Manufacturing Data Trust
Your KPIs look right.
Are they?
After ERP migrations, system changes, or just years of drift,
the dashboard says one thing. The floor says another.
28 checks that tell you if your data is lying. No software. No IT. Just your CSV.
Sample forensic audit report
68%
of OEE reports are wrong
Addend Analytics
3%
of manufacturing execs fully trust their data
IndustryWeek / Dassault
$12.9M
average annual cost of poor data quality
Gartner
Production has one number.
Finance has another.
After an ERP go-live, a MES rollout, or just years of manual processes, your KPIs still populate. Dashboards still render. Reports still export.
But 68% of OEE reports are wrong. 88% of spreadsheets have errors. And only 3% of manufacturing executives fully trust their data.
Most teams discover the gap when the board asks why the numbers moved. By then, the decisions have already been made.
Upload a dataset. Get the evidence. Decide what to trust.
Your BI tool
Visualizes data. It doesn't question it.
Your ERP vendor
Configures the system. They don't audit what comes out of it.
There is no layer between “the data exists” and “the data can be trusted.”
That's what Plant Intel is.
Clean files are nice.
You need a partner that learns how
your plant is different.
Your plant. Our memory.
Every upload teaches us your naming conventions, shift patterns, and rates. The tenth audit knows your plant better than your documentation.
Domain expertise you can't prompt.
A general AI tool doesn't know that three shift values when you run four shifts is a problem. We do.
Nothing to install. Nothing to change.
Works alongside your ERP, your BI tool, your spreadsheets. Export a CSV, upload it.
People leave. The knowledge stays.
Personnel changes, system migrations, process updates. The knowledge survives all of it.
Free KPI Forensic Audit.
What you will learn
- Do your numbers actually add up, or are the rollups broken?
- Is your time coverage complete, or are there gaps nobody noticed?
- Which machines, shifts, and products are present, and which are missing?
- Has anything drifted since the last system change?
Results in under a minute.
No call. No commitment.
Use your results to determine whether a full validation engagement makes sense.
Who This Is For.
PE firms that inherited data they can't verify
70-90% of deals miss expectations. Validate KPI integrity across every facility before you build the 100-day plan on assumptions.
Learn morePlant teams whose dashboards don't match the floor
Upload a dataset, get a forensic audit in under a minute. Find out if your OEE is real before leadership asks.
Learn moreConsultants who want evidence before the first meeting
Walk in knowing more about the data than the team that manages it. Scope the real problems in minutes, not weeks.
Learn moreHow It Works.
Three steps from raw export to validated KPI.
Export
Pull a KPI dataset (CSV) from your ERP, MES, or reporting layer. No formatting required.
Stress Test
28 structural checks run automatically: key integrity, entity detection, time coverage, duplication, rollup logic, and drift.
Verdict
A forensic audit that tells you what's broken, what's drifted, and what still holds.
When This Is Used.
ERP Go-Live or Migration
The new system is live. The dashboards populate. But the rollup logic changed during configuration and nobody validated the output. You're three weeks from a board report built on assumptions that no longer hold.
MES / OEE Rollout
Real-time data collection changes what gets captured, how it aggregates, and which baselines still apply. The OEE number looks right. The components underneath may not be.
Ongoing Operations
No major system change required. Manual processes, product updates, and configuration drift quietly redefine KPIs over time. Most manufacturers discover this when leadership stops trusting the numbers -- not when it starts happening.
New Line Launch
New equipment means new variables, new baselines, and new risk.
Audit or PE Reporting
Investor diligence requires KPIs that can withstand scrutiny.
KPI Disputes
When Ops and Finance disagree on the numbers, the data structure is the tiebreaker.
Continuous Improvement
CI programs depend on trustworthy baselines. Test the foundation before building on it.
Sometimes the system changes. Sometimes nothing changes except the numbers. Either way, the structure needs validation.
Built for the plant floor, not the data lake.
Plant Intel was built for one thing: validating whether manufacturing KPIs still mean what they used to after a system change.
It knows about work orders, lot numbers, equipment IDs, shift patterns, and OEE components. It understands the difference between a batch process and a discrete assembly line.
of ERP implementation failures are caused by data migration issues. Plant Intel catches them before the board does.
A packaging line reported 78% OEE. Sensors found the real number was 58%. The gap was invisible until the data was validated.
28
structural checks
9
report sections
Every analyzer tested against real manufacturing data patterns: ERP exports, MES outputs, and line-level reporting across batch, process, and discrete environments.
Generic tools check for nulls. Plant Intel checks for broken assumptions.