4.4. Random variationsRandom variations (RR) are “blips” in the data caused by chance and unusual situations
24. 24. © 2009 Prentice-Hall, Inc. 5 – 24 Decomposition of a Time-Series Average Demand over 4 Years Trend Component Actual Demand Line Time DemandforProductorService | | | | Year Year Year Year 1 2 3 4 Seasonal Peaks Figure 5.3
25. 25. © 2009 Prentice-Hall, Inc. 5 – 25 Decomposition of a Time-Series  There are two general forms of time-series models  The multiplicative model Demand = T x S x C x R  The additive model Demand = T + S + C + R  Models may be combinations of these two forms  Forecasters often assume errors are normally distributed with a mean of zero
26. 26. © 2009 Prentice-Hall, Inc. 5 – 26 Moving Averages  Moving averagesMoving averages can be used when demand is relatively steady over time  The next forecast is the average of the most recent n data values from the time series  The most recent period of data is added and the oldest is dropped This methods tends to smooth out short-term irregularities in the data series n n periodspreviousindemandsofSum forecastaverageMoving =
27. 27. © 2009 Prentice-Hall, Inc. 5 – 27 Moving Averages  Mathematically n YYY F nttt t 11 1 +−− + +++ = ... where = forecast for time period t + 1 = actual value in time period t n = number of periods to average tY 1+tF
28. 28. © 2009 Prentice-Hall, Inc. 5 – 28 Wallace Garden Supply Example  Wallace Garden Supply wants to forecast demand for its Storage Shed  They have collected data for the past year  They are using a three-month moving average to forecast demand (n = 3)
29. 29. © 2009 Prentice-Hall, Inc. 5 – 29 Wallace Garden Supply Example Table 5.3 MONTH ACTUAL SHED SALES THREE-MONTH MOVING AVERAGE January 10 February 12 March 13 April 16 May 19 June 23 July 26 August 30 September 28 October 18 November 16 December 14 January — (12 + 13 + 16)/3 = 13.67 (13 + 16 + 19)/3 = 16.00 (16 + 19 + 23)/3 = 19.33 (19 + 23 + 26)/3 = 22.67 (23 + 26 + 30)/3 = 26.33 (26 + 30 + 28)/3 = 28.00 (30 + 28 + 18)/3 = 25.33 (28 + 18 + 16)/3 = 20.67 (18 + 16 + 14)/3 = 16.00 (10 + 12 + 13)/3 = 11.67
30. 30. © 2009 Prentice-Hall, Inc. 5 – 30 Weighted Moving Averages  Weighted moving averagesWeighted moving averages use weights to put more emphasis on recent periods  Often used when a trend or other pattern is emerging ∑ ∑=+ )( ))(( Weights periodinvalueActualperiodinWeight 1 i Ft  Mathematically n ntntt t www YwYwYw F +++ +++ = +−− + ... ... 21 1121 1 ere wi = weight for the ith observation
31. 31. © 2009 Prentice-Hall, Inc. 5 – 31 Weighted Moving Averages  Both simple and weighted averages are effective in smoothing out fluctuations in the demand pattern in order to provide stable estimates  Problems Increasing the size of n smoothes out fluctuations better, but makes the method less sensitive to real changes in the data Moving averages can not pick up trends very well – they will always stay within past levels and not predict a change to a higher or lower level
32. 32. © 2009 Prentice-Hall, Inc. 5 – 32 Wallace Garden Supply Example  Wallace Garden Supply decides to try a weighted moving average model to forecast demand for its Storage Shed  They decide on the following weighting scheme WEIGHTS APPLIED PERIOD 3 Last month 2 Two months ago 1 Three months ago 6 3 x Sales last month + 2 x Sales two months ago + 1 X Sales three months ago Sum of the weights
33. 33. © 2009 Prentice-Hall, Inc. 5 – 33 Wallace Garden Supply Example Table 5.4 MONTH ACTUAL SHED SALES THREE-MONTH WEIGHTED MOVING AVERAGE January 10 February 12 March 13 April 16 May 19 June 23 July 26 August 30 September 28 October 18 November 16 December 14 January — [(3 X 13) + (2 X 12) + (10)]/6 = 12.17 [(3 X 16) + (2 X 13) + (12)]/6 = 14.33 [(3 X 19) + (2 X 16) + (13)]/6 = 17.00 [(3 X 23) + (2 X 19) + (16)]/6 = 20.50 [(3 X 26) + (2 X 23) + (19)]/6 = 23.83 [(3 X 30) + (2 X 26) + (23)]/6 = 27.50 [(3 X 28) + (2 X 30) + (26)]/6 = 28.33 [(3 X 18) + (2 X 28) + (30)]/6 = 23.33 [(3 X 16) + (2 X 18) + (28)]/6 = 18.67 [(3 X 14) + (2 X 16) + (18)]/6 = 15.33
