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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Results 1-3 of 54
Page 70
... model that describes the past . The data are then used to estimate the parameters of the model . Finally , the model ... linear model with slope ô Ѣ ( 3.2 ) ( 3.3 ) Ŷ ( t ) = â + bt + êt2 : quadratic model with slope b and change of ...
... model that describes the past . The data are then used to estimate the parameters of the model . Finally , the model ... linear model with slope ô Ѣ ( 3.2 ) ( 3.3 ) Ŷ ( t ) = â + bt + êt2 : quadratic model with slope b and change of ...
Page 100
... linear model would be expected to be better than a constant model , just because of the apparent trend . Results from the PREDICTS runs are given in Table 3.15 . The MSE and MAP values are computed only in PREDICTS for a particular model's ...
... linear model would be expected to be better than a constant model , just because of the apparent trend . Results from the PREDICTS runs are given in Table 3.15 . The MSE and MAP values are computed only in PREDICTS for a particular model's ...
Page 113
... model Ý ( t ) = a ( 10 ) bt Using an appropriate linear transform , derive equations to allow the determination of â and b through linear regression . 11. Consider the following data : t 1 2 3 4 5 6 Y ( t ) 8.0 12.0 20.0 32.0 48.0 80.0 ...
... model Ý ( t ) = a ( 10 ) bt Using an appropriate linear transform , derive equations to allow the determination of â and b through linear regression . 11. Consider the following data : t 1 2 3 4 5 6 Y ( t ) 8.0 12.0 20.0 32.0 48.0 80.0 ...
Contents
THE ROLE OF PRODUCTION CONTROL | 1 |
INFORMATION FLOW | 18 |
FORECASTING | 59 |
Copyright | |
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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