LeadTimeTruth.

ERP LEAD-TIME AUDIT / 001

Your ERP knows
your supplier
lead times.
But are they
still true?

Upload your PO history and find planning lead times that no longer match how suppliers actually deliver.

Audit my lead times See an example

Start with CSV. No ERP integration required.

ONE STORED ASSUMPTION.DOZENS OF REAL DELIVERIES.
✳ LEAD TIME AUDITLT—001 EXAMPLE
SUPPLIER / ITEM

Supplier ABC / SKU-1044

! REVIEW
ERP ASSUMPTION30daysTHE STORED VALUE
OBSERVED MEDIAN41daysTHE DELIVERY HISTORY
ACTUAL DELIVERIESDAYS →
ERP 30 MEDIAN 41263035404548
34374235484139443647

10 recent observations · statistics from all 37 deliveries

P8046 days
LAST 90 DAYS43 days
VARIABILITYHigh
PLANNING GAP+11 days

The 30-day assumption is no longer supported by delivery history.

BALANCED REVIEW41 DAYS
CONSERVATIVE REVIEW46 DAYS
✓ HIGH CONFIDENCE
37 completed deliveries
OBSERVED HISTORY · NOT A FORECAST01 / 17
↳ The number stayed still. Reality moved.
CALIBRATE THE PARAMETER. KEEP YOUR PLANNING SYSTEM.SCROLL TO INSPECT

01 / THE PROBLEM

A planning parameter can look precise
long after it stopped being true.

ERP MASTER DATA UNCHANGED

Supplier ABC / SKU-1044

30 days
PLANNING LEAD TIMELAST UPDATED: 2024
MEANWHILE, RECENT DELIVERIES
374241484446
OBSERVED MEDIAN43 DAYS

The ERP keeps planning with the stored value until someone notices that reality moved.

Not “Is the supplier slow?”
“Can we still trust the number?”

THE WORK BETWEEN EXPORTS

Today, calibration usually
lives outside the ERP.

01

The familiar route

Export PO data → Open Excel → Write formulas → Group by supplier → Check average → Compare manually → Forget spreadsheet ↺

“The average looks OK.
Why do we keep getting surprised?”
02

With LeadTimeTruth

Upload→Audit→Review exceptions→Update ERP yourself

LESS SPREADSHEET MAINTENANCE. MORE PARAMETER REVIEW.

DELIBERATELY NARROW

Not another
inventory
planning system.

LeadTimeTruth checks one assumption your planning system already depends on.

YOUR EXISTING PLANNING STACK
FORECASTINGREPLENISHMENTSAFETY STOCKPO GENERATIONSUPPLIER MANAGEMENTOPTIMIZATION
↓ ONE CRITICAL INPUT
LEADTIMETRUTHLead time.IS IT STILL TRUE?

Keep your planning stack. Audit the parameter underneath it.

02 / HOW IT WORKS

From purchasing history
to a prioritized review list.

01INPUT

Bring the PO data
you already export.

Upload completed PO history and your current lead-time master. Supplier, item, order date, receipt date. Start there.

po_history_2026.csv
12,482 ROWS ✓ READY
Vendor Code→SUPPLIERPO_DT→ORDER DATE
02MEASURE

Measure what
suppliers actually did.

Deterministic statistics calculate observed distributions, recent drift, variability, and sample size.

ERP30
MEDIAN41
P8046
LAST 90D43

RECEIPT DATE − ORDER DATE = ACTUAL LEAD TIME

03REVIEW

See which values
deserve attention.

Get an exception-first list of assumptions that are no longer well supported. Your team decides what changes.

17! REVIEW
48~ WATCH
417✓ SUPPORTED
Review 17 parameters

03 / THE LEAD TIME AUDIT

Not more metrics.
A shorter list of decisions.

Start with the assumptions
most worth changing.

LEADTIMETRUTH / EXAMPLE REPORT

Lead Time Audit✳

SEPTEMBER 2026
12,482 COMPLETED PO LINES
482PARAMETERS CHECKED
417✓ SUPPORTED BY HISTORY
48~ WATCH
17! REVIEW
EXCEPTION QUEUE / SELECTED PARAMETERS↓ LARGEST MATERIAL GAPS FIRST
SUPPLIER / SKUERPMEDIANP80GAPVAREVIDENCEACTION
01ABC INDUSTRIALSKU-1044304146+11HIGH37 POs! REVIEW
02UNION COMPONENTSSKU-61283743+9HIGH46 POs! REVIEW
03OMEGASKU-18211519−6LOW27 POs↘ REDUCE
04BETASKU-54454447−1LOW81 POs✓ SUPPORTED
PLANNING GAP = OBSERVED MEDIAN − ERP VALUE · ALL VALUES IN DAYSHUMAN REVIEW REQUIRED

A stored value is not a fact.
Give it an evidence check.

