Estimating productivity gains in digital automation
- URL: http://arxiv.org/abs/2210.01252v1
- Date: Mon, 3 Oct 2022 22:11:42 GMT
- Title: Estimating productivity gains in digital automation
- Authors: Mauricio Jacobo-Romero, Danilo S. Carvalho and Andr\'e Freitas
- Abstract summary: This paper proposes a novel productivity estimation model to evaluate the effects of adopting Artificial Intelligence (AI) components in a production chain.
We provide (i) theoretical and empirical evidence to explain Solow's dichotomy; (ii) a data-driven model to estimate and asses productivity variations; (iii) a methodology underpinned on process mining datasets to determine the business process, BP, and productivity.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper proposes a novel productivity estimation model to evaluate the
effects of adopting Artificial Intelligence (AI) components in a production
chain. Our model provides evidence to address the "AI's" Solow's Paradox. We
provide (i) theoretical and empirical evidence to explain Solow's dichotomy;
(ii) a data-driven model to estimate and asses productivity variations; (iii) a
methodology underpinned on process mining datasets to determine the business
process, BP, and productivity; (iv) a set of computer simulation parameters;
(v) and empirical analysis on labour-distribution. These provide data on why we
consider AI Solow's paradox a consequence of metric mismeasurement.
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