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Chapter 17 Forecasting Demand for Services

Chapter 17 Forecasting Demand for Services. Learning Objectives Demand characteristics Overview of forecasting models Common demand pattern for services Linear regression to account for trend Seasonality indices for seasonal demand Combination of trend and seasonality. 17- 1.

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Chapter 17 Forecasting Demand for Services

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  1. Chapter 17Forecasting Demand for Services Learning Objectives Demand characteristics Overview of forecasting models Common demand pattern for services Linear regression to account for trend Seasonality indices for seasonal demand Combination of trend and seasonality 17-1

  2. Demand Demand Random movement Time (a) Trend Time (b) Cycle Demand Demand Time (c) Seasonal pattern Time (d) Trend with seasonal pattern Demand Characteristics

  3. Forecasting Models • Subjective Models Delphi Methods • Causal Models Regression Models • Time Series Models Moving Averages Exponential Smoothing 17-3

  4. xy - nxy x2- nx2 b = a = y - b x where n = number of periods x = = mean of the x values y = = mean of the y values x n y n Using Linear Regression to account for trend y = a + bx where a = intercept b = slope of the line x = time period y = forecast for demand for period x

  5. x(PERIOD) y(DEMAND) xy x2 1 37 37 1 2 40 80 4 3 41 123 9 4 37 148 16 5 45 225 25 6 50 300 36 7 43 301 49 8 47 376 64 9 56 504 81 10 52 520 100 11 55 605 121 12 54 648 144 78 557 3867 650 Least Squares Example

  6. x = = 6.5 y = = 46.42 b = = =1.72 a = y - bx = 46.42 - (1.72)(6.5) = 35.2 557 12 78 12 xy - nxy x2 - nx2 3867 - (12)(6.5)(46.42) 650 - 12(6.5)2 Least Squares Example (cont.)

  7. Linear trend line y = 35.2 + 1.72x Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – Actual Demand Linear trend line | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Period

  8. Ai Aij Seasonal factor = Si = Seasonal Adjustments • Repetitive increase/ decrease in demand • Use seasonal factor to adjust forecast • Si = seasonality index of period i • Ai(j) = demand in season i (in year j) Note: The method used here is different from the book

  9. DEMAND (1000’S PER QUARTER) YEAR 1 2 3 4 Total 2005 12.6 8.6 6.3 17.5 45.0 2006 14.1 10.3 7.5 18.2 50.1 2007 15.3 10.6 8.1 19.6 53.6 Total 42.0 29.5 21.9 55.3 148.7 42.0 148.7 29.5 148.7 55.3 148.7 21.9 148.7 A3 Aij A2 Aij A4 Aij A1 Aij S1 = = = 0.28 S2 = = = 0.20 S4 = = = 0.37 S3 = = = 0.15 Seasonal Adjustment (cont.)

  10. Forecast to account for both Trend and Seasonality Step 1: Calculate the seasonal index for each season. Step 2: Use linear regression to forecast the total demand for the following year to account for trend. (In the previous slide example, use the year as dependent variable, and yearly demand as independent variable) a = 40.97, b = 4.30 (Note: 2005/6/7 are years 1/2/3) F(2008)= 40.97 + 4.30(4) = 58.17 Step 3: Use the forecast total demand (obtained in Step 2) and multiply by the seasonal index to determine the forecast seasonal demand. SF1 = (S1) (F2008) = (0.28)(58.17) = 16.28 SF2 = (S2) (F2008) = (0.20)(58.17) = 11.63 SF3 = (S3) (F2008) = (0.15)(58.17) = 8.73 SF4 = (S4) (F2008) = (0.37)(58.17) = 21.53

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