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Forecasting
| Question | Answer |
|---|---|
| Forecast | an estimate about the future value of a variable of interest |
| Forecast are important to making informed decisions | True |
| Expected Level of Demand | The level of demand may be a function of some structural variation such as trend or seasonal variation |
| Accuracy | Related to the potential size of forecast error (ability of the forecaster to correctly model demand) |
| Accounting | New product/process cost estimates, profit projections, cash management |
| Finance | Equipment replacement needs, timing, and amount of funding/borrowing needs |
| Human Resources | Hiring activities, including recruitment, interviewing, and training; layoff planning including outplacement counseling. |
| Marketing | Pricing and promotion, e-business strategies, global competition strategies |
| MIS | New/revised information systems, internet services |
| Operations | Scheduled, capacity planning |
| Product/Service Design | Revision of current features, design of new products or services |
| Qualitative Forecasring | permits the inclusion of soft information |
| Quantitative Forecasting | rely hard on data |
| Executive Opinions | a small group of upper-level managers may meet and collectively develop a forecast |
| Salesforce Opinions | Members of the sales or customer service staff can be good sources of information due to their direct contact with customers and may be aware of plans customers may be considering for the future |
| Consumer Survey | Since consumers ultimately determine demand, it makes sense to solicit input from them |
| Delphi Method | an iterative process intended to achieve a consensus |
| Time-Series | a time ordered sequence of observations taken at regular time intervals |
| Time Series Forecast | forecasts that project patterns identified in recent time-series observations |
| Trend | refers to a long term upward or downward movement in data due to population shifts, changing income, cultural changes |
| Seasonality | refers to a short term , fairly regular variations related to the calendar or time of the day |
| Cycle | are wavelike variations lasting more than one year |
| Irregular Variations | are due to unusual circumstances that do not reflect typical behavior |
| Random Variation | are residual variation that remains after all other behaviors have been accounted for |
| Naive Forecast | uses a single previous value of a time series as the basis for a forecast |
| Stable time series | the forecast for a time period is equal to the previous time period's value |
| Time Series Forecasting Averaging | these techniques work best when a series tends to vary about an average |
| Moving Average | technique that averages a number of the most recent actual values in generating a forecast |
| Weighted Moving Average | is similar to the moving average, except that it typically assigns more weight to the most recent values in the time series |
| Exponential Smoothing | A weighted average method that is based on the previous forecast plus a percentage of the forecast error |
| Linear Trend Equation | a simple data plot can reveal the existence and nature of a trend |
| Trend Adjusted Exponential Smoothing | has the ability to respond to changes in trend |
| Seasonality | regularly repeating movements in series values that can be tied to recurring events |
| Seasonality Relatives | the seasonal percentage used in the multiplicative seasonally adjusted forecasting model |
| Multiplicative | seasonality is expressed as a percentage of the average (or trend) amount which is then used to multiply the value of a series in order to incorporate seasonality |
| Additive | |
| Time Series Forecast | forecasts that project patterns identified in recent time-series observations |
| Trend | refers to a long term upward or downward movement in data due to population shifts, changing income, cultural changes |
| Seasonality | refers to a short term , fairly regular variations related to the calendar or time of the day |
| Cycle | are wavelike variations lasting more than one year |
| Irregular Variations | are due to unusual circumstances that do not reflect typical behavior |
| Random Variation | are residual variation that remains after all other behaviors have been accounted for |
| Naive Forecast | uses a single previous value of a time series as the basis for a forecast |
| Stable time series | the forecast for a time period is equal to the previous time period's value |
| Time Series Forecasting Averaging | these techniques work best when a series tends to vary about an average |
| Moving Average | technique that averages a number of the most recent actual values in generating a forecast |
| Weighted Moving Average | is similar to the moving average, except that it typically assigns more weight to the most recent values in the time series |
| Exponential Smoothing | A weighted average method that is based on the previous forecast plus a percentage of the forecast error |
| Linear Trend Equation | a simple data plot can reveal the existence and nature of a trend |
| Trend Adjusted Exponential Smoothing | has the ability to respond to changes in trend |
| Seasonality | regularly repeating movements in series values that can be tied to recurring events |
| Seasonality Relatives | the seasonal percentage used in the multiplicative seasonally adjusted forecasting model |
| Multiplicative | seasonality is expressed as a percentage of the average (or trend) amount which is then used to multiply the value of a series in order to incorporate seasonality |
| Additive | seasonality is expressed as quantity that gets added to or subtracted from time-series average in order to incorporate seasonality |
| Seasonal Relatives | the seasonal percentage used in the multiplicative seasonally adjusted forecasting model |
| Associative Techniques | are based on the development of an equation that summarizes the effects of predictor variables |
| Predictor Variables | variables that can be used to predict values of the variable interest |
| Regression | a technique for fitting a line to a set of data points |
| Simple Linear Regression | the simplest form of regression that involves a linear relationship between two variables |
| Correlation | a measure of strength and direction of relationship between two variables |
Created by:
s4bine