Hexagon Launches APOLLO for Metrology Asset Monitoring

Why It Matters

Predictive metrology reduces unplanned downtime, directly enhancing manufacturing productivity and quality in a tightening labor market.

Key Takeaways

• AI predicts CMM failures up to 90 days ahead
• Predictive maintenance reduces unplanned downtime
• Platform integrates Hexagon and third‑party devices
• Cloud or on‑premises deployment meets data sovereignty
• Real‑time dashboards improve OEE and measurement reliability

Pulse Analysis

Hexagon’s new APOLLO platform marks a significant step in the digital transformation of manufacturing metrology. By embedding AI-driven condition monitoring directly into coordinate measuring machines and machine tools, APOLLO shifts the maintenance paradigm from reactive fixes to proactive insight. The system continuously ingests sensor data, environmental metrics and usage patterns, applying machine‑learning models to flag anomalies before they manifest as defects. For manufacturers grappling with skilled‑labor shortages, this predictive capability translates into fewer emergency repairs and more predictable production schedules.

The platform’s flexibility further differentiates it in a crowded market. APOLLO supports both Hexagon‑branded equipment and third‑party devices, consolidating disparate data streams into a single, cloud‑enabled dashboard while also offering on‑premises deployment for firms with strict cybersecurity or data‑sovereignty policies. This unified view of fleet health enables quality engineers to schedule maintenance during planned downtime, extending overall equipment effectiveness and stabilizing measurement drift. Early adopters report throughput gains of up to 15 percent without compromising inspection accuracy.

Beyond immediate operational gains, APOLLO positions manufacturers to capitalize on broader Industry 4.0 initiatives. The granular, real‑time asset data can feed digital‑twin models, supporting advanced simulation and continuous improvement programs. As production complexity rises and regulatory scrutiny intensifies, data‑driven metrology becomes a competitive moat. Hexagon’s move underscores a growing trend where AI not only optimizes equipment performance but also preserves institutional knowledge that traditionally resides in undocumented logs.