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chapter 4 Forecasting
Terms in this set (44)
the art and science of predicting future events.
a forecast is usually classified as
future time horizon
what are the 3 time horizon categories?
1. Short-range forecast.
2.Medium- range forecast.
3.Long- range forecast.
* this forecast has a time span up to a year but usually is less than 3 months.
* it is used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels.
*this forecast generally spans from 3 months to 3 years.
* it is used for sales planning, production planning, and budgeting, cash budgeting, and analysis of various operating plans.
*this forecast generally is 3 years or more in time span.
*it is used in planning for new products, capital expenditures, facility location or expansion, and research and development.
Medium and long forecast are different from short range forecast by 3 features?
1. Intermediate and long forecast deal with more comprehensive issues supporting management decisions.
2.Short term forecasting usually employs different methodologies than longer term forecasting.
3.Short term forecast tend to be more accurate than longer range forecast.
what are the 3 types of forecast?
1. economic forecast
2. Technological forecast
3. Demand forecast
Planning indicators that are valuable in helping organizations prepare medium- to long range forecast.
It addresses the business cycle by prediction inflation rates, money supplies, housing starts, and other planning indicators.
Long term forecast concerned with the rates of technological progress.
are concerned with rates of technological progress, which can result in the birth of new products.
Projections of company's sales for each time period in the planing horizon.
they are projecting of demand for a company's product or services,demand driven forecasts, which they made use P.O.S. data and tracking customer desires.
Good forecast are of critical importance because
the forecast is the only estimate of demand until actual demand comes known.
what are the 3 activities that impact the product demand forecast
1. supply chain management.
2. human resources.
Supply chain management
Good supplier relations and the ensuing advantages in product innovations, cost, and speed to market depend on accurate forecasts.
Example: Apple, Toyota, Walmart( Collaborating, forecasting,and replenishment)
Hiring, training, and laying off workers all depend on anticipated demand.
Example: Louisiana chemical firm.
When capacity is inadequate, the resulting shortages can lead to loss of customers and market share.
Example:Nabisco, Nintendo with wii, amazon with kindle.
the 7 steps in the forecast system
1. Determine the use of forecast.
2. Select the items to be forecasted.
3.Determine the time horizon forecast.
4. Select the forecasting model.
5.Gather data needed ti make the forecast.
6. Make the forecast.
7. Validate and implement the results.
what are the 2 forecast approaches
quantitative forecasts and qualitative forecast.
forecasts that employ mathematical modeling to forecast demand.
Forecasts that incorporate such factors as the decisions maker's intuition, emotions, personal experiences, and value system.
what are the 4 qualitative techniques
1. jury of execution opinion.
3. Sales force composite.
4. market survey.
Jury of execution opinion
a forecasting technique that uses the opinion of a small group of high-level managers to form a group estimate of demand.
A forecasting technique using a group process that allows experts to make forecasts.
sales force composite
A forecasting technique based on salespersons' estimates of expected sales.
A forecasting method that solicits input from customers or potential customer regarding future purchasing plans.
What are the 5 quantitative method
1.Naive approach. Time series model
2.Moving averages. Time series model
3. Exponential smoothing. Time series model
4.Trend projection. Associative model
5.Linear regression Associative model
a forecasting technique that uses a series of past data points to make a forecast.
time series has 4 components
4. random variations.
A forecasting technique which assumes that demand in the next period is = to demand in the most recent period.
a forecasting method that uses an average of the n most recent periods of data to forecast the next period.
What are the 3 problems that moving averages have
1. increasing the size of n does smooth out fluctuations better, but it makes the method less sensitive to change in the data.
2. they cannot pick up trend very well.
3. moving averages require extensive records of past data.
a weighted moving average forecasting technique in which data points are weighted by an exponential function.
the weighted factor used in an exponential smoothing forecast, a number greater than or equal to 0 and less than or equal to 1.
What are the 3 measures of forecast accuracy
1. mean absolute deviation (MAD)
2.mean squared error (MSE)
3. mean absolute percent error ( MAPE)
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.
Regular upward or downward movements in a time series that tie to recurring events.
steps to follow for a company that has seasons of 1 month
1. find the average historical demand each season.
2. compute the average demand over all months and dividing the number of seasons.
3. compute a sesonal index for each season.
4. estimate next year total annual demand.
5. Divide this estimate of total annual demand by the number of seasons,then multiply it by the seasonal index for each month.
Linear- regression analysis
A straight-line mathematical model to describe the functional relationships between independent and dependent variables.
Coefficient of correlation
a measure of the strength of the relationship between two variables.
An associative forecasting method with more than one independent variable.
a measurement of how well a forecast is predicting actual values.
ratio of the cumulative error to MAD.
A forecast that is consistently higher or lower than actual events of a time series.
an approach to exponential smoothing forecasting in which the smoothing constant is automatically changed to keep errors to a minimum.
Forecasting that tries a variety of computer model and selects the best one for a particular application.
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