Traditional learning curve models disregard the impact of break periods between consecutive repetitions. Since such breaks generate forgetting, when they do occur, actual performance is inferior to performance forecasted by a typical learning curve model. This study has two major objectives: (1) to test two hypotheses regarding learning and forgetting in the automated factory, proposed for a traditional industrial setting by Bailey (1989); (a) Forgetting is a function of the amount of learning prior to the interruption and the elapsed time of the interruption and (b) relearning rate is a function of the original learning rate. (2) to identify a proper forgetting model and estimate its parameters so that it may be compared to existing learning-forgetting models. The results of this study confirmed that Bailey's hypotheses are valid in a high tech manufacturing environment where computers are used for the control of machines, material handling systems and inspection equipment. Based on these hypotheses a power learning-forgetting model was found to be the preferred model to depicting the relationship between the break length and the degree of forgetting.
|Number of pages||13|
|Journal||The International journal of human factors in manufacturing|
|State||Published - 1993|