34. 34. © 2009 Prentice-Hall, Inc. 5 – 34 Wallace Garden Supply Example Program 5.1A
35. 35. © 2009 Prentice-Hall, Inc. 5 – 35 Wallace Garden Supply Example Program 5.1B
36. 36. © 2009 Prentice-Hall, Inc. 5 – 36 Exponential Smoothing  Exponential smoothingExponential smoothing is easy to use and requires little record keeping of data  It is a type of moving average ecast = Last period’s forecast + α(Last period’s actual demand – Last period’s forecast) Where α is a weight (or smoothing constantsmoothing constant) with a value between 0 and 1 inclusive A larger α gives more importance to recent data while a smaller value gives more importance to past data
37. 37. © 2009 Prentice-Hall, Inc. 5 – 37 Exponential Smoothing  Mathematically )( tttt FYFF −+=+ α1 re Ft+1 = new forecast (for time period t + 1) Ft = pervious forecast (for time period t) α = smoothing constant (0 ≤ α ≤ 1) Yt = pervious period’s actual demand  The idea is simple – the new estimate is the old estimate plus some fraction of the error in the last period
38. 38. © 2009 Prentice-Hall, Inc. 5 – 38 Exponential Smoothing Example  In January, February’s demand for a certain car model was predicted to be 142  Actual February demand was 153 autos  Using a smoothing constant of α = 0.20, what is the forecast for March? New forecast (for March demand) = 142 + 0.2(153 – 142) = 144.2 or 144 autos  If actual demand in March was 136 autos, the April forecast would be New forecast (for April demand) = 144.2 + 0.2(136 – 144.2) = 142.6 or 143 autos
39. 39. © 2009 Prentice-Hall, Inc. 5 – 39 Selecting the Smoothing Constant  Selecting the appropriate value for α is key to obtaining a good forecast  The objective is always to generate an accurate forecast  The general approach is to develop trial forecasts with different values of α and select the α that results in the lowest MAD
40. 40. © 2009 Prentice-Hall, Inc. 5 – 40 Port of Baltimore Example QUARTER ACTUAL TONNAGE UNLOADED FORECAST USING α =0.10 FORECAST USING α =0.50 1 180 175 175 2 168 175.5 = 175.00 + 0.10(180 – 175) 177.5 3 159 174.75 = 175.50 + 0.10(168 – 175.50) 172.75 4 175 173.18 = 174.75 + 0.10(159 – 174.75) 165.88 5 190 173.36 = 173.18 + 0.10(175 – 173.18) 170.44 6 205 175.02 = 173.36 + 0.10(190 – 173.36) 180.22 7 180 178.02 = 175.02 + 0.10(205 – 175.02) 192.61 8 182 178.22 = 178.02 + 0.10(180 – 178.02) 186.30 9 ? 178.60 = 178.22 + 0.10(182 – 178.22) 184.15 Table 5.5  Exponential smoothing forecast for two values of α
41. 41. © 2009 Prentice-Hall, Inc. 5 – 41 Selecting the Best Value of α QUARTER ACTUAL TONNAGE UNLOADED FORECAST WITH α = 0.10 ABSOLUTE DEVIATIONS FOR α = 0.10 FORECAST WITH α = 0.50 ABSOLUTE DEVIATIONS FOR α = 0.50 1 180 175 5….. 175 5…. 2 168 175.5 7.5.. 177.5 9.5.. 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.3.. Sum of absolute deviations 82.45 98.63 MAD = Σ|deviations| = 10.31 MAD = 12.33 Table 5.6 Best choiceBest choice
42. 42. © 2009 Prentice-Hall, Inc. 5 – 42 Port of Baltimore Example Program 5.2A
43. 43. © 2009 Prentice-Hall, Inc. 5 – 43 Port of Baltimore Example Program 5.2B
44. 44. © 2009 Prentice-Hall, Inc. 5 – 44 PM Computer: Moving Average Example  PM Computer assembles customized personal computers from generic parts  The owners purchase generic computer parts in volume at a discount from a variety of sources whenever they see a good deal.  It is important that they develop a good forecast of demand for their computers so they can purchase component parts efficiently.
