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O peration M anagement Forecasting

O peration M anagement Forecasting. Rachmat A. Anggara PMBS, BOPR 5301, Session 4. ??. Why we have to forecast??. Forecast Reduces Cost Under forecast  the condition when capacity is below actual demand Over forecast  the condition where capacity is above actual demand

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O peration M anagement Forecasting

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  1. Operation ManagementForecasting Rachmat A. Anggara PMBS, BOPR 5301, Session 4

  2. ?? Why we have to forecast??

  3. Forecast Reduces Cost • Under forecast the condition when capacity is below actual demand • Over forecast the condition where capacity is above actual demand • Increase Competitive advantage • Economic Forecast • Technological forecasts • Demand forecasts

  4. FORECASTING • Process of predicting a future event • Forecasting Time Horizons • Short-range Forecast • Medium-range Forecast • Long-range Forecast

  5. Forecasting Approach movie Intuitive Decision Making

  6. Qualitative Methods Jury of executive opinion Sales force composite Quantitative Method Consumer Market Survey Delphi method Time-Series Models Associative Model Forecasting Approach • Naive approach • Moving averages • Exponential smoothing • Trend projection • Linear regression

  7. Qualitative Method • Used when situation is vague and little data exist • New products • New technology • Involves intuition, experience • e.g., forecasting sales on Internet

  8. Quantitative Method • Used when situation is vague and little data exist • New products • New technology • Involves intuition, experience • e.g., forecasting sales on Internet

  9. Quantitative Method 1. TIME SERIES • Set of evenly spaced numerical data • Obtained by observing response variable at regular time periods • Forecast based only on past values • Assumes that factors influencing past and present will continue influence in future

  10. Trend Cyclical Seasonal Random Quantitative Method TIME SERIES COMPONENT

  11. Trend component Seasonal peaks Actual demand Demand for product or service Average demand over four years Random variation | | | | 1 2 3 4 Year Component of Demand

  12. Steps of Forecasting • Determine the use of the forecast • Select the items to be forecasted • Determine the time horizon of the forecast • Select the forecasting model(s) • Gather the data • Make the forecast • Validate and implement results

  13. Actual 3-Month Month Shed Sales Moving Average January 10 February 12 March 13 April 16 May 19 June 23 July 26 (12 + 13 + 16)/3 = 13 2/3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1/3 10 12 13 (10 + 12 + 13)/3 = 11 2/3 ∑ demand in previous n periods n Moving average = TIME SERIES METHOD 1. Moving Average

  14. Actual 3-Month Weighted Month Shed Sales Moving Average January 10 February 12 March 13 April 16 May 19 June 23 July 26 [(3 x 16) + (2 x 13) + (12)]/6 = 141/3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 201/2 10 12 13 [(3 x 13) + (2 x 12) + (10)]/6 = 121/6 Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago TIME SERIES METHOD 2. Weighted Moving Average

  15. Weighted moving average 30 – 25 – 20 – 15 – 10 – 5 – Actual sales Sales demand Moving average | | | | | | | | | | | | J F M A M J J A S O N D TIME SERIES METHOD Graph of Moving Averages

  16. TIME SERIES METHOD 3. Exponential Smoothing Ft = Ft – 1 +a(At – 1 - Ft – 1) where Ft = new forecast At – 1 = previous Actual Demand Ft – 1 = previous forecast a = smoothing (or weighting) constant (0  a  1) • Example – Ford Mustangs : • Predicted demand = 142 • Actual demand = 153 • Smoothing constant a = .20 • Next Period Forecast = …

  17. Rounded Absolute (Error)2 Absolute Actual Forecast Deviation Percentage Tonnage with for Error Quarter Unloadeda = .10 a = .10 1 180 175 5 52=25 2.78% 2 168 176 8 64 4.76% 3 159 175 16 256 10.06% 4 175 173 2 4 1.14% 5 190 173 17 289 8.95% 6 205 175 30 900 14.63% 7 180 178 2 4 1.11% 8 182 178 4 16 2.20% 84 1,558 45.63% n i = 1 100 ∑ |actuali - forecasti|/actuali n MAPE = MAD = ∑ |actual - forecast| n ∑(forecast errors)2 n MSE = TIME SERIES METHOD Measuring Forecast Error

