Evaluating economic impacts of automation using big data approaches

As automation is increasingly driven by advanced technological integration, quantitatively evaluating its economic impacts becomes crucial. This paper studies the effects of automation on three economic outcomes: transactions, sales, and costs. First, we use big data to distinguish transaction distribution patterns across various temporal segments. We examine the relationship between automation and transaction dynamics through probabilistic modeling. Empirical evidence suggests a positive relationship between automation and customer traffic volume. Second, we illustrate how automation can lead to higher forecast accuracy in sales. Third, we study the impact of automation on costs. The results suggest that while labor costs increase due to retraining and longer hours, a potential reduction in turnover and waste costs can offset these rises.