Predicting performance times for long cycle time tasks

A long cycle time task is assumed to consist of a series of non-repetitive unique sub-tasks whose standard times average at about 1½minutes. ‘Forgetting’ is therefore a consequence of a specific sub-task reappearing in the next cycle after a whole cycle time of other activities is completed. Learning behavior of long cycle tasks is therefore predicted on the learning of its constituent sub-tasks. A method for predicting the learning curve parameters for the sub-tasks (the learning constant, and execution time of the first repetition) are proposed and tested. The extent of ‘forgetting’ is empirically determined as a function of the learning constant and interruption length. Finally, a model is developed for predicting execution times for long cycle tasks.