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Econometrics
scm 301 chp 9
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Terms in this set (23)
Laws of forecasting
1. forecasts are almost always wrong
2. forecasts in the near terms are more accurate
3. forecasts for groups tend to be more accurate
4. forecasts are not substitutes for actual values
quantitative forecasting models
forecasting models that use measurable, historical data to generate forecasts. Quantitative forecasting models can be divided into two major types; time series models and causal models.
qualitative forecasting techniques
forecasting techniques based on intuition or informed opinion. These techniques are used when data are scarce not available or irrelevant
market survey
a structured questionnaire submitted to potential customers, often to gauge potential demand
panel consensus forecasting
a qualitative forecasting technique that bring experts together to discuss and develop a forecast
delphi method
a qualitative forecasting technique in which experts work individually to develop forecasts. The individual forecast are shared among the group, and then each participant is allowed to modify his or her forecast based on information from the other experts. This process is repeated until consensus is reached
live cycle analogy method
a qualitative forecasting technique that attempts to identify the time frames and demand levels for the introduction growth, maturity and decilne life cycle stages of a new product or service
build-up forecast
a qualitative forecasting technique in which individuals familiar with specific market segments estimate the demand within these segments. these individual forecasts are then added up to get an overall forecast.
time series
a series of observation arranged in chronological order
time series forecasting model
a quantitative forecasting model that uses a time series to develop forecasts. With a time series model, the chronology of the observations and their values are important in developing forecast.
randomness
in the context of forecasting, unpredictable movement from one time period to the next
trend
long-term movement up or down in a time series
seasonality
a repeated pattern of spikes or drops in a time series
moving average model
a time series forecasting model that derives a forecast by taking an average of recent demand values
smoothing model
another name for a moving average model. The name refer to the fact that using averages to generate forecasts results in forecasts that are less susceptible to random fluctuations in demand.
weighted moving average model
a form of the moving average model that allows the actual weights applied to past observations to differ.
exponential smoothing model
a special form of the moving average model in which the forecast for the next period is calculated as the weighted average of the current period's actual value and forecast.
adjusted exponential smoothing model
an expanded version of the exponential smoothing model that includes a trend adjustment factor
Linear regression
a statistical technique that expresses a forecast variable as a linear function of some independent variable. Linear regression can be used to develop both time series and causal forecasting models
4 steps to develop seasonal adjustments
1. for each of the demand values in the time series calculate the corresponding forecast, using the unadjusted forecast model
2. for each demand value, calculate demand/forecast. if the ratio is less than 1, then the forecast model over-forecasted; if it is greater than1, then the model under forecasted.
3. if the time series covers multiple years, take the average demand/forecast for corresponding months or quarter to derive the seasonal index. Otherwise use demand/forecast calculated in step 2 as the seasonal index.
4. multiply the unadjusted forecast by the seasonal index to get the seasonally adjusted forecast value
causal forecasting model
a class of quantitative forecasting models in which the forecast is modeled as a function of something other than time
multiple regression
a generalized form of linear regression that allows for more than one independent variable
collaborative planning forecasting, and replenishment CPFR
a set of business processes, backed up by information technology, in which supply chain partners agree to mutual business objective and measures, develop joint sales and operational plans, and collaborate to generate and update sales forecasts and replenishment plans.
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