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An overview of various forecasting techniques used in operations management, including associative forecasting, time series forecasting, and qualitative forecasting. It covers the key characteristics and applications of these different forecasting approaches, as well as factors that can improve forecast accuracy. Topics such as multiple regression, exponential smoothing, the delphi method, and the use of business judgment in demand planning. It offers insights into how organizations can leverage different forecasting models and strategies to better predict future demand and make informed operational decisions.
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associative forecasting - correct answer ✔✔predicts forecast based on a related variable which is also called an independent variable. The variable for which the forecasting ids done is called the dependent variable Associative models - correct answer ✔✔- multiple regression
A vendor using advance ticket sales to forecast demand for stadium concessions for a professional football game would most likely use which of the following? - correct answer ✔✔Linear regression A tax accountant who calculates the total demand for her services from the past four years and divides by four to forecast demand for the next year is using which of the following? - correct answer ✔✔moving average Forecasting demand for a new restaurant concept would most likely rely on which of the following? - correct answer ✔✔Qualitative techniques Snow increases demand for snow shovels so a retail store manager watching the weather to forecast demand for snow shovels is applying which of the following? - correct answer ✔✔Associative forecasting A movie theater using demand from the past 28 days to forecast demand for the next seven days is applying which of the following? - correct answer ✔✔Time-series forecasting Forecasts can be made more accurate by: - correct answer ✔✔- using a shorter time horizon