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. |
From inside the book
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Page 69
... parameters of the forecasting model is regression analysis . Implementation is typically accomplished using a computerized statistical software package . Many periods of historical data are gathered for the indicator and forecast ...
... parameters of the forecasting model is regression analysis . Implementation is typically accomplished using a computerized statistical software package . Many periods of historical data are gathered for the indicator and forecast ...
Page 102
... parameters of fit , constant ε , random error component . This model is called autoregressive ( AR ) because the equation of fit contains prior values of the data set that is being predicted . If there are p values of o , to be ...
... parameters of fit , constant ε , random error component . This model is called autoregressive ( AR ) because the equation of fit contains prior values of the data set that is being predicted . If there are p values of o , to be ...
Page 110
... parameters was better , the model was humorously worse . Consider now a linear model that uses data from years 1 , 2 , and 3 to determine the parameters . One could logically argue that using more data to determine the parameters of the ...
... parameters was better , the model was humorously worse . Consider now a linear model that uses data from years 1 , 2 , and 3 to determine the parameters . One could logically argue that using more data to determine the parameters of the ...
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