Learning and forgetting industrial skills: an experimental model

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.