Title :
Rough cut capacity planning in a learning environment
Author :
Smunt, Timothy L.
Author_Institution :
Babcock Graduate Sch. of Manage., Wake Forest Univ., Winston-Salem, NC, USA
fDate :
8/1/1996 12:00:00 AM
Abstract :
The area of capacity planning is receiving increased emphasis in the management of operations due to the financial benefits of efficiently utilizing capacity and to the importance of accurate capacity plans for use with material requirements planning (MRP) and other information-oriented planning systems. Most of the prior research in capacity planning has been limited to improving capacity management techniques that assume a constant level of productivity. But it has been shown in past empirical research that many firms exhibit productivity improvements, or learning, as more units are produced. These productivity improvements are usually associated with a learning process-human, technological, or organizational-and have been measured by logarithmic functions known as learning curves. When companies exhibit this learning process in the use of their capital or human resources, the capacity planning methodology used should consider the effects of future productivity improvements on capacity utilization. This paper presents an overview learning curve analysis (LCA) for rough-cut capacity planning and illustrates the effective use of learning curves for capacity planning through a comparison of traditional approaches with ones that incorporate the learning curve concept
Keywords :
human resource management; production; scheduling; capacity management techniques; capital resources; efficient capacity utilisation; financial benefits; human resources; information-oriented planning systems; learning curves; learning environment; logarithmic functions; material requirements planning; operation management; production management; production planning; production scheduling; productivity improvements; rough cut capacity planning; Aggregates; Capacity planning; Costs; Environmental management; Humans; Job shop scheduling; Machine learning; Materials requirements planning; Production; Productivity;
Journal_Title :
Engineering Management, IEEE Transactions on