Quality insight: exponential decay of quality learning curves during COVID-19 lockdown

Abstract

This paper is presented as a quality-insight column designed to spark new research interest in computational views of how rapidly a learning curve declines during a period of prolonged interruption. Specifically, the paper considers the case of the worldwide COVID-19 lockdown that afflicted business, industry, academia, and government. As a result of being barred from practicing their respective functions, workers are prevented from the normal positive effects of being on a learning curve. Instead of performance improvement due to learning curves, there is performance degradation due to the lockdown. Although not enough live data is available yet for a direct modelling, the paper presents a postulated analytical framework that researchers can use later on for empirical modelling of the adverse impacts of the lockdown on learning curves. A decline in learning can translate to a decline in quality of work and quality of products. Mathematical methods suggested in the paper include exponential decay, hyperbolic decline, and half-life learning curves.