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Operations Management
Ravi Behara
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- English
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eBook - ePub
Operations Management
Ravi Behara
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About This Book
Quintessential reference to business operations filled with key terms, equations, graphs, processes, models, and more. A perfect tool for any student of business, working professional, or business owner. The business knowledge compressed into six pages can be found nowhere else for this price.
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Topic
BetriebswirtschaftSubtopic
Business allgemeinForecasting
Forecasting involves predicting future events that are then used for planning purposes in business. They typically involve forecasting customer demand for a companyâs products. Forecasting methods can be both quantitative and qualitative.
Qualitative Forecasting Methods - Market research: Systematically determining customer interest in the companyâs products and services through surveys
- Sales force estimates: Estimates of future demand made periodically by the companyâs sales force
- Executive opinion: Summary forecast based on the opinion, experience, and knowledge of one or more executives or managers
- Expert judgment: Forecast based on judgment of one or more subject matter experts
- Delphi method: Forecasts developed through consensus of a panel of experts who usually do not know each other
- Two main models include causal models (linear regression) and time series models, which include the following methods:
- Smoothing methods (simple moving average, weighted moving average, and exponential smoothing)
- Trend projection method
- A pattern formed by repeated observations of demand for a product or service in the order in which they occurred is a time series. Basic patterns of a time series are:
- Horizontal: Fluctuation of demand around a constant mean
- Trend: Gradual increase or decrease in the mean of the time series over a longer period of time
- Seasonal: A repeated pattern of demand increases and decreases that depend on time of day, week, month, or season
- Cyclical: Periodic increases and decreases (around trend) of the time series over longer periods of time (years or decades)
- Random: Irregular and unforecastable variations in demand
- Accuracy is an important part of any forecast. Forecast error Et = At â Ft, where At is actual demand or value and Ft is the forecasted demand or value in time period t.
- Methods to measure forecast error are:
- Mean squared error (MSE): The average of the squared forecast errors for the historical data is calculated, and the forecasting model that minimizes this mean squared error is then selected for forecasting demand. MSE = [ÎŁ(At â Ft)2]/n
- Mean absolute deviation (MAD): The mean of the absolute values of all forecast errors is calculated, and the forecasting model that minimizes this measure is then selected for forecasting demand. MAD = [ÎŁ|(At â Ft)|]/n
- Mean absolute percentage error (MAPE): The mean of the absolute percentage values of all forecast errors is calculated as a percentage, and the forecasting model that minimizes this measure is then selected for forecasting demand....