PREDICTING PERFORMANCE TIMES FOR LONG CYCLE TIME TASKS

A long cycle time task is assumed to consist of a series of non-repeti tive unique sub-tasks whose standard times average at about 11/2 minut es. 'Forgetting' is therefore a consequence of a specific sub-task rea ppearing in the next cycle after a whole cycle time of other activitie s is completed. Learning behavior of long cycle tasks is therefore pre dicted on the learning of its constituent sub-tasks. A method for pred icting the learning curve parameters for the sub-tasks (the learning c onstant, and execution time of the first repetition) are proposed and tested. The extent of 'forgetting' is empirically determined as a func tion of the learning constant and interruption length. Finally, a mode l is developed for predicting execution times for long cycle tasks.