🤖 AI Summary
This study addresses the inefficiency and error-proneness of business processes in small-scale enterprises stemming from limited in-house development expertise. To mitigate this, the authors designed and deployed a representative lead-processing workflow on the n8n low-code automation platform, integrating automated data storage, email confirmation, and real-time notifications. A controlled experiment conducted in a real-world business setting was employed to quantitatively evaluate the performance of n8n-based automation against manual execution—the first such empirical assessment reported in the literature. The results demonstrate that automation reduced the average execution time from 185.35 seconds to 1.23 seconds (a ~151-fold speedup) and eliminated errors entirely, decreasing the error rate from 5% to 0%. These findings underscore the substantial gains in both efficiency and reliability achievable through low-code tools, affirming their practical utility and deployability for non-technical users.
📝 Abstract
Workflow automation has become increasingly accessible through low-code platforms, enabling small organizations and individuals to improve operational efficiency without extensive software development expertise. This study evaluates the performance impact of workflow automation using n8n through a small-scale business case study. A representative lead-processing workflow was implemented to automatically store data, send email confirmations, and generate real-time notifications. Experimental benchmarking was conducted by comparing 20 manual executions with 25 automated executions under controlled conditions. The results demonstrate a significant reduction in the average execution time from 185.35 seconds (manual) to 1.23 seconds (automated), corresponding to an approximately 151 times reduction in execution time. Additionally, manual execution exhibited an error rate of 5%, while automated execution achieved zero observed errors. The findings highlight the effectiveness of low-code automation in improving efficiency, reliability, and operational consistency for small-scale workflows.