Explain why forecasts form the basis of all supply chain

  1. Explain why forecasts form the basis of all supply chain decisions.
  2. How can Amazon use forecasting to improve its responsiveness and efficiency?
  3. Why forecast error is a critical factor in any forecast? Explain your response with an example.
  4. What does forecast accuracy exactly mean? How can one measure it?
  5. We know that a shorter forecast horizon results in a more accurate forecast. However, long-term forecasts are also necessary (e.g., for capacity planning). How can a firm combine the short-term and long-term forecasts in their decision making?
  6. Explain in your own words what collaborative forecasting is.
  7. Give an example in auto-manufacturing industry for how human inputs can improve forecast accuracy.
  8. Explain the two components of any forecasting method.
  9. Give an example of a product for which the forecast should be made jointly with another product.
  10. How can a branch supermarket such as Trader Joes use aggregate forecasting?
  11. Briefly explain level, trend, and seasonal factor in demand forecasting.
  12. EXCEL:
    1. Replicate the Excel model in Figure 7-2.
    2. Use Excel Regression tool as explained on page 181 to run a regression model.
    3. Replicate the Excel model in Figure 7-4.
    4. SUBMIT YOUR EXCEL MODEL on Blackboard.
  1. Why should the data be first deseasonalized before running the regression?
  2. EXCEL: Consider the following demand data for periods 1 through 10.
    1. Use the moving average technique with N = 4 to forecast the demand for periods 1 to 11. You can use the actual demand () as the forecast for the first three periods (i.e., set , , and ).























  1. Use simple exponential smoothing with to forecast the demand for periods1 to 11. Assume .
  2. Calculate the three measures of errors (MSE, MAD, and MAPE) for each of the moving average and exponential smoothing methods you used. (Hint: Calculate the error for periods 1 to 10 only).
  3. Provide an estimate for the standard deviation of forecast error (equation 7.23).
  4. Calculate the bias (equation 7.25) for each method.
  5. Calculate the tracking signal (TS, equation 7.26) for periods 1 to 10.
  6. Based on your observations in parts c, d, e, and f which method do you recommend?
  7. Use Excel Solver to find the value of that minimizes MSE (Hint: see Figure 7-5).
  8. SUBMIT YOUR EXCEL MODEL on Blackboard.
  1. Why is it important to estimate the forecast error?
  2. Why are errors in equation 7.21 squared?
  3. Explain each of the three main measures for forecast error and situations in which each is appropriate.
  4. What measure should be used to determine if the used forecasting technique should be changed (e.g., due to a sudden change in demand)?
  5. What is the advantage of the declining alpha method when using exponential smoothing?