Find your discrepancies

04 / THE AVERAGE CAN LIE

Same average.
Very different planning risk.

Thirty days on average
can still be a bad assumption.

SUPPLIER A ✓ LOW VARIABILITY
30 DAYS060
29 · 30 · 31 · 30 · 29 · 31
MEDIAN 30P80 31MEAN 30
SUPPLIER B ! HIGH VARIABILITY
30 DAYS060
12 · 18 · 27 · 41 · 54 · 31
MEDIAN 29P80 41MEAN 30.5

P80 uses linear interpolation. Six observations illustrate spread; small samples deserve caution.

05 / REALITY MOVES

The value was
correct once.
And now?

A long-term average can hide a recent change. Compare periods while keeping the ERP assumption in view.

CURRENT MEDIAN VS ERP+8 days! REVIEW
ONE FIXED ASSUMPTION. A SHIFTING DISTRIBUTION.
ERP 35PREVIOUS QUARTER36 DAYSCURRENT QUARTER43 DAYSPERIOD-OVER-PERIOD DRIFT: +7 DAYS

Recent drift and the ERP planning gap are different signals. See both.

SUPPLIER TRUTH CARD ST—017

Shenzhen
Parts Ltd.

ERP REFERENCE ASSUMPTION35 days
MEDIAN43
P8051
LAST QUARTER47
PREVIOUS QUARTER39
17 SKUs IN SCOPE! HIGH VARIABILITY

A CLEARER SUPPLIER CONVERSATION

Less “it feels slower.”
More “here’s the history.”

Lead time has drifted upward. The current reference value understates observed delivery time.

11HIGH-CONFIDENCE
SKU-LEVEL MISMATCHES

Start with the supplier pattern. Review each affected item. One supplier-level value does not automatically fit every SKU.

ILLUSTRATIVE SUPPLIER SUMMARY

06 / SHOW THE EVIDENCE

A recommendation
should explain itself.

CURRENT → SUGGESTED REVIEW
30 → 41

Not one magical “correct” number. Transparent review values, with the evidence beside them.

You decide which planning policy fits your operation.

AN EXAMPLE / THE SUPPORTING HISTORY
MEDIAN
38 days
P80
41 days
P90
47 days
LAST 90 DAYS
42 days
SAMPLE
37 deliveries
VARIABILITY
Medium
BALANCED / MEDIAN38 days
CONSERVATIVE / P8041 days

Review values, not automatic ERP updates.

THE RIGHT AMOUNT OF DETAIL

Everything to inspect the assumption.
Nothing to replace your ERP.

01

Actual vs configured.

Compare planning master data with completed PO history, supplier by supplier, item by item.

30CONFIGURED≠41OBSERVED
02

Beyond the average.

See P80 and P90. Know how far the delivery distribution extends.

MEDIAN 41 / P80 46
03

Variability, visible.

Flag combinations where a single number hides a wide range.

! HIGH VARIABILITY
04

Recent drift.

See what changed recently, not just what happened historically.

PREVIOUS 39 → CURRENT 47
05

Evidence count.

Six deliveries or sixty? Sample size belongs next to the finding.

SAMPLE 37 COMPLETED POs
06

Less CSV housekeeping.

Map purchasing fields and see excluded rows before trusting the result.

Vendor Code → SUPPLIER

07 / BUILT FOR REAL PURCHASE DATA

One parameter.
Several ways to get it wrong.

01

Wholesale & distribution

Find supplier/item lead times that have drifted before stale assumptions keep influencing replenishment decisions.

02

Light manufacturing

Audit incoming-material lead times using purchasing history instead of assumptions that date back to ERP setup.

03

Importers

Separate consistently long lead times from highly variable ones. A planning parameter should reflect more than a simple average.

04

Quarterly supplier review

Bring objective lead-time evidence into supplier reviews without building another Power BI workbook.

A BETTER HOME FOR A FAMILIAR CALCULATION

The math fits in a spreadsheet.
The workflow usually doesn’t.

THE SPREADSHEET WORKFLOW

Export → Clean → Map columns → Normalize suppliers → Write formulas → Group → Calculate → Sort → Save → Forget → Rebuild

THE LEADTIMETRUTH WORKFLOW

Upload → Map → Audit → Review → Repeat

The value is not a new formula.
It is making the audit repeatable.

08 / MESSY EXPORTS ARE PART OF THE JOB

The audit should start
before the spreadsheet
is perfect.

Mapping and cleanup reduce the prep work. Missing or invalid business data is excluded and explained, not magically repaired.