  18. Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloadeda = .10 a = .10 a = .50 a = .50 1 180 175 5 175 5 2 168 176 8 178 10 3 159 175 16 173 14 4 175 173 2 166 9 5 190 173 17 170 20 6 205 175 30 180 25 7 180 178 2 193 13 8 182 178 4 186 4 84 100 TIME SERIES METHOD Calculate MAPE, α = 0.50

  19. Forecast including (FITt) = trend exponentially exponentially smoothed (Ft) + (Tt) smoothed forecast trend TIME SERIES METHOD 4. Exponential Smoothing with Trend Adjustment When a trend is present, exponential smoothing must be modified Ft = a(At - 1) + (1 - a)(Ft - 1 + Tt - 1) Tt = b(Ft - Ft - 1) + (1 - b)Tt - 1 Step 1: Compute Ft Step 2: Compute Tt Step 3: Calculate the forecast FITt= Ft+ Tt

  20. Exponential Actual Smoothing Tonnage FtTt with Trend Quarter Unloadeda = 0.10 β = .20 Adjustment 1 180 175 2 177 2 168 177 2 179.4 3 159 178 2 180.1 4 175 178 1 179.4 5 190 179 1 180.3 6 205 181 2 182.7 7 180 185 2 186.9 8 182 186 2 188.1 TIME SERIES METHOD Gambar perbandingan (xls)

  21. TIME SERIES METHOD Graphics

  22. ^ y = a + bx ^ where y = computed value of the variable to be predicted (dependent variable) a = y-axis intercept b = slope of the regression line x = the independent variable TIME SERIES METHOD 5. Trend Projections Fitting a trend line to historical data points to project into the medium-to-long-range Linear trends can be found using the least squares technique

  23. Actual observation (y value) Deviation7 Deviation5 Deviation6 Deviation3 Values of Dependent Variable Deviation4 Deviation1 Deviation2 ^ Trend line, y = a + bx Time period TIME SERIES METHOD Least Squares Method Figure 4.4

  24. Sxy - nxy Sx2 - nx2 b = ^ y = a + bx a = y - bx TIME SERIES METHOD Least Squares Method Equations to calculate the regression variables

  25. Time Electrical Power Year Period (x) Demand x2xy 1999 1 74 1 74 2000 2 79 4 158 2001 3 80 9 240 2002 4 90 16 360 2003 5 105 25 525 2004 6 142 36 852 2005 7 122 49 854 ∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063 x = 4 y = 98.86 3,063 - (7)(4)(98.86) 140 - (7)(42) a = y - bx = 98.86 - 10.54(4) = 56.70 ∑xy - nxy ∑x2 - nx2 b = = = 10.54 TIME SERIES METHOD Least Squares Example

  26. 160 – 150 – 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – 60 – 50 – Trend line, y = 56.70 + 10.54x ^ Power demand | | | | | | | | | 1999 2000 2001 2002 2003 2004 2005 2006 2007 Year TIME SERIES METHOD Least Squares Example

  27. ASSOCIATIVE METHOD Associative Forecasting • Forecasting an outcome based on predictor variables. • Methods: • Regression Analysis • Correlation Coefficients • Standard Error of the Estimate. • Multiple Regression Analysis.

  28. Sales Local Payroll ($000,000), y ($000,000,000), x 2.0 1 3.0 3 2.5 4 2.0 2 2.0 1 3.5 7 ^ y = a + bx ^ where y = computed value of the variable to be predicted (dependent variable) a = y-axis intercept b = slope of the regression line x = the independent variable though to predict the value of the dependent variable ASSOCIATIVE METHOD Regression Analysis Example:

  29. ^ y = 1.75 + .25x 4.0 – 3.0 – 2.0 – 1.0 – 3.25 Sales | | | | | | | 0 1 2 3 4 5 6 7 Area payroll ASSOCIATIVE METHOD Sales = 1.75 + .25(payroll) If payroll next year is estimated to be $600 million, then: Sales = 1.75 + .25(6) Sales = $325,000

  30. nSxy - SxSy [nSx2 - (Sx)2][nSy2 - (Sy)2] r = ASSOCIATIVE METHOD Correlation Coefficient • How strong is the linear relationship between the variables? • Correlation does not necessarily imply causality! • Coefficient of correlation, r, measures degree of association • Values range from -1 to +1

  31. ^ y = a + b1x1 + b2x2 … ASSOCIATIVE METHOD Multiple Regression If more than one independent variable is to be used in the model, linear regression can be extended to multiple regression to accommodate several independent variables Computationally, this is quite complex and generally done on the computer

  32. Questions?

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