Integrated Production, Control Systems: Management, Analysis, and DesignFocuses on the quantitative approaches necessary to computer-integrated manufacturing systems, and integrates major topics covering all phases of the production control cycle: production information processing and flow, production planning, forecasting, material requirements planning and monetary control, and scheduling. This new edition features a compendium set of 11 user-friendly computer programs for the IBM PC that enhance the teaching power of the text, allowing readers to solve real-life problems. Among programs included are growth forecasting, aggregate planning, material requirements planning, lot sizing and inventory control, and limited-resource scheduling. The chapters on scheduling give particularly thorough coverage on this difficult subject. Solutions are clearly presented, with many examples and exercises included in the text. |
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Page 99
... error analysis ( see Section 3.8 ) and will also determine , through a search routine , the optimum alpha . The user may override any of the search approaches to allow any growth model to be fit ... ERROR ANALYSIS 99 Forecast Error Analysis.
... error analysis ( see Section 3.8 ) and will also determine , through a search routine , the optimum alpha . The user may override any of the search approaches to allow any growth model to be fit ... ERROR ANALYSIS 99 Forecast Error Analysis.
Page 100
... error measure . If a lead time of 4 is being used , the error at time period 20 is formed by subtracting the prediction made at time period 16 from Y ( 20 ) . So , if 20 values of data are available with a lead time of 4 , the error ...
... error measure . If a lead time of 4 is being used , the error at time period 20 is formed by subtracting the prediction made at time period 16 from Y ( 20 ) . So , if 20 values of data are available with a lead time of 4 , the error ...
Page 110
... error measure for the data used to build the model as an indicator of forecasting accuracy . Note also that the error in year 4 is largest in the early and late months , suggesting the inappropriateness of a linear model . To address ...
... error measure for the data used to build the model as an indicator of forecasting accuracy . Note also that the error in year 4 is largest in the early and late months , suggesting the inappropriateness of a linear model . To address ...
Contents
THE ROLE OF PRODUCTION CONTROL | 1 |
INFORMATION FLOW | 18 |
FORECASTING | 59 |
Copyright | |
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Common terms and phrases
ACTIM activity aggregate planning algorithm allow analysis approach assembly assigned assumed BASICA batch BEGIN INVENTORY Box-Jenkins calculate carrying costs Chapter completion component considered constraints critical path cycle Data Set determine due date Equation error example problem exponential smoothing factors follows forecasted demand function function key Gantt chart given in Figure GROSS REQUIREMENTS historical data Industrial Engineering input inventory costs inventory item inventory level KANBAN Line Balancing line-of-balance linear linear model machine makespan manufacturing master schedule MATERIAL REQUIREMENTS PLANNING maximum mean tardiness minimize minimum needed node operation optimal order costs order quantity output overtime parameters percent period personal computer procedure processor production control purchase quadratic RECPT regression regression analysis resource safety stock sequence shift shown in Figure solution step storage Tandem Computers technique total cost units values vendor week