---
title: "The Floor Report: Three Decisions You Can’t Make on Gut Anymore"
date: 2026-05-29 13:09:37
description: "This Floor Report breaks down insourcing, hiring, and ERP schedule slips— and the decisions real-time machine data is changing now."
keywords: "The Floor Report"
categories: [The Datanomix Blog]
tags: [Annie Michaud, Capacity, Delivery Track, Efficiency, ERP Connectivity, labor shortage, Machine Monitoring, Manufacturing Technology, OEE, On-Time Delivery (OTD), Production Monitoring, Scheduling, Visibility]
---

## Q2 2026 with Annie Michaud, VP of Customer Success

Most precision manufacturers think they’re running their machines at 80%. [The real number is closer to 40%. ](https://datanomix.io/2026/04/23/how-to-make-more-money-in-precision-manufacturing-with-production-monitoring/)That gap is where every Q2 decision we’re seeing right now starts.

As VP of Customer Success at Datanomix, I spend a lot of time talking with shop owners and operations leaders about what’s actually happening on their floors. Q2 already feels [different from Q1](https://datanomix.io/2026/03/11/the-floor-report-what-cnc-machine-monitoring-reveals/), and I’ve been hearing three consistent themes I want to share. These aren’t ongoing struggles. They’re decisions shops are actively making right now, with real money and real customer relationships on the line.

Chances are, at least one of these is on your radar.

[](https://www.linkedin.com/in/annie-abber-michaud-78316554/)**Annie Michaud**
VP of Customer Success

### Decision #1: Should You Bring Outsourced Work Back In-House?

Supplier quality is slipping. Lead times are unpredictable. And every job you send out is margin you’re handing to someone else, plus the risk that it comes back wrong.

The instinct to bring that work in-house makes sense. The question is whether you actually have the capacity to absorb it.

Here’s what I hear consistently: shops assume their machines are full. The work going out the door is usually the painful kind. Turning runs that fill up the lathes. OD and ID grinding the shop is already equipped for. 5-axis finishing on parts that could absolutely run on the machine sitting idle three bays over. The work isn’t going out because the shop can’t do it. It’s going out because nobody can see clearly whether there’s room to do it. When we look at the real production data together, there’s almost always more room than anyone expected. 

One precision manufacturer described it this way: 

**“We’re outsourcing turning and grinding because it looks like we’re at capacity. But then we end up with certain areas totally swamped and other machines sitting idle. We’re losing time and still sending work out.”**

That’s not a capacity problem. It’s a visibility problem.

Before you commit to insourcing,[ Production Monitoring](https://datanomix.io/production-monitoring/) gives you the answer at the machine level. How much real capacity do I have? Where is it? When is it free? Once you can see it clearly, the decision gets a lot easier to make with confidence, and the work you bring back doesn’t blow up [the schedule](https://datanomix.io/2026/02/06/industry-insights-planning-meets-reality-and-reality-wins/) for everything else.

## FIRST DOWN WEBINAR SERIES: Machine Efficiency: Unlocking Capacity You Already Own

Learn how to use real production data to uncover hidden capacity and make smarter decisions without defaulting to capital spend.

[Learn More](https://datanomix.io/2026/02/26/webinar-machine-efficiency-unlocking-capacity-you-already-own/)

### Decision #2: Hiring isn’t getting easier. Do Your Machines Need to Carry More of the Load?

Hiring for[ second shift keeps stalling](https://nam.org/manufacturing-in-the-united-states/). The skilled operators you do have are being pulled off machines to program, inspect, and handle everything that falls through the cracks. Meanwhile, the spindles aren’t running.

