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Manufacturing Data Trust

Your KPIs populate.
Your dashboards render.
But can you trust them?

The check that should happen before every other check.

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

How every engagement starts

1

Upload

Drop a CSV from any system. 28 structural checks run automatically.

2

Evidence

See where KPI logic has broken, drifted, or been gamed.

3

Scope

Determine which breaks are cosmetic and which are business-critical.

4

Monitor

Ongoing validation. So it doesn't happen again.

What you get

  • Find out if your rollup logic survived the last system change
  • Detect entity gaps: missing machines, phantom shifts, orphaned work orders
  • Surface the structural breaks your BI tool assumes don't exist
  • Time coverage and drift analysis across your entire dataset

What this is not

  • Not a BI tool. Dashboards show what your data says. We show whether it's telling the truth.
  • Not OEE software. They measure your data. We validate it.
  • Not a consulting engagement. Consultants take weeks. This takes 60 seconds.
  • Not software. No install, no integration, no IT ticket.
1

Start your engagement here

Drag and drop a CSV file here

or click to browse. CSV files up to 50MB.

Private by default. Your results are only accessible through a unique link.

CSV export from any system. Up to 50MB. No integration. No IT. Just your CSV.

Not ready to use your own data?

Try it on sample data that mirrors your environment.

88% of spreadsheets have errors.
Build a sample that mirrors yours and see what we find.

You've never had it this good.

Clean files are nice.

You need a partner that learns how
your plant is different.

1st uploadMC_IDmachine_id?
42%
3rd uploadMC_IDmachine_id
88%
6th uploadMC_IDmachine_id
97%

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.

shift_id has 3 values, expected 4auto-detected
oee_pct > 100% on 23 rowsauto-detected
machine_id naming inconsistencyauto-detected

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.

SAP
Oracle
Epicor
Excel
Custom ERP
SCADA/CSV

Nothing to install. Nothing to change.

Works alongside your ERP, your BI tool, your spreadsheets. Export a CSV, upload it.

Jan
Initial mapping23 columns
Mar
New operator added24 columns
Jun
ERP update24 columns
Sep
Analyst turnover24 columns

People leave. The knowledge stays.

Personnel changes, system migrations, process updates. The knowledge survives all of it.

The industry knows the problem exists.

“68% of OEE reports are wrong”— Addend Analytics
“78% on the report. 58% with sensors.”— Addend Analytics
“70% use lean. 2% achieve their objectives.”— IndustryWeek
“$12.9M average cost of poor data quality”— Gartner
“75% of ERP failures caused by data migration”— Ultra Consultants
“84% of digital transformations fail”— Altix Consulting
“Only 3% fully trust their data”— IndustryWeek

Three ways this ends.

Do nothing

Your KPIs keep populating. Your dashboards keep rendering. Nobody knows they're wrong until the board asks.

84% of digital transformations fail. The common denominator is data nobody validated.

Upload your data

60 seconds. 28 checks. Find out what's actually in your data before you build on it.

Free. No software. No IT ticket.
No commitment.

Talk to us first

For teams that want to scope what to fix before they upload.

The first conversation is free. We will tell you exactly where you stand.