45. 45. © 2009 Prentice-Hall, Inc. 5 – 45 PM Computers: Data Period Month Actual Demand 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 June 50 7 July 43 8 Aug 47 9 Sept 56  Compute a 2-month moving average  Compute a 3-month weighted average using weights of 4,2,1 for the past three months of data  Compute an exponential smoothing forecast using α = 0.7, previous forecast of 40  Using MAD, what forecast is most accurate?
46. 46. © 2009 Prentice-Hall, Inc. 5 – 46 PM Computers: Moving Average Solution 2 month MA Abs. Dev 3 month WMA Abs. Dev Exp.Sm. Abs. Dev 37.00 37.00 3.00 38.50 2.50 39.10 1.90 40.50 3.50 40.14 3.14 40.43 3.43 39.00 6.00 38.57 6.43 38.03 6.97 41.00 9.00 42.14 7.86 42.91 7.09 47.50 4.50 46.71 3.71 47.87 4.87 46.50 0.50 45.29 1.71 44.46 2.54 45.00 11.00 46.29 9.71 46.24 9.76 51.50 51.57 53.07 5.29 5.43 4.95MAD Exponential smoothing resulted in the lowest MAD.
47. 47. © 2009 Prentice-Hall, Inc. 5 – 47 Exponential Smoothing with Trend Adjustment  Like all averaging techniques, exponential smoothing does not respond to trends  A more complex model can be used that adjusts for trends  The basic approach is to develop an exponential smoothing forecast then adjust it for the trend t) = New forecast (Ft) + Trend correction (Tt)
48. 48. © 2009 Prentice-Hall, Inc. 5 – 48 Exponential Smoothing with Trend Adjustment  The equation for the trend correction uses a new smoothing constant β  Tt is computed by )()1( 11 tttt FFTT −+−= ++ ββ where Tt+1 = smoothed trend for period t + 1 Tt = smoothed trend for preceding period β = trend smooth constant that we select Ft+1 = simple exponential smoothed forecast for period t + 1 Ft = forecast for pervious period
49. 49. © 2009 Prentice-Hall, Inc. 5 – 49 Selecting a Smoothing Constant  As with exponential smoothing, a high value of β makes the forecast more responsive to changes in trend  A low value of β gives less weight to the recent trend and tends to smooth out the trend  Values are generally selected using a trial-and- error approach based on the value of the MAD for different values of β  Simple exponential smoothing is often referred to as first-order smoothingfirst-order smoothing  Trend-adjusted smoothing is called second-second- orderorder, double smoothingdouble smoothing, or Holt’s methodHolt’s method
50. 50. © 2009 Prentice-Hall, Inc. 5 – 50 Trend Projection  Trend projection fits a trend line to a series of historical data points  The line is projected into the future for medium- to long-range forecasts  Several trend equations can be developed based on exponential or quadratic models  The simplest is a linear model developed using regression analysis
51. 51. © 2009 Prentice-Hall, Inc. 5 – 51 Trend Projection  Trend projections are used to forecast time- series data that exhibit a linear trend.  A trend line is simply a linear regression equation in which the independent variable (X) is the time period  Least squares may be used to determine a trend projection for future forecasts.  Least squares determines the trend line forecast by minimizing the mean squared error between the trend line forecasts and the actual observed values.  The independent variable is the time period and the dependent variable is the actual observed value in the time series.
