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Forecasting

Forecasting. MD707 Operations Management Professor Joy Field. Components of the Forecast. Forecasting using Judgment Methods. Sales force estimates Executive opinion Market research Delphi method. Forecasting using Time Series Methods. Naïve forecasts Moving averages

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Forecasting

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  1. Forecasting MD707 Operations Management Professor Joy Field

  2. Components of the Forecast

  3. Forecasting using Judgment Methods • Sales force estimates • Executive opinion • Market research • Delphi method

  4. Forecasting using Time Series Methods • Naïve forecasts • Moving averages • Weighted moving averages • Exponential smoothing • Trend-adjusted exponential smoothing • Multiplicative seasonal method

  5. Moving Average Method MonthCustomers 1 800 2 740 3 810 4 790 • Use a 3-month moving average, what is the forecast for month 5? • If the actual demand for month 5 is 805 customers, what is the forecast for month 6?

  6. Comparison of Three-Week and Six-Week Moving Average Forecasts

  7. Weighted Moving Average Method MonthCustomers 1 800 2 740 3 810 4 790 • Let Calculate the forecast for Month 5. • If the actual number of customers in month 5 is 805, what is the forecast for month 6?

  8. Exponential Smoothing MonthCustomers 1 800 2 740 3 810 4 790 • Suppose What is the forecast for Month 5? • If the actual number of customers in month 5 is 805, what is the forecast for month 6?

  9. Trend-Adjusted Exponential Smoothing MonthCustomers 1 48 2 52 3 50 4 54 5 55 • Using months 1-4, an initial estimate of the trend for Month 5 is 2 [(4-2+4)/3 = 2]. The starting forecast for month 5 is 54+2 = 56. Using and forecast the number of customers in month 6.

  10. Trend-Adjusted Exponential Smoothing (cont.) • If the actual number of customers in month 6 is 58, what is the forecast for month 7?

  11. Multiplicative Seasonal Method Procedure • Calculate the trend line based on the available data using regression. • Calculate the centered moving average, with the number of periods equal to the number of seasons. • Calculate the seasonal relative for a period by dividing the actual demand for the period by the corresponding centered moving average. • Calculate the overall estimated seasonal relative by averaging the seasonal relatives from the same periods over the cycle. • Calculate the trend values for each of the periods to be forecast based on the trend line determined in Step 1. • To get a forecast for a given period in a future cycle, multiply the seasonal factor by the trend values.

  12. Multiplicative Seasonal Method Example * Using regression, the trend line is 218.6 + 12.48t.

  13. Linear Regression y = a + bx • where • y = dependent (predicted) variable • x = independent (predictor) variable • a = y-intercept of the line (i.e., value of y when x = 0) • b = slope of the line

  14. Linear Regression Line Relative to Actual Data

  15. Regression Analysis Example An analyst for a chain of seafood restaurants is interested in forecasting the number of crab cake appetizers sold each week. He believes that the number sold has a linear relationship to the price and uses linear regression to determine if this is the case.

  16. Regression Analysis Example (cont.)

  17. Least Squares Regression LineAppetizer Example

  18. Interpretation of the Regression Intercept

  19. Another Regression Analysis Example A professor is interested in determining whether average study hours per week is a good predictor of test scores. The results of her study are: A student says: "Professor, what can I do to get a B or better on the next test. The professor asks, "On average, how many hours do you spend studying for this course per week?" The student responds, "About 2 hours." Use linear regression to forecast the student's test score.

  20. Another Regression Analysis Example (cont.)

  21. Forecast Error Measures • Bias • Average error • Variability • Mean squared error (MSE) • Standard deviation (s) • Mean absolute error (MAD) • Mean percent absolute error (MAPE) • Relative bias • Tracking signal (TS)

  22. Summarizing Forecast Accuracy

  23. Tracking and Analyzing Forecast Errors 2s Control Chart for Errors UCL = 69.6 LCL = -69.6

  24. Forecast Performance of Various Forecasting Methods for a Medical Clinic

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