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HMP 654 Operations Research and Control Systems in Health Care Fall 2014

HMP 654 Operations Research and Control Systems in Health Care Fall 2014. Forecasting - Introduction. Forecasting in Health Care Forecasting Models Structural Models Time Series Models Expert Judgment Time Series Models: Demand has exhibited some measurable structure in the past.

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HMP 654 Operations Research and Control Systems in Health Care Fall 2014

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  1. HMP 654Operations Research and Control Systems in Health CareFall 2014

  2. Forecasting - Introduction • Forecasting in Health Care • Forecasting Models • Structural Models • Time Series Models • Expert Judgment • Time Series Models: • Demand has exhibited some measurable structure in the past. • The structure will continue into the future.

  3. Forecasting - Time Series • Signal vs. Noise • Extrapolation Models • Accuracy of Forecasts

  4. Forecasting - Stationary Models • Stationary Time-Series • Moving Averages

  5. Forecasting - Moving Avgs.

  6. Forecasting - Moving Avgs. 33 + 38 2 38 + 31 2 33 + 38 + 31 + 35 4 SUMXMY2(B7:B26,D7:D26)/COUNT(D7:D26)

  7. Forecasting - Moving Avgs.

  8. Forecasting - Weighted M.A. • Weighted Moving Averages

  9. Forecasting - Weighted M.A. 0.3 x 33 + 0.7 x 38 0.3 x 38 + 0.7 x 31

  10. Forecasting - Weighted M.A Finding the Optimal Weights

  11. Forecasting - Weighted M.A. Finding the Optimal Weights MSE vs W2 W2

  12. Forecasting - Weighted M.A. Finding the Optimal Weights

  13. Forecasting - Weighted M.A. Finding the Optimal Weights

  14. Forecasting - Exp. Smoothing • Exponential Smoothing

  15. Forecasting - Exp. Smoothing 0.7 x 33 + 0.3 x 33 0.7 x 38 + 0.3 x 33

  16. Forecasting - Exp. Smoothing

  17. Forecasting - Trend Models

  18. Forecasting - Holt’s Method • Compute the base level Et for time period t using equation 11.6 • Compute expected trend value Tt for time period t using equation 11.7 • Compute the final forecast Y^t+k for time period t+k using equation 11.5

  19. Forecasting - Holt’s Method Initial base level = first demand value Set initial trend to 0 Forecast for Qtr. 3, 1990: 634.2= 0.5 x 584.1 + (1 - 0.5) x (684.2 + 0) -25 = 0.5 x (634.2 - 684.2) + (1 - 0.5) x 0 609.1 = 634.2 + 1 x (- 25)

  20. Forecasting - Regression • Linear Trend Model

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  23. Forecasting - Regression

  24. Forecasting - Regression • Linear Trend Model

  25. Forecasting - Regression • Quadratic Trend Model

  26. Forecasting - Regression

  27. Forecasting - Regression • Quadratic Trend Model

  28. Forecasting - Seasonality • Adjusting trend predictions with seasonal indices 102 + 107 + 106 + 108 + 106 5

  29. Forecasting - Seasonality

  30. Forecasting - Seasonality • Use of Seasonal Indices • Create a trend model and calculate the estimated value for each observation in the sample. • For each observation, calculate the ratio of the actual value to the predicted trend value • For each season, compute the average of the ratios calculated in step 2. These are the seasonal indices. • Multiply any forecast produced by the trend model by the appropriate seasonal index calculated in step 3.

  31. Forecasting - Seasonal Regression Models

  32. Forecasting - Seasonal Regression Models

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