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

Authors: Adedeji Badiru

Addresses: Air Force Institute of Technology, Dayton, OH 45433, USA

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.

Keywords: learning curves; learn-forget curves; performance disruption; exponential decay; hyperbolic decline; COVID-19 pandemic; theory of expected performance.

DOI: 10.1504/IJQET.2020.110328

International Journal of Quality Engineering and Technology, 2020 Vol.8 No.1, pp.106 - 117

Received: 13 May 2020
Accepted: 30 Jun 2020
Published online: 14 Oct 2020 *

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