1. HMP 654 Operations Research and Control Systems in Health Care Spring/Summer 2016

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. 2 2 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 x 38
0.3 x 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 x 33
0.7 x 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 ( ) x ( )
-25 = 0.5 x ( ) + ( ) x 0
609.1 = 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 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