AI can help clean the input.
Statistics tell the truth.

AI-assisted mapping and name normalization are product concepts. The local audit below uses explicit CSV fields and deterministic rules.

INPUT → AUDIT-READY FIELDS
Vendor Code→SUPPLIERPO_DT→ORDER DATEGR_DATE→RECEIPT DATEItem Code→SKU
ACME Ltd.
ACME LTD
Acme Limited→ACME
REVIEW MATCH
! 18% OF ROWS EXCLUDED
Missing receipt date112
Invalid date order24
Duplicate PO line11
Illustrative quality check · 147 of 817 rows

THE WORKSPACE / LESS HUNTING, MORE REVIEW

An exception queue.
Not another dashboard.

✳ LEAD TIME AUDIT / Q3 2026482 PARAMETERS CHECKED EXAMPLE
SKU-1044

ABC Industrial

ERP ASSUMPTION30 days
MEDIAN41
P8046
LAST 90D43
GAP+11
! HIGH VARIABILITY
OBSERVED HISTORY. EXPLAINABLE REVIEW VALUES.NO AUTOMATED ERP WRITE-BACK

FREE LEAD TIME AUDIT

You already
have the data.
See what it says.

Use a purchasing export to see which configured lead times deserve a closer look.

01 / TWO CSV FILES02 / DETERMINISTIC CALCULATIONS03 / A PRIORITIZED REVIEW LIST

This first audit runs locally in your browser. Your files are not sent to a server.

START YOUR AUDIT CSV → EVIDENCE
What should my CSV files contain?

PO history: PO ID, Supplier, SKU, Order Date, Receipt Date

Lead-time master: Supplier, SKU, Current Planning Lead Time

Use YYYY-MM-DD dates and calendar-day lead times. One row per completed PO line. Supplier and SKU identifiers must match. Blank, invalid, or repeated rows are excluded with a count.

Common field aliases such as Vendor Code, Item Code, PO_DT, and GR_DATE are also accepted. No automatic supplier merging.

OBSERVED HISTORY · NOT A FORECAST

09 / AFTER THE FREE AUDIT

Repeat the calibration
when reality changes.

Start with one useful check. Recurring audits are the next product hypothesis.

↳

Proposed packaging, not a subscription offer. No checkout or recurring charge.

FREE AUDIT

$0

An initial check of your planning assumptions.

  • Local CSV audit
  • Prioritized discrepancies
  • Evidence & review values
Audit my lead times

OPERATIONS PROPOSED

$39/ month

For making calibration a repeatable practice.

  • Recurring audits & history
  • Historical comparisons
  • Exports & supplier reports
PRIMARY PRICING HYPOTHESIS

WHEN SHOULD YOU RE-AUDIT?

Whenever supplier reality
has had time to move.

INITIAL AUDIT→ERP REVIEW→NEW PO HISTORY→RE-AUDIT→NEW DRIFT

Monthly. Quarterly. Before a supplier review.

The right frequency depends on how quickly your supplier performance changes.

THE PRINCIPLE IS SIMPLE

Planning systems
run on assumptions.
Assumptions need calibration.

LeadTimeTruth checks whether one critical input — supplier lead time — is still supported by observed purchasing history.

ASSUMPTION → OBSERVATION
CALIBRATION → REVIEW

10 / A FEW GOOD QUESTIONS

Before you
check the numbers.

01Is LeadTimeTruth an inventory planning system?

No. It is deliberately narrower: an audit of supplier lead-time assumptions using completed PO history, rather than a tool for forecasting, replenishment, or purchasing.

02Does it connect directly to my ERP?

The initial workflow is CSV-based. Export your purchasing history and current planning lead times. No ERP integration or implementation is required.

03How is actual lead time calculated?

Receipt date minus order date, in calendar days, for completed PO lines. Median, percentiles, variability, and period comparisons use deterministic statistics. The local audit displays its methods and thresholds with the results.

04Does AI choose my new lead time?

No. AI may help map and clean input data or explain findings in the broader product concept. Statistical calculations remain deterministic. Review values show their evidence, and your team chooses the policy. The local audit on this page does not use AI.

05Why show P80 or P90 instead of only the average?

An average can hide variability. P80 is the lead time at or below which approximately 80% of observed deliveries fall. P90 shows a higher percentile. Neither is a guarantee about future deliveries.

06Does LeadTimeTruth automatically update my ERP?

No. It identifies planning values worth reviewing. Your team decides what should change and updates the planning system through your existing process.

YOUR ERP HAS AN ANSWER

Check whether
reality agrees.

Upload your purchasing history and find the lead-time assumptions that deserve review.

Audit my lead times

Observed history. Explainable recommendations.
No planning-system replacement.