52. 52. © 2009 Prentice-Hall, Inc. 5 – 52 Trend Projection  The mathematical form is XbbY 10 +=ˆ where = predicted value b0 = intercept b1 = slope of the line X = time period (i.e., X = 1, 2, 3, …, n) Yˆ
53. 53. © 2009 Prentice-Hall, Inc. 5 – 53 Trend Projection ValueofDependentVariable Time * * * * * * *Dist2 Dist4 Dist6 Dist1 Dist3 Dist5 Dist7 Figure 5.4
54. 54. © 2009 Prentice-Hall, Inc. 5 – 54 Midwestern Manufacturing Company Example  Midwestern Manufacturing Company has experienced the following demand for it’s electrical generators over the period of 2001 – 2007 YEAR ELECTRICAL GENERATORS SOLD 2001 74 2002 79 2003 80 2004 90 2005 105 2006 142 2007 122 Table 5.7
55. 55. © 2009 Prentice-Hall, Inc. 5 – 55 Midwestern Manufacturing Company Example Program 5.3A Notice code instead of actual years
56. 56. © 2009 Prentice-Hall, Inc. 5 – 56 Midwestern Manufacturing Company Example Program 5.3B r2 says model predicts about 80% of the variability in demand Significance level for F-test indicates a definite relationship
57. 57. © 2009 Prentice-Hall, Inc. 5 – 57 Midwestern Manufacturing Company Example  The forecast equation is XY 54107156 ..ˆ +=  To project demand for 2008, we use the coding system to define X = 8 (sales in 2008) = 56.71 + 10.54(8) = 141.03, or 141 generators  Likewise for X = 9 (sales in 2009) = 56.71 + 10.54(9) = 151.57, or 152 generators
58. 58. © 2009 Prentice-Hall, Inc. 5 – 58 Midwestern Manufacturing Company Example        GeneratorDemand Year 160 – 150 – 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – 60 – 50 – | | | | | | | | | 2001 2002 2003 2004 2005 2006 2007 2008 2009  Actual Demand Line Trend Line XY 54107156 ..ˆ += Figure 5.5
59. 59. © 2009 Prentice-Hall, Inc. 5 – 59 Midwestern Manufacturing Company Example Program 5.4A
60. 60. © 2009 Prentice-Hall, Inc. 5 – 60 Midwestern Manufacturing Company Example Program 5.4B
61. 61. © 2009 Prentice-Hall, Inc. 5 – 61 Seasonal Variations  Recurring variations over time may indicate the need for seasonal adjustments in the trend line  A seasonal index indicates how a particular season compares with an average season  When no trend is present, the seasonal index can be found by dividing the average value for a particular season by the average of all the data
62. 62. © 2009 Prentice-Hall, Inc. 5 – 62 Seasonal Variations  Eichler Supplies sells telephone answering machines  Data has been collected for the past two years sales of one particular model  They want to create a forecast that includes seasonality
63. 63. © 2009 Prentice-Hall, Inc. 5 – 63 Seasonal Variations MONTH SALES DEMAND AVERAGE TWO- YEAR DEMAND MONTHLY DEMAND AVERAGE SEASONAL INDEXYEAR 1 YEAR 2 January 80 100 90 94 0.957 February 85 75 80 94 0.851 March 80 90 85 94 0.904 April 110 90 100 94 1.064 May 115 131 123 94 1.309 June 120 110 115 94 1.223 July 100 110 105 94 1.117 August 110 90 100 94 1.064 September 85 95 90 94 0.957 Seasonal index = Average two-year demand Average monthly demand Average monthly demand = = 94 1,128 12 months Table 5.8
64. 64. © 2009 Prentice-Hall, Inc. 5 – 64 Seasonal Variations  The calculations for the seasonal indices are Jan. July969570 12 2001 =× . , 1121171 12 2001 =× . , Feb. Aug.858510 12 2001 =× . , 1060641 12 2001 =× . , Mar. Sept.909040 12 2001 =× . , 969570 12 2001 =× . , Apr. Oct.1060641 12 2001 =× . , 858510 12 2001 =× . , May Nov.1313091 12 2001 =× . , 858510 12 2001 =× . , June Dec.1222231 12 2001 =× . , 858510 12 2001 =× . ,
65. 65. © 2009 Prentice-Hall, Inc. 5 – 65 Regression with Trend and Seasonal Components  Multiple regressionMultiple regression can be used to forecast both trend and seasonal components in a time series  One independent variable is time  Dummy independent variables are used to represent the seasons  The model is an additive decomposition model where X1 = time period X2 = 1 if quarter 2, 0 otherwise X3 = 1 if quarter 3, 0 otherwise X4 = 1 if quarter 4, 0 otherwise 44332211 XbXbXbXbaY ++++=ˆ
66. 66. © 2009 Prentice-Hall, Inc. 5 – 66 Regression with Trend and Seasonal Components Program 5.6A
67. 67. © 2009 Prentice-Hall, Inc. 5 – 67 Regression with Trend and Seasonal Components Program 5.6B (partial)
