Abstract
Production and operations management (POM) uses learning curve (LC) models to determine the length of training sessions for new workers and predicting future task performance. Empirically validated LC parameters provide managers with quantitative information on the effects of the presumed factors behind the learning process. Previous studies considered LC to compose of cognitive and motor curves. Another widely acknowledged but only recently parameterized phenomenon in the POM field is interference, which assumes some loss of information or experience could occur over a learning session. This paper takes a logical step in this line of research by developing an interference-adjusted power LC model, a composite of cognitive and motor elements. This paper accounts for the decay of cognitive and motor memory traces from repetitions to measure the residual (interference-adjusted) experience and capture these phenomena. Three variants of the model are developed that assume power and exponential decay functions and an approximate version of the exponential one. Assembly data representing various forms of an individual learning profile have been used to test the fits of the developed models. In addition to those models, four potential models from the literature were selected for comparison purposes. The results show that the approximate model fits very well exponential learning profile. The findings highlight the confluence of the three phenomena in learning, component (cognitive/motor) learning, interference, and plateauing.
Highlights
Learning curve (LC) models are practical and quantitative industrial engineering tools to measure performance changes as a function of experience
The above results suggest that the plateauing effect is related to motor learning, which is slower than cognitive learning, partly due to motor interference (A-DP-IALC)
This paper presents a modification of the WLC by aggregating cognitive and motor components and considering interference when learning
Summary
Learning curve (LC) models are practical and quantitative industrial engineering tools to measure performance changes as a function of experience. None of the studies in the literature provide models that integrate both components (cognitive/motor) learning and interference (decay) In this regard, Jaber and Kher [32] and Jaber and Glock [14] modified the dual-phase (cognitive/motor) LC of Dar-El et al [13] to show that forgetting is still valid. The results show that the approximate model performed well for individuals with exponential learning profiles, who are likely to struggle at the early learning phase but improve quickly, which is expected for novices at complex assembly. They highlight the confluence of three learning phenomena: components (cognitive/motor) learning, interference, and plateauing.
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