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MGMT 3240 Chp 5 & 6

TermDefinition
Autocorrelation occurs when the value of one data point is highly correlated with the past values.
Example of Autocorrelation: when waiting in a long line, the time for the fifteenth person in line is highly correlated with (and guaranteed to be longer than) the time for the tenth person in line.
There are two basic types of forecasting: quantitative and qualitative.
Quantitative techniques rely on existing data for demand and use mathematical formulas of varying complexity to accommodate different types of demand.
There are two primary groups of quantitative methods. True
Time-series analysis utilizes past demand data to predict future demand by examining cyclical, trend, and seasonal influences.
Causal relationships identify a connection between two factors, one that precedes and causes changes in the second or effect factor, such as the effect of advertising on sales.
Qualitative forecasting is based on subjective factors, estimates, and opinions.
Qualitative methods are important for new products or when past demand data are lacking.
Two major elements of forecasting: time-series analysis and measurement of errors.
Time-series analysis is based on historical data and the assumption that past patterns Will continue in the future.
Time-series analysis goal is to identify the underlying patterns of demand and develop a model to predict these patterns in the future.
five basic time-series techniques: naive forecasts, moving averages, exponential smoothing, trend-adjusted exponential smoothing, and seasonal patterns.
A naive forecast uses the demand for the current period as the forecast for the next period.
The naive forecast is very simple and low cost to use.
Naive forecast works best when demand, trend, and seasonal patterns are stable and there is relatively little random variation.
The naive approach is the simplest of all the possible forecasting methods and works particularly well when there is autocorrelation.
Every series of demand figures includes at least two of the six components of demand: an average and random variation.
a naive forecast which is a moving average with one period
Time-series analysis a technique that utilizes past demand data to predict future demand by examining cyclical, trend, and seasonal influences
Causal relationships a technique that identifies a connection between two factors, one that precedes and causes changes in the second or effect factor
Qualitative forecasting a method of forecasting that is based on subjective factors, estimates, and opinions
Naive forecast a method of forecasting that uses the demand for the current period as the forecast for the next period.
Created by: kizito
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