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Countering forgetting through training and deployment
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DOI：10.1016/S0925-5273(03)00084-7.png)
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Although worker flexibility has several advantages, it is costly to obtain and maintain given the productivity losses that arise from worker learning and forgetting effects. In this study, we review factors that influence worker forgetting in industrial settings, and analyze the degree to which existing mathematical models conform to observed human forgetting behavior. We find that the learn-forget curve model (LFCM) satisfies many characteristics of forgetting. In the context of worker flexibility, we use LFCM to understand the extent to which cross training and deployment become important in helping reduce forgetting effects. Finally, we enhance LFCM by augmenting it to incorporate the job similarity factor. Sensitivity analysis reveals that the importance of training and deployment policies is reduced as task similarity increases. (C) 2003 Elsevier Science B.V. All rights reserved.
Keyword:
learning
forgetting
DRC systems
cross training
transfer policy
job similarity
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