The use of the number of units produced, as the only independent variable to monitor the progress of a task, has raised important questions about the effectiveness of learning models. The traditional learning curve, initially developed for airplane production, proved to be impractical for many other engineering processes since it implies that all units produced are of acceptable quality. Seeking a link between learning curve tools and quality control, many authors attempted to overcome these limitations and quantify learning through the quality performance of a production process.
Fine (1986) was one of the first authors to link previous disjoint literature of learning curves and quality, by proposing formulations of quality-based learning models. Fine, opposed to the traditional notion that improving quality is always costly and argues that a firm choosing to produce higher quality products would learn faster and therefore have lower production costs, due to a steeper learning curve. In his work a model based on quality-weighted volume replaces the conventional volume-based learning curve, based on the traditional economic conformance level model. Fine (1988) also proposed an extension to the classic operations research model for optimal inspection policy thought the lens of quality improvement and learning. Additionally, Fine and Porteus (1989) studied a stochastic quality improvement model, with investments over time, resulting in random amount of process improvement. In the same line of research Marcellus and Dada (1991) and Dada and Marcellus (1994) modeled process improvement through dynamic quality-based learning models considering that each defective part provides a learning opportunity. Investment decisions in resourc...
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