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
Results 1-3 of 92
Page 147
... quantity between families of products . The second step is an algorithm to divide a family's production quantity between items in the family . This procedure is programmed as DIS.BAS on the available diskette . Before going into the ...
... quantity between families of products . The second step is an algorithm to divide a family's production quantity between items in the family . This procedure is programmed as DIS.BAS on the available diskette . Before going into the ...
Page 206
... quantity should make intuitive sense . If the quantity is constant over T , interest charges should certainly be based on that quantity . But if the quantity decreases uniformly from Q to zero , interest charges initially would be based ...
... quantity should make intuitive sense . If the quantity is constant over T , interest charges should certainly be based on that quantity . But if the quantity decreases uniformly from Q to zero , interest charges initially would be based ...
Page 230
... quantity ordered is [ Q , − ( Q – ss ) ] . One problem now is that if Q is close to Q ,, when demand is low , small quantities will be ordered . Also , if demand is high , possible runout could be realized if Q is very low . Policy ( t ...
... quantity ordered is [ Q , − ( Q – ss ) ] . One problem now is that if Q is close to Q ,, when demand is low , small quantities will be ordered . Also , if demand is high , possible runout could be realized if Q is very low . Policy ( t ...
Other editions - View all
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 Industrial Engineering input inventory control inventory costs inventory item inventory level KANBAN lead-time 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 quadratic RECPT regression regression analysis resource safety stock sequence shift shown in Figure solution step storage Tandem Computers technique total cost units vendor week