Across precision manufacturing, automation is on every owner's mind. Pallet pools, cobots, mill-turn platforms, lights-out shifts, and integrated cells are no longer aspirational. They are a competitive necessity for shops planning to grow.
Why Reata's Data Foundation Made Their Automation Strategy Work
Case Study from | Datanomix
The case for measuring the shop you have before scaling the shop you want.
Why Most Manufacturing Automation Investments Underperform
Across precision manufacturing, automation is on every owner's mind. Pallet pools, cobots, mill-turn platforms, lights-out shifts, and integrated cells are no longer aspirational. They are a competitive necessity for shops planning to grow.
But the failure mode of those investments is well documented and rarely discussed openly. Pallet pools running half empty. Cobots idle more than the spindles they were meant to support. Lights-out shifts that nobody can confidently report on the next morning. Capacity gains quoted in the proposal that never materialize on the floor.
These outcomes don't happen because the hardware was wrong. They happen because the data layer beneath the automation was incorrect, missing, or guessed.
The industry shift worth paying attention to is not the hardware itself. It is the growing recognition that automation only pays off in a shop that is being measured honestly. Real-time machine data, integrated with planning systems and trusted by the people running operations, is what determines whether the next capital investment delivers the gains it promised.
What Reata Engineering Needed Before Their Next Automation Investment
Reata Engineering & Machine Works, founded more than 30 years ago and based in Englewood, Colorado, has built its reputation on complex, high-precision work. The company runs an engineer-led shop with modern hardware, including Integrex platforms, a pallet pool, and an active path toward more automation.
By the time the team began evaluating new automation investments, Reata's engineering culture had already done much of the hard work. Cycle times were carefully quoted. Setup times were rigorously discussed. Processes were validated and refined.
The challenge was different from what most shops face. Reata did not lack engineering discipline. What they lacked was a way to confirm in real time whether the shop floor matched the engineering plan.
Specifically, the team needed answers to questions that should not have required guesswork:
- Were machines running as planned?
- Was utilization matching the quoted assumptions?
- Were any cells overloaded or underutilized?
- Which jobs were trending late, and why?
Without real-time visibility, the answers were either delayed, partial, or unavailable. That gap mattered most in the context of automation. Reata's leadership recognized that any next investment, whether a pallet pool, an additional cobot, or a path toward lights-out, would only pay off if the automation landed on was being measured honestly.
The team had previously shared how their engineering culture set the foundation for data integration. This project is about what that foundation made possible next: a confident, measured approach to automation.
Production Monitoring as the Linchpin for Automation
Reata implemented Datanomix Production Monitoring to deliver real-time machine performance directly from each controller, with No Operator InputTM required. Cycle times, utilization, downtime causes, and notifications about jobs at risk all flow continuously into a single view of the shop.
The platform was then integrated with Reata's ERP, Fulcrum, which sends its planning data, including job plans, cycle times, and delivery targets, directly into Datanomix. The result is a single screen where planned expectations and actual machine performance sit side by side.
That setup gave Reata's engineering team an honest picture of the shop, updated in real time, with both the plan and the reality visible simultaneously.
Mike Masterson, an engineer at Reata, described what made the data foundation matter for automation specifically:
"If you want to automate anything, you have to be accurate. That's the linchpin for our automation."
Automation, in Mike's framing, is not a hardware decision. It is an accuracy decision. The hardware multiplies whatever shop it lands on. If the shop is being measured honestly, automation multiplies productivity. If the shop is being guessed at, automation multiplies the guesses.
How Real-Time Visibility Changed Reata's Engineering Operations
The most useful surprise during the rollout was how the connected environment changed engineering operations beyond what the team had expected.
Reata's engineers do not just design parts and processes. They also manage flow, balance load across cells, and act as the technical bridge between the shop floor and the customer. Craig Seykota, another engineer at Reata, described the model:
"We're not strictly just engineers, we're also operations directors, value stream leaders, and ops central managers all at the same time. We're leveraging our engineering expertise and bringing that to a more director's role, which really helps bridge the gap between the shop and the customer."
That model only works if the engineers have access to real-time truth. Without it, even the strongest engineering team is playing the same guessing games every other shop plays.
A second insight came on the customer-facing side. Darius Totah, also an engineer at Reata, described how the foundation changed customer conversations:
"The biggest thing is obviously just the visualization. That's helped tremendously with being able to get answers as quickly as possible to customers, especially when they're expecting parts, or they're needing something changed real quick."
When the data is honest, customer service stops being a guessing game, too. Late jobs get flagged the day they slip, not the week after a customer complaint. Customers hear about risks earlier and with a plan attached, rather than late and with an apology.
For a shop competing on high-precision, high-complexity work, that shift is not a peripheral benefit. It is a competitive edge.
27% More Uptime, 28% More Utilization, and a 7x ROI
After the Datanomix pilot wrapped and the platform became part of the normal production rhythm, the post-pilot numbers came back measured rather than projected:
- 27% increase in machine uptime
- 28% increase in machine utilization
- 10% reduction in common waste
For Reata, Datanomix translated into more capacity. Each machine delivered an additional 204 productive hours per year. At a standard $80 shop rate, that is $16,308 per machine in recovered productivity in just one year. Across the shop, Reata saw a 7x return on their Datanomix investment.
Those numbers came from a real production environment, not modeled projections. But the more important outcome is qualitative. Reata's leadership now approaches their next automation investments with confidence rather than hope because their decisions are evaluated against measured reality rather than projected best-case scenarios
That confidence is the durable outcome. The percentages and dollar figures change shop to shop. The pattern they prove does not. Real-time machine data, integrated with planning systems and used by people empowered to act on it, is what turns automation from a hopeful investment into a profitable return.
The content & opinions in this article are the author’s and do not necessarily represent the views of ManufacturingTomorrow
Datanomix
Datanomix empowers manufacturers of all sizes to increase productivity and profitability through its Data-Powered Productionâ„¢ solutions. Its product portfolio includes Production Monitoring, G-Code Cloudâ„¢ + DNC, TMAC AIâ„¢, and ToolAnalytixâ„¢ - all designed to turn machine data into actionable insights with zero operator input. Headquartered in New Hampshire, Datanomix software analyzes real-time production signals to identify bottlenecks, improve quality, and provide prescriptive coaching to drive continuous improvement. For more information, visit www.datanomix.io.
Other Articles
If the Headline Number on Your Monitoring System Is Spindle Uptime, You're Measuring the Wrong Thing
Automate 2026 Q&A with Datanomix
From 32 Minutes to 10: How Neo Industries Turned Production Data Into Compounding Returns
More about Datanomix
Featured Product
