Dylan Hoback
Accu-Tube
My Praxie analytics software transformed our operations, enabling data-driven decisions and streamlining our manufacturing process.
The line stopped at 2am. You found out at the morning meeting.
Replace manual downtime logs and end-of-shift reports with AI that watches every line in real time — showing what stopped, why, and what it cost while you can still do something about it.
$0
Send us one week of your real production data
See Praxie in Action Now
Free pilot on your own plant data. You keep what we find.
AI production monitoringfor plant managers and operations leaders
Customer Success Stories
Dylan Hoback
Accu-Tube
Jason Carpenter
Environmental Pest Management
With Praxie, I found a way to take what was in documents and spreadsheets and provide my team with a visual application environment to drive our strategy with full accountability.
Elizabeth Pridham
Perfection Fresh
Praxie dramatically improved our process through digital transformation. We now have visibility and can drive decisions at a fraction of the time.
Scott Russell
NUCOR Vulcraft
I would heartily recommend the Praxie team to any organization seeking to seriously undertake a lasting and successful innovation process.
Mike Bainbridge
Dover Food Retail
Our MFG Ops application greatly improved our daily management initiatives. It is easy to see how we are doing, identify issues, and track improvements—no matter where we sit.
Jeff Piotrowicz
ChemLink
With Praxie we've created a way to report on complex projects that gives management full visibility. Executives have visibility to projects, assignments, and more at their fingertips.
Maureen Thompson
American Nurses Association
Our Praxie App makes it easy to track progress on strategic objectives across the organization and includes an executive-level dashboard with real-time reports to the board on key initiatives.
Tom Anderson
Springfield Armory
Praxie is used every day to track and analyze every aspect of production, quality, safety, and more. We improved quality by 10%.
Peggie Pelosi
Innovators Alliance
Almost overnight, Praxie created a customized solution for our 100 member organizations across Canada to drive strategy and innovation.
Kate Merton
Anthem
Praxie's innovation solution stood out from other options because it can be customized so quickly to fit our exact process requirements. Plus, it is incredibly easy to use and manage.
By the time it is in a report, the shift is over.
Collecting the downtime data was the easy part. Knowing what it means, while the line is still down, is the job.
“Why did we miss the number again?”
“It went down, we fixed it, I did not log the reason.”
An OEE figure that is accurate, a week late, and argued with anyway.
You do not need another report. You need the line to tell you itself — while the shift is still running.
If you own the line
Is this your shift?
Most plant managers and operations leaders recognise at least four of these. If you recognise six, the rest of this page is written for you.
- Downtime reasons are logged from memory at the end of shift.
- OEE is calculated weekly and disputed every time.
- Nobody agrees what counts as planned versus unplanned.
- The same stoppage recurs without anyone noticing the pattern.
- Micro-stops never get recorded at all.
- The real cause is whoever shouted loudest in the meeting.
- Improvement targets are set against numbers nobody trusts.
- “Why did we lose those hours?” takes a week to answer.
None of this is a measurement problem. It is a latency problem.
Is this you?
Worked example — multi-line manufacturing siteDowntime used to be reconstructed after the shift.
Shifts↓Secondsto know a line has stopped and why
Several lines, three shifts, and downtime reasons written on a clipboard at the end of the day.
Because the record was made from memory hours later, causes were approximate, micro-stops disappeared entirely, and the OEE number was argued with before it was ever acted on.
With AI-powered monitoring the stoppage, its reason and its cost are captured as they happen, so patterns surface while the line is still running rather than in next week's report.
Monday, 6am
What you walk onto the floor with
Today
A report about last week.
- Downtime reasons logged from memory
- Micro-stops invisible
- OEE disputed before it is acted on
- Causes found after the loss
→
With Praxie
A view of what is happening now.
- Stoppages captured as they happen
- Reasons and cost attached automatically
- Recurring patterns surfaced early
- One number everyone works from
Key ROI Elements
24%
OEE Improvement
Higher availability, performance, and quality across lines.
15%
Less Downtime
Faster detection, escalation, and corrective action.
20%
Higher Throughput
More good output from existing labor and equipment.
