tailieunhanh - Lecture Principle of inventory and material management - Lecture 18

Lecture 18 - Forecasting (Continued). The contents of this chapter include all of the following: Compute three measures of forecast accuracy, develop seasonal indexes, conduct a regression and correlation analysis, use a tracking signal. | Lecture 18 Forecasting (Continued) Books Introduction to Materials Management, Sixth Edition, J. R. Tony Arnold, ., CFPIM, CIRM, Fleming College, Emeritus, Stephen N. Chapman, ., CFPIM, North Carolina State University, Lloyd M. Clive, ., CFPIM, Fleming College Operations Management for Competitive Advantage, 11th Edition, by Chase, Jacobs, and Aquilano, 2005, .: McGraw-Hill/Irwin. Operations Management, 11/E, Jay Heizer, Texas Lutheran University, Barry Render, Graduate School of Business, Rollins College, Prentice Hall Objectives When you complete this chapter you should be able to : Compute three measures of forecast accuracy Develop seasonal indexes Conduct a regression and correlation analysis Use a tracking signal 2 Common Measures of Error Mean Absolute Deviation (MAD) MAD = ∑ |Actual - Forecast| n Mean Squared Error (MSE) MSE = ∑ (Forecast Errors)2 n Common Measures of Error Mean Absolute Percent Error (MAPE) MAPE = ∑100|Actuali - Forecasti|/Actuali n n i = 1 . | Lecture 18 Forecasting (Continued) Books Introduction to Materials Management, Sixth Edition, J. R. Tony Arnold, ., CFPIM, CIRM, Fleming College, Emeritus, Stephen N. Chapman, ., CFPIM, North Carolina State University, Lloyd M. Clive, ., CFPIM, Fleming College Operations Management for Competitive Advantage, 11th Edition, by Chase, Jacobs, and Aquilano, 2005, .: McGraw-Hill/Irwin. Operations Management, 11/E, Jay Heizer, Texas Lutheran University, Barry Render, Graduate School of Business, Rollins College, Prentice Hall Objectives When you complete this chapter you should be able to : Compute three measures of forecast accuracy Develop seasonal indexes Conduct a regression and correlation analysis Use a tracking signal 2 Common Measures of Error Mean Absolute Deviation (MAD) MAD = ∑ |Actual - Forecast| n Mean Squared Error (MSE) MSE = ∑ (Forecast Errors)2 n Common Measures of Error Mean Absolute Percent Error (MAPE) MAPE = ∑100|Actuali - Forecasti|/Actuali n n i = 1 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded a = .10 a = .10 a = .50 a = .50 1 180 175 175 2 168 3 159 4 175 5 190 6 205 7 180 8 182 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded a = .10 a = .10 a = .50 a = .50 1 180 175 175 2 168 3 159 4 175 5 190 6 205 7 180 8 182 MAD = ∑ |deviations| n = = For a = .10 = = For a = .50 Comparison of Forecast Error Rounded Absolute .