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54 Cards in this Set
- Front
- Back
forecasting
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the art and science of predicting future events
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economic forecasts
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planning indicators that are valuable in helping organizations prepare medium-to long-range forecasts
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technological forecasts
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long-term forecasts concerned with the rates of technological progress
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demand forecasts
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projection of a company's sales for each time period in the planning horizon
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the forecast is the only estimate of demand until actual demand becomes known.
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forecasts of demand drive decisions in many areas, including: human resources, capacity, supply-chain management.
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forecasting follows seven basic steps:
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1 determine the use of the forecast 2 select the items to be forecasted 3 determine the time horizon of the forecast 4 select the forecasting model(s) 5 gather the data needed to make the forecast 6 make the forecast 7 validate and implement the results
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forecasting approaches are:
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quantitative forecasts, qualitative forecasts, jury of executive opinion, delphi method, sales force composite, consumer market survey, time series
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quantitative forecasts
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forecasts that employ mathematical modeling to forecast demand
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qualitative forecast
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forecasts that incorporate such factors as the decision maker's intuition, emotions, personal experiences, and value system
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jury of executive opinion
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takes the opinion of a small group of high-level managers and results in a group estimate of demand
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delphi method
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uses an interactive group process that allows experts to make forecasts
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sales force composite
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based on salesperson' estimates of expected sales
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consumer market survey
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solicits input from customers or potential customers regarding future purchasing plans
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time series
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uses a series of past data points to make a forecast
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a time series has four components
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1 trend 2 seasonality 3 cycles 4 random variations
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naive approach
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simplest time series approach that assumes that demand in the next period is equal to demand in the most recent period
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moving averages
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time-series forecasting that uses an average of the most recent periods (n) of data to forecast the next period
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moving average=
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sum(demand in previous periods (n))
---------------------------------------------------- n (periods) |
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weighted moving average
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sum(weight for period n)(demand in period n)
-------------------------------------------------- sum(weights) |
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exponential smoothing
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time-series forecasting, a weighted-moving-average forecasting technique which data points are weighted by an exponential function
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smoothing constant
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time-series forecasting approach, weighting factor, alpha, used in an exponential smoothing forecast (alpha is a number between 0 and 1)
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exponential smoothing formula
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Ft = Ft-1 + alpha(At-1 - Ft-1)
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F1
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new forecast
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Ft-1
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previous period's forecast
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alpha
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smoothing (or weighting) constant (a number between 0 and 1)
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At-1
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previous period's actual demand
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mean absolute deviation (MAD)
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time-series forecasting, a measure of the overall forecast error for a model
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MAD formula
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Sum[Actual - Forecast]
------------------------------------ n |
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Mean squared error (MSE)
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time-series forecasting, the average of the squared differences between the forecast and observed values
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MSE formula
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Sum(Forecast errors)^2
----------------------------------- n |
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Mean absolute percent error (MAPE)
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time-series forecasting, the average of the absolute differences between the forecast and actual values, expressed as a percentage of actual values
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Mean absolute percent error formula
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Sum100[Actualt - Forecastt]/Actualt
------------------------------------------------------ n |
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exponential smoothing with trend adjustment
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forecast including trend (FITt) = Exponentially smoothed forecast (Ft) + Exponentially smoothed trend (Tt)
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Trend projection
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a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts
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trend projection and regression analysis
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yhat = a + bx,
where b = Sumxy - n(xbar)(ybar) ------------------------------- Sumx^2 - n(xba)r^2 where a = ybar - b(xbar) |
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yhat
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computed value of the variable to be predicted (called the dependent variable)
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a
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y-axis intercept
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b
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slope of the regression line (or the rate of change in y for given changes in x)
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x
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the independent variable (which in this case is time)
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seasonal variations
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regular upward or downward movements in a time series that tie to recurring events
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cycles
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patterns in the data that occur every several years
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Unlike time-series forecasting, associative forecasting models usually....
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consider several variables that are related to the quantity being predicted. Once these variables have been found, a statistical model is built and used to forecast the item of interest. More powerful than a time-series method that use only the historical values for the forecasted value
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linear-regression analysis
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associative forecasting method, a straight-line mathematical model to describe the functional relationships between independent and dependent variables
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standard error of the estimate
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associative forecasting method, a measure of variability around the regression line
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coefficient of correlation
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associative forecasting method, a measure of the strength of the relationship between two variables
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coefficient of determination
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associative forecasting method, a measure of the amount of variation in the dependent variable about its mean that is explained by the regression equation
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multiple regression
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associative forecasting method with > 1 independent variable
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Multiple regression forecast
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yhat = a + b1x1 + b2x2
*yhat is the dependent variables, sales *a is a constant, the y intercept *x1 and x2 are the values of the two independent variables *b1 and b2 are the coefficients for the two independent variables |
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tracking signal
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monitoring and controlling forecast, a measurement of how well the forecast is predicting actual values
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tracking signal formula
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Sum(Actual demand in period i - Forecast demand in period i)
-------------------------------------------------- MAD |
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Bias
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monitoring and controlling forecast that is consistently higher or lower than actual values of a time series
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adaptive smoothing
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monitoring and controlling forecast approach to exponential smoothing forecasting in which the smoothing constant is automatically changed to keep errors to a minimum
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focus forecasting
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monitoring and controlling forecasting that tries a variety of computer models and selects the best one for a particular application
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service-sector forecasting may require good short-term demand records, even per 15 minute intervals.
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demand during holidays or specific weather events may also need to be tracked.
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