10%
Less Scrap & Rework
Quality losses become easier to find and prevent.
80%
Faster Reporting
Automate shift reports, KPI summaries, and OEE dashboards.
Better
Root Cause Focus
Prioritize the biggest loss drivers by line, asset, shift, and product.
See Praxie in Action Now
Which line loses you the most time, and do you know why?
Now select a day and time below.
Why Shift Reports Arrive Too Late
Spreadsheets &
Manual Reports
- Manual data collection
- Lagging OEE reports
- Inconsistent downtime codes
- Limited root-cause visibility
- Hard to sustain improvement
ERP / MES
Standard Reporting
- Connected to production records
- Often rigid dashboards
- Limited cross-system context
- Heavy configuration changes
- Reports without action workflows
Traditional OEE /
Shop-Floor Tools
- Good machine and line visibility
- Useful downtime tracking
- Can be hardware-dependent
- Often narrow to plant-floor data
- Limited AI recommendations
BI Dashboards &
Data Warehouses
- Strong reporting layer
- Useful executive dashboards
- Requires data modeling
- Limited operational workflows
- Insights often stop at analysis
★ BEST FIT
Praxie
AI Manufacturing & OEE Analytics
- Flexible AI-powered analytics workspace
- OEE, downtime, scrap, throughput & quality visibility
- Connects ERP, MES, machine, sensor & operator data
- AI root-cause insights, alerts & recommendations
- Workflow automation to drive corrective action
Real-time OEE visibility
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AI root-cause analysis
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Flexible KPI configuration
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Cross-system data integration
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Alerts & workflow automation
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Speed to deploy
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Why Praxie
Stands Out
Stands Out
More flexible than standard ERP and MES reports
Far more automated than spreadsheets and manual OEE reporting
Broader than point OEE tools because analytics connect to workflows
Faster to deploy than heavy BI or custom data warehouse projects
Questions Plant Leaders Ask Us First
The objections we hear in the first 20 minutes — answered plainly.
1
How does AI-powered manufacturing analytics improve OEE?
Answer: AI connects production, downtime, quality, labor, and machine data so teams can see what is reducing availability, performance, and quality. Instead of only reporting OEE after the fact, the system highlights losses, trends, and improvement opportunities while there is still time to act.
2
Can the system identify downtime causes and bottlenecks automatically?
Answer: Yes. AI analyzes downtime codes, machine events, production rates, operator notes, work orders, and quality issues to surface recurring causes of lost time. It can also flag bottlenecks by line, cell, asset, shift, product, or process step so teams know where improvement work should start.
3
How is this different from a standard OEE dashboard?
Answer: A standard dashboard usually shows what happened. AI-powered OEE analytics helps explain why it happened and what to do next. It can detect patterns, summarize performance, recommend actions, and connect OEE losses to maintenance, quality, scheduling, materials, and operator workflows.
4
Will this replace our ERP, MES, CMMS, or quality systems?
Answer: No. AI-powered manufacturing analytics works as an intelligence layer across those systems. It brings together data from ERP, MES, CMMS, quality systems, spreadsheets, sensors, and operator inputs so managers can get one clear view of performance without replacing core systems.
5
Can operators and supervisors actually use it on the shop floor?
Answer: Yes. The goal is to make analytics practical for daily management. Supervisors can review shift performance, downtime, scrap, throughput, and open issues, while operators can capture notes, reasons, and events that make the data more accurate and actionable.
6
Does AI help turn OEE insights into improvement actions?
Answer: Yes. AI can summarize recurring losses, suggest root causes, create action items, trigger workflows, and support improvement methods like 5 Whys, A3, DMAIC, maintenance follow-up, and shift handoff reviews. This helps teams move from reporting problems to fixing them.
Bottom line: AI-powered manufacturing and OEE analytics helps teams see production losses faster, understand root causes earlier, and turn performance data into measurable operational improvement.
Which line would you watch first, if you could see it live?
Send us one week of your real production data. We’ll show you what Praxie would have caught — your lines, your stoppages, your losses.
1Send us a week of data230-minute review3Free pilot on your data
See Praxie in Action
No cost, no obligation — and you keep what we find either way.