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50 Cards in this Set

  • Front
  • Back
Forecasting
Art and science of predicting future events
Future time Horizons
Short-range forecast, Medium range, long-range.
Short-range
Span up to 1 year, used for planning purchasing, job scheduling, workforce levels, job assisgnments, and production levels
Medium-range
Spans from 3 months to 3 years. Useful in sales planning, production planning and budgeting, cash budgeting, and analyzing operating plans
Long-range
3 years or more, planning new products, capital expenditures, facility location or expansion, R&D
Intermediate and long run forecasts
Deal with more comprehensive issues and support management decisions
Short Term forecasting
Employs different methodologies. (moving averages, exponential smoothing, and trend extrapolation)
Short-range forecasting
Tend to be more accurate than longer-range forecasts.
Product Life Cycles
Introduction, growth, maturity, decline
Type of forecasts
Economic, technological, demand
Strategic important of forecasting
human resources, capacity, supply-chain management.
Seven steps in forecasting
Determing the use of the forecast
Select the items to be forecasted
Determine the time horizon of the forecast
Select the forecasting models
Gather the data
Make the forecast
Validate and implement results
Realties
Forecast are seldom perfect, most techniques assume an uderlying stability in the system, product family and aggregated forecasts are more accurate than individual product forecasts.
Qualitative approach
Decisions maker's intuition, emotions, personal experiences, and value system in reaching a forecast
Quantitative approach
variety of mathematical models that rely on historical data.
Qualitative methods
Jury of executive opinion - pool opinions of high-level executives
Delphi Method
- panel of experts
Sales force composite
Based on salespersons' estimates of expected sales
Consumer market survey
Forecasting method that solicits input from customers
Quantitative Methods
Naive approach, moving averages, exponential smoothing, trend projection, linear regression.
Naive approach
assumes next period is the same as next
The forecasting time horizon that would typically be easiest to predict for would be the
short-range
A forecast that projects a company's sales is a(n)
demand forecast
Quantitative methods of forecasting include
exponential smoothing
The method that considers several variables that are related to the variable being predicted is
multiple regression
The forecasting model that is based upon salesperson's estimates of expected sales is
sales force composite
Decomposing a time series refers to breaking down past data into the components of
trends, cycles, seasonal and random variations
Realties
Forecast are seldom perfect, most techniques assume an uderlying stability in the system, product family and aggregated forecasts are more accurate than individual product forecasts.
Qualitative approach
Decisions maker's intuition, emotions, personal experiences, and value system in reaching a forecast
Quantitative approach
variety of mathematical models that rely on historical data.
Qualitative methods
Jury of executive opinion - pool opinions of high-level executives
Delphi Method
- panel of experts
Sales force composite
Based on salespersons' estimates of expected sales
Consumer market survey
Forecasting method that solicits input from customers
Quantitative Methods
Naive approach, moving averages, exponential smoothing, trend projection, linear regression.
Naive approach
assumes next period is the same as next
The forecasting time horizon that would typically be easiest to predict for would be the
short-range
A forecast that projects a company's sales is a(n)
demand forecast
Quantitative methods of forecasting include
exponential smoothing
The method that considers several variables that are related to the variable being predicted is
multiple regression
The forecasting model that is based upon salesperson's estimates of expected sales is
sales force composite
Decomposing a time series refers to breaking down past data into the components of
trends, cycles, seasonal and random variations
With regard to regression-based forecast, the standard error of the estimate gives a measure of
the variability around the regression line
When using exponential smoothing, the smoothing constant
is typically between .05 and .50 for most business applications
indicates the amount of weight placed on the most recent period
can be determined using MAD
Exponential smoothing is most similar to
the weighted moving average method of forecasting
Time series patterns that repeat themselves after a period of days or weeks are called
seasonality
Which of the following is not a time-series model?
linear regression