Analysis of the Impact of Automation on Operational Efficiency Indicators
DOI:
https://doi.org/10.70577/asce.v5i2.800Keywords:
Automation, Artificial intelligence, Operational efficiency, Industrial productivity, Process optimization, Performance indicatorsAbstract
In recent years, the industrial sector has undergone a rapid transformation driven by automation and the adoption of artificial intelligence (AI), technologies that are reshaping how production processes are executed and managed. However, many organizations still face challenges in effectively integrating these tools, limiting their ability to improve operational efficiency and remain competitive in an increasingly demanding environment.
In this context, this study analyzes the impact of automation and artificial intelligence on operational efficiency across different industrial processes. A mixed-method approach with a quantitative emphasis was applied, evaluating key indicators such as productivity, cycle time, operational costs, and error rates, as well as their relationship with Overall Equipment Effectiveness (OEE).
The results show significant improvements in the analyzed processes, including a 76% reduction in errors, a 50% increase in productivity, and a 60% decrease in cycle time. These findings suggest that the implementation of AI-based technologies not only enhances operational performance but also represents a crucial step toward the transition from traditional industrial models to more intelligent and efficient systems.
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Copyright (c) 2026 William Ricardo Navas Espin, Dennis Holger Zambrano Silva, Freddy Steve Pincay Bohorquez, Annabelle Sally Lizarzaburu Mora, Mixi Joselyne Segura Torres
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