OM 300 1nd Edition Lecture 25 Outline of Last Lecture I Trend Projections II Least Squares Method III Seasonal Variations in Methods Outline of Current Lecture I Associate Forecasting II Monitoring and Controlling Forecasts III Adaptive Smoothing IV Focus Forecasting V Forecasting in the Service Sector Current Lecture Associative Forecasting Used when changes in one or more independent variables can be used to predict the changes in the dependent variable Most common technique is linear regression analysis We apply this technique just as we did in the time series example Monitoring and Controlling Forecasts Tracking Signal Measures how well the forecast is predicting actual values Ration of cumulative forecast errors to mean absolute deviation MAD o Good tracking signal has low values o If forecasts are continually high or low the forecast has a bias error These notes represent a detailed interpretation of the professor s lecture GradeBuddy is best used as a supplement to your own notes not as a substitute Adaptive Smoothing It s possible to use the computer to continually monitor forecast error and adjust the values of the a and coefficients used in exponential smoothing to continually minimize forecast error This technique is called adaptive smoothing Focus Forecasting Developed at American Hardware Supply based on two principles o Sophisticated forecasting models are not always better than simple ones o There is no single technique that should be used for all products or services Uses historical data to test multiple forecasting models for individual items Forecasting model with the lowest error used to forecast the next demand Forecasting in the Service Sector Presents unusual challenges o Special need for short term records o Needs differ greatly as function of industry and product o Holidays and other calendar events o Unusual events
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