68. 68. © 2009 Prentice-Hall, Inc. 5 – 68 Regression with Trend and Seasonal Components  The resulting regression equation is 4321 130738715321104 XXXXY .....ˆ ++++=  Using the model to forecast sales for the first two quarters of next year  These are different from the results obtained using the multiplicative decomposition method  Use MAD and MSE to determine the best model 13401300738071513321104 =++++= )(.)(.)(.)(..ˆY 15201300738171514321104 =++++= )(.)(.)(.)(..ˆY
69. 69. © 2009 Prentice-Hall, Inc. 5 – 69 Regression with Trend and Seasonal Components  American Airlines original spare parts inventory system used only time-series methods to forecast the demand for spare parts  This method was slow to responds to even moderate changes in aircraft utilization let alone major fleet expansions  They developed a PC-based system named RAPS which uses linear regression to establish a relationship between monthly part removals and various functions of monthly flying hours  The computation now takes only one hour instead of the days the old system needed  Using RAPS provided a one time savings of \$7 million and a recurring annual savings of nearly \$1 million
70. 70. © 2009 Prentice-Hall, Inc. 5 – 70 Monitoring and Controlling Forecasts  Tracking signalsTracking signals can be used to monitor the performance of a forecast  Tacking signals are computed using the following equation MAD RSFE =signalTracking n ∑= errorforecast MAD where
71. 71. © 2009 Prentice-Hall, Inc. 5 – 71 Monitoring and Controlling Forecasts Acceptable Range Signal Tripped Upper Control Limit Lower Control Limit 0 MADs + – Time Figure 5.7 Tracking Signal
72. 72. © 2009 Prentice-Hall, Inc. 5 – 72 Monitoring and Controlling Forecasts  Positive tracking signals indicate demand is greater than forecast  Negative tracking signals indicate demand is less than forecast  Some variation is expected, but a good forecast will have about as much positive error as negative error  Problems are indicated when the signal trips either the upper or lower predetermined limits  This indicates there has been an unacceptable amount of variation  Limits should be reasonable and may vary from item to item
73. 73. © 2009 Prentice-Hall, Inc. 5 – 73 Regression with Trend and Seasonal Components  How do you decide on the upper and lower limits?  Too small a value will trip the signal too often and too large will cause a bad forecast  Plossl & Wight – use maximums of ±4 MADs for high volume stock items and ±8 MADs for lower volume items  One MAD is equivalent to approximately 0.8 standard deviation so that ±4 MADs =3.2 s.d.  For a forecast to be “in control”, 89% of the errors are expected to fall within ±2 MADs, 98% with ±3 MADs or 99.9% within ±4 MADs whenever the errors are approximately normally distributed
74. 74. © 2009 Prentice-Hall, Inc. 5 – 74 Kimball’s Bakery Example  Tracking signal for quarterly sales of croissants TIME PERIOD FORECAST DEMAND ACTUAL DEMAND ERROR RSFE |FORECAST | | ERROR | CUMULATIVE ERROR MAD TRACKING SIGNAL 1 100 90 –10 –10 10 10 10.0 –1 2 100 95 –5 –15 5 15 7.5 –2 3 100 115 +15 0 15 30 10.0 0 4 110 100 –10 –10 10 40 10.0 –1 5 110 125 +15 +5 15 55 11.0 +0.5 6 110 140 +30 +35 30 85 14.2 +2.5 214 6 85errorforecast .MAD === ∑ n sMAD. .MAD RSFE 52 214 35 signalTracking ===
75. 75. © 2009 Prentice-Hall, Inc. 5 – 75 Forecasting at Disney  The Disney chairman receives a dailyThe Disney chairman receives a daily report from his main theme parks thatreport from his main theme parks that contains only two numbers – thecontains only two numbers – the forecastforecast of yesterday’s attendance at the parks andof yesterday’s attendance at the parks and thethe actualactual attendanceattendance  An error close to zero (using MAPE as the measure) is expected  The annual forecast of total volume conducted in 1999 for the year 2000 resulted in a MAPE of 0
76. 76. © 2009 Prentice-Hall, Inc. 5 – 76 Using The Computer to Forecast  Spreadsheets can be used by small and medium-sized forecasting problems  More advanced programs (SAS, SPSS, Minitab) handle time-series and causal models  May automatically select best model parameters  Dedicated forecasting packages may be fully automatic  May be integrated with inventory planning and control