Analysis of Process Reengineering Based on K-means Clustering

This paper addresses efficiency optimization in office automation systems through a process reengineering methodology based on K-means clustering analysis. User behavior metrics specifically average processing time and task engagement frequency (number of tasks processed)—were collected and analyzed. Applying the K-means algorithm, users were categorized into four distinct groups: high-efficiency, lowefficiency, high-effort, and low-engagement users. The analysis identified critical system bottlenecks: Low-efficiency users (22.7% of the total) accounted for 38% of overall processing time; Lowengagement users (4.2% of the total) demonstrated insufficient task participation. Based on these clustering results, a tiered optimization strategy was implemented: intelligent assistant tools were deployed for low-efficiency users, while incentive mechanisms were designed for low-engagement users. Postimplementation, the system's overall efficiency increased by 42%, validating the effectiveness of this data-driven process reengineering approach.