介绍operationmanagement项目管理3.pptVIP

3-31 Linear Model Seems Reasonable A straight line is fitted to a set of sample points. 0 10 20 30 40 50 0 5 10 15 20 25 X Y 7 15 2 10 6 13 4 15 14 25 15 27 16 24 12 20 14 27 20 44 15 34 7 17 Computed relationship 3-32 Linear Regression Assumptions ? Variations around the line are random ? Deviations around the line normally distributed ? Predictions are being made only within the range of observed values ? For best results: ? Always plot the data to verify linearity ? Check for data being time-dependent ? Small correlation may imply that other variables are important 3-33 Forecast Accuracy ? Error: difference between actual value and predicted value ? Mean Absolute Deviation (MAD) ? Average absolute error ? Mean Squared Error (MSE) ? Average of squared error ? Mean Absolute Percent Error (MAPE) ? Average absolute percent error McGraw-Hill/Irwin Copyright ? 2010 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting 3-2 Learning Objectives ? List the elements of a good forecast. ? Outline the steps in the forecasting process. ? Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each. ? Compare and contrast qualitative and quantitative approaches to forecasting. 3-3 Learning Objectives ? Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems. ? Describe two measures of forecast accuracy. ? Describe two ways of evaluating and controlling forecasts. ? Identify the major factors to consider when choosing a forecasting technique. 3-4 FORECAST : ? A statement about the future value of a variable of interest such as demand. ? Forecasting is used to make informed decisions. ? Long-range ? Short-range 3-5 Forecasts ? Forecasts affect decisions and activities throughout an organization ? Accounting, finance ? Human resources ? Marketing ? MIS ? Operations ? Product/service design 3-6 Accounting Cost/profit estimates Finance Cas