Hexagon is showcasing innovation in metrology with the launch of APOLLO, a platform designed to predict failures in CMMs and machine tools before quality and production schedules are affected. This technology highlights the shift from reactive to predictive metrology, saving teams from significant avoidable delays, equipment failure, and drift. Its vendor-agnostic visibility makes it accessible and easy to work with, and ensures better results.

APOLLO replaces manual tracking with automated insights, helping workers stay up to date and upholding high quality assurance standards. Quality Digest was able to learn more from Shaun Wissner, product manager at Hexagon Manufacturing Intelligence.

Quality Digest: How does APOLLO decrease downtime, increase reliability, and improve equipment function and effectiveness? 

Shaun Wissner: Three things: First, APOLLO watches machines in real time and flags issues the moment they happen, not three batches later when someone notices the measurements look weird. Second—and this one’s underrated—it scores collision criticality. Not every collision wrecks your measurement quality; some are harmless, some aren’t. APOLLO can tell the difference. That saves downtime in both directions: You stop ignoring the bad ones, and you stop overreacting to the harmless ones. Third, reference-part tracking predicts when a device is drifting out of spec, so interim checks and calibrations can be scheduled before problems, not after. It’s worth noting that annual certifications should always be considered as standard practice to ensure that the overall health and accuracy of your CMM is confirmed with NIST/ISO-traceable artifacts, but APOLLO is what keeps you confident in between those certifications. On the effectiveness side, APOLLO breaks OEE down into queue time, setup time, idle time, error time—all the stuff that’s normally invisible and expensive. Once you can see it, you can actually do something about it.

QD: How easy is it for different teams to use and integrate APOLLO into their processes?

Wissner: Pretty easy, actually. Different roles get different views into the same underlying data. Inspection managers see machine health and runtime; process engineers see collision analysis and stability; production managers see OEE and utilization; quality teams get SPC and traceability. Nobody’s exporting spreadsheets to each other or arguing over which tool is the source of truth.

On the integration side, APOLLO connects natively to Hexagon, Leitz, ZEISS, OGP, LK, and Renishaw—and for everything else there’s generic OPC-UA and MQTT support, so CNC machines, robots, and third-party equipment all come along for the ride. We also run our own OPC-UA server, which means if you want to pull APOLLO data into MES, ERP, or a custom dashboard, you can, no custom integration project required.

QD: What does training and setup look like? 

Wissner: Lighter than you’d probably expect, and almost always remote. Private Cloud installs take about an hour with one of our engineers on a Teams call. If you want to self-host, the APOLLO server needs a pretty modest box; 64-bit Windows 10+ or Linux, 16 GB RAM, 15 GB storage, sixth-gen i5 or equivalent. VMs are fine. Adding each measuring device is about 20 minutes of work total; 15 minutes to install the local services on the machine computer, five to configure the connection. Customers can do it themselves or have us walk them through it. Training is a one-hour remote workshop, and we’re happy to run as many follow-up one-on-ones as a team wants. There’s also a full library of demo and training videos on Vimeo for anyone who’d rather learn at their own pace.

QD: What type of AI analytics does APOLLO gather and analyze to optimize production? 

Wissner: APOLLO pulls from four data layers—machine status, measurement results, reference-part readings, and probe telemetry—and runs analytics on all of them. For condition monitoring, it combines collision speed, deflection, and reference-part drift to score how critical a collision actually was and forecast measurement stability. For calibration timing, it projects when a device will drift out of spec, so interim checks and maintenance get triggered on evidence instead of the calendar.

And just to reiterate the point, annual certifications should always be considered as standard practice to ensure that the overall health and accuracy of your CMM is confirmed with NIST/ISO-traceable artifacts. APOLLO complements that baseline; it doesn’t replace it. For production optimization, it buckets utilization into productive, queue, setup, idle, and error time; computes OEE; and flags where the biggest recoverable time is hiding. On the quality side, it runs ISO-compliant SPC—including process capability, control charts, and histograms—automatically on every routine, no separate stats package needed.

QD: What makes APOLLO unique compared to other platforms? 

Wissner: Short answer: It’s the only platform we know of that puts condition monitoring, OEE, collision criticality assessment, in-spec prediction, SPC, and probe life-cycle tracking into one product that works across manufacturers. Most tools in this space are good at one of those things. Customers end up with a condition-monitoring tool from one vendor, an SPC package from another, a homegrown OEE dashboard built on somebody’s spreadsheet, and no way to trace a bad measurement back to the device condition that caused it. APOLLO collapses that stack. The other differentiator is deployment flexibility: full on-premise for customers with data sovereignty concerns, or private cloud if you want a lighter lift. For regulated industries, that choice matters a lot.

QD: What are the key features, and do you have any specific data that include percentages of downtime reduced, percentage of production increased? 

Wissner: On features: real-time device status and event logging; collision detection with criticality scoring; automated reference-part tracking and in-spec forecasting; OEE analytics with queue, setup, and idle-time breakdowns; centralized measurement storage with full program-to-part traceability; ISO-compliant SPC; probe utilization and life-cycle tracking; OPC-UA server for third-party data access; and native compatibility with Hexagon, Leitz, ZEISS, OGP, LK, and Renishaw (plus OPC-UA/MQTT for everything else).

On numbers: APOLLO is built to deliver up to 25% more machine utilization and operator efficiency, and we’re seeing that hold up in the field. A production manager using APOLLO has reported a 25% OEE increase, along with better planned runtimes and eliminating manual scanning. Beyond that, we target 100% real-time visibility into measurement stability, 100% traceability of results and conditions per part, and effectively 0% risk of undetected collisions or missed critical machine errors. That’s the “never blind” principle the product was built around.

QD: Any other thoughts to include or that you’d like to discuss? 

Wissner: Two things worth mentioning: First, APOLLO is manufacturer-agnostic on purpose. Hexagon built it, but the road map has always assumed customers run mixed shop floors, because they do; a monitoring tool that only sees half the fleet is only half a solution. Second, APOLLO puts capabilities like SPC and OEE analysis within reach of teams that don’t have dedicated statisticians or data engineers on staff. A lot of what used to require expensive specialist software or outside consultants is now just running automatically in the background. Honestly, that’s the part of the product I’m proudest of. 

Photo credit: Hexagon