Chapter 3 definitions

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the art and science of predicting future events
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forecasting
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planning indicators that are valuable in helping organizations prepare medium- to long-range forecasts. address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators
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economic forecasts
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are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment. long term forecasts concerned with the rates of technological progress
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technological forecasts
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projections of a company’s sales for each time period in the planning horizon
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demand forecasts
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forecasts that employ mathmatical modeling to forecast demand
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quantitiative forecasts
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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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qualitative forecasts
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a forecasting technique that uses the opinion of a small group of high-level managers to form a group estimate of demand
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jury of executive opinion
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a forecasting technique using a group process that allows experts to make forecasts
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delphi method
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a forecasting technique based on salespersons’ estimates of expected sales
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sales force composite
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a forecasting method that solicits input from customers or potential customers regarding future purchasing plans
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market survey
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market survey, sales force composite, delphi method, jury of executive opinion
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qualitative methods
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naive approach, moving averages, exponential smoothing
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time-series models (quantitiative methods,)
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trend progression, linear progression
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associative models (quantitative methods)
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predict on the assumption that the future is a function of the past. Uses a series of past data points to make a forecast. lawn mower makes predictions based on past sales for lawn mowers
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time-series
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such as linear regression, incorporate the variables or factors that might influence the quantity being forecast. An associate model for lawn mower sales might use factors such as new housing starts, advertising budge, and competitors’ prices.
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associative models
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the gradual upward or downward movement of the data over time. Changes in income, population, age distribution, or cultural views may account for movement in trend.
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trend
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is a data pattern that repeats itself after a period of days, weeks months, or quarters. There are 6 typrs. Barbers on weekends
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seasonality
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patterns in the data that occur every several years. They are usually tied into the business cycle and are of major importance in short-term business analysis and planning. Predicting business cycles is difficult because they may be affected by political events or by international turmoil.
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cycles
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are “blips” in the data caused by chance and unusual situations. They follow no discernible pattern, so they cannot be predicted,
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random variations
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trend, seasonality, cycle, random variation
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four components of time-series
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the simplest way to forecast is to assume that demand in the next period will be equal to demand in the most recent period.
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naive approach
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a forecasting method that uses an average of the n most recent periods of data to forecast the next period MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time
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moving averages
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Used when some trend might be present Older data usually less important Weights based on experience and intuition
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weighted moving averages
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a weighted-moving average forecasting technique in which data points are weighted by an exponential function
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exponential smoothing
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the weighing factor used in an exponential smoothing forecast, a number greater than or equal to 0 and less than or equal to 1.
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smoothing constant
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a measure of the overall forecast error for a model
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Mean absolute deviation (MAD)
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the average of the squared differences between the forecasted and observed values
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Mean squared error (MSE)
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the average of the absolute differences between the forecast and actual values expressed as a percent of actual values
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Mean absolute percent error (MAPE)

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