1. Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When quantitative data are difficult to obtain, qualitative models may be appropriate. 2. Approaches are qualitative and quantitative. Qualitative is relatively subjective; quantitative uses numeric models. 3. Short-range (under 3 months), medium-range (3 months to 3 years), and long-range (over 3 years).
4. The steps that should be used to develop a forecasting
(a) Determine the purpose and use of the forecast
(b) Select the item or quantities that are to be forecasted
(c) Determine the time horizon of the forecast
(d) Select the type of forecasting model to be used
(e) Gather the necessary data
(f) Validate the forecasting model
(g) Make the forecast
(h) Implement and evaluate the results
5. Any three of: sales planning, production planning and budgeting, cash budgeting, analyzing various operating plans. 6. There is no mechanism for growth in these models; they are built exclusively from historical demand values. Such methods will always lag trends. 7. Exponential smoothing is a weighted moving average where all previous values are weighted with a set of weights that decline exponentially. 8. MAD, MSE, and MAPE are common measures of forecast accuracy. To find the more accurate forecasting model, forecast with each tool for several periods where the demand outcome is known, and calculate MSE, MAPE, or MAD for each. The smaller error indicates the better forecast. 9. The Delphi technique involves:
(a) Assembling a group of experts in such a manner as to preclude direct communication between identifiable members of the group (b) Assembling the responses of each expert to the questions or problems of interest
(c) Summarizing these responses
(d) Providing each expert with the summary of all responses (e) Asking each expert to study the summary of the responses and respond again to the questions or problems of interest. (f) Repeating steps (b) through (e) several times as necessary to obtain convergence in responses. If convergence has not been obtained by the end of the fourth cycle, the responses at that time should probably be accepted and the process terminated—little additional convergence is likely if the process is continued. 10. A time series model predicts on the basis of the assumption that the future is a function of the past, whereas an associative model incorporates into the model the variables of factors that might influence the quantity being forecast. 11. A time series is a sequence of evenly spaced data points with the four components of trend, seasonality, cyclical, and random variation. 12. When the smoothing constant, (, is large (close to 1.0), more weight is given to recent data; when ( is low (close to 0.0), more weight is given to past data. 13. Seasonal patterns are of fixed duration and repeat regularly. Cycles vary in length and regularity. Seasonal indices allow “generic” forecasts to be made specific to the month, week, etc., of the application. 14. Exponential smoothing weighs all previous values with a set of weights that decline exponentially. It can place a full weight on the most recent period (with an alpha of 1.0). This, in effect, is the naïve approach, which places all its emphasis on last period’s actual demand. 15. Adaptive forecasting refers to computer monitoring of tracking signals and self-adjustment if a signal passes its present limit. 16. Tracking signals alert the user of a forecasting tool to periods in which the forecast was in significant error. 17. The correlation coefficient measures the degree to which the independent and dependent variables move together. A negative value would mean that as X increases, Y tends to fall. The variables move together, but move in opposite directions. 18. Independent variable...