University:
Cameron University
Course:
MGMT 3613 | Operations Management
Academic year:

2024
Views:

330

Pages:

2
Author:

Kailyn Pham

MGMT 3613 | Operations Management Chapter 3 Key Terms Associative Model: Forecasting technique that uses explanatory variables to predict future demand. Bias: Persistent tendency for forecasts to be greater or less than the actual values of a time series. Centered Moving Average: A moving average positioned at the center of the data that were used to compute it. Control Chart: A visual tool for monitoring forecast errors. Correlation: A measure of the strength and direction of the relationship between two variables. Cycle: Wavelike variations lasting more than one year. Delphi Method: Managers and staff complete a series of questionnaires, each developed from the previous one, to achieve a consensus forecast. Error: Difference between the actual value and the value that was predicted for a given period. Exponential Smoothing: Weighted averaging method based on forecast plus a percentage of the forecast error. Forecast: Statement about the future value of a variable of interest. Irregular Variation: Caused by unusual circumstances not reflective of typical behavior. Judgmental Forecast: Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and exports. Least Squares Line: Minimizes the sum of the squared vertical deviations around the line. Linear Trend Equation: Ft = a + bt, used to develop forecasts when trend is present. Mean Absolute Deviation (MAD): The average absolute forecast error. Mean Absolute Percent Error (MAPE): The average of squared forecast errors. Mean Squared Error (MSE): The average absolute percent error. Moving Average: Technique that averages a number of recent actual values, updated as new values become available. Naïve Forecast: The forecast of any period equals the previous period’s actual value. Predictor Variables: Variables that can be used to predict values of the variable of interest. Random Variations: Residual variations after all other behaviors are accounted for. Regression: Technique for fitting a line to a set of points. Seasonal Relative: Percentage of average or trend. Seasonal Variations: Regular repeating movements in series values can be tied to recurring events. Seasonality: Short term regular variations related to the calendar or time of day. Time Series: A time-ordered sequence of observations taken at regular intervals. Time Series Forecasts: Forecasts that project patterns identified n recent time-series observations. Tracking Signal: The ratio of cumulative forecast error to the corresponding value of MAD, used to monitor a forecast. Trend: A long term upward or downward movement in data. Trend-Adjusted Exponential Smoothing: Variations of exponential smoothing used when a time series exhibits trend. Weighted Average: More recent values in a series are given more weight in computing a forecast.