The answer isn’t always more headcount. Sometimes it’s[ robots, sometimes it’s lights-out production](https://datanomix.io/2026/04/27/best-automation-investments-for-precision-manufacturers/), and sometimes it’s just getting a cleaner picture of where operator time is going. The shops getting automation right are the ones who earned the right to automate first. They know which machines are running, which ones aren’t, and where a robot or a pallet pool will pay back fastest. The shops that skip that step end up with expensive automation feeding an [underutilized cell.](https://datanomix.io/automation-calculator/)

What I see again and again is [shops trying to solve a labor problem](https://datanomix.io/2024/04/23/building-a-culture-of-efficiency/) when what they actually have is a data problem. Without a clear picture of where machine time and operator time are really going, it’s hard to see where automation would actually pay off, where the real bottleneck lives, or where your people are best deployed. Once that picture is in place, [the guessing stops, and the planning starts](https://datanomix.io/erp-insights/).

### Decision #3: When Will the Schedule Slip, and What Do You Do About It?

The schedule lives in the ERP. The reality lives on the machines. And somewhere between those two things, jobs slip, dates get missed, and customers find out before you do.

[ERP schedules](https://datanomix.io/erp-insights/) are built on estimated cycle times that may be years old or never validated against what the machines actually do. The job the ERP says a job will take 4 hours, but it’s actually taking 6. The standard setup says 90 minutes, but it’s really 3 hours. Multiply that across a week of jobs, and the schedule you built on Monday is fiction by Wednesday.

A shop leader told me recently,

[](https://datanomix.io/2025/04/16/making-more-a-manufacturing-evolution/)ERPiphany is the moment you realize your ERP won’t give you the answers you need to make more

**“The visibility disappeared when we went live on the new ERP. ** **We were on track to receive a customer excellence award in June. That’s not going to happen anymore.”**

That one stuck with me. The team had worked hard for that recognition, and they lost it without ever seeing it slip.

Real-time[ production monitoring](https://datanomix.io/production-monitoring/) closes that gap. It tells you, by the end of the first hour of a job, whether you’re going to hit the estimate or not. It flags the setup that’s running over before the next job in the queue is at risk. It updates[ delivery projections](https://datanomix.io/delivery-track/) based on what your machines are actually doing right now, not on a number someone typed three years ago. You see the slip on Tuesday morning, not on Friday afternoon when your customer calls. You move jobs, reset expectations, and call ahead instead of getting caught. 

That’s the difference between managing your schedule and chasing it. And it’s how the next customer excellence award doesn’t get away from you.

### How Real-Time Machine Data Changes the Way You Decide

The root cause behind all three of these decisions is the same: shops are making them based on estimates instead of what the machines are actually doing right now. That’s where insourcing decisions get made without confidence, where workforce constraints turn into hiring problems you can’t hire your way out of, and where ERP schedules drift from the reality on the floor.

Production Monitoring built on real, automated intelligence changes that. With no operator input required, you can[ get the story straight from the machine](https://datanomix.io/the-datanomix-difference/), see capacity before you outsource it, find labor leverage before you hire, and catch delivery risk before your customer does.

Manufacturers using Datanomix see an average 15% increase in parts output and 27% improvement in cycle time.[ Right Angle Steel & Fab](https://datanomix.io/2026/04/29/machine-monitoring-fabrication-right-angle-steel/) found 41% more uptime and 45% more capacity.[ Coastal Machine and Supply](https://datanomix.io/2026/04/29/coastal-machine-five-axis-utilization-46-percent/) lifted five-axis utilization 46%.[ Neo Industries](https://datanomix.io/2026/04/13/video-how-neo-industries-optimized-cycle-times-reduced-downtime-19-and-built-a-path-to-automation-in-their-small-shop/) cut downtime 19% and built a path to automation.

[See how shops like yours are putting real machine data to work.](https://datanomix.io/request-a-pilot/#results)

### Ready to Stop Deciding on Gut?

If any of these decisions are on your desk right now, we’d love to find some time to connect. We’ll look at your floor together, identify the real opportunities, and show you how a clear picture of your production data would change how you decide.

Every customer starts with our pilot program and pays for itself in weeks, not years.

[Request Your Pilot](https://datanomix.io/schedule-a-demo/)