S&OP response-time simulator

What a stale delivery date costs you at quarter-end.

Revenue is recognized when the customer takes delivery. So the days a changed material date sits un-updated in your ERP don't just dent a KPI — they push promises past their dates, break OTIF, and slide proof-of-delivery into next quarter. Move the sliders to your reality and watch it flow through.

The lever you control
Update lag — supplier date change → ERP 5days
Your reality disturbances
wk
Order to material on the dock — long pipes breed large slips
%
Share of inbound POs whose date moves each quarter
%
A typical slip as a share of lead time (25% of 8 weeks ≈ 2 weeks)
%
Orders whose promised date waits on a slipping material
days
Slack you build into the dates you promise customers
days
Horizon over which a promise can be corrupted by the lag
Your economics days → $
Customer orders you fulfill each quarter
$
Revenue recognized per order at delivery
%
Where you sit before the date-update lag
%
Where penalties or contract triggers kick in
%
Chargeback as a share of order value
$
Extra freight per order you rush to save
%
Share of expedited orders that arrive on time

Sliders are a guide — type any value in the number fields, even beyond the slider range.

The chain, live
97.5%
promise accuracy
91.4%
resulting OTIF
1.6
OTIF points lost
Supplier delivery date slips
Uncontrollable — and grows with lead time
+7d25% of POs · 4wk lead
YOUR LEVER
Procurement date-update lag
Stale date sits in ERP, feeding ATP & MRP
5 daysupdate latency
Promise made on stale ATP
Customer date set too optimistically
97.5%promise accuracy
Material late → OTIF miss
Binary: one day late counts as a full fail
91.4%OTIF
POD slips past quarter close
Revenue recognized in the next quarter
$38kslips / qtr

Output A · Revenue timing

$38.0k/ quarter
Deliveries that fail near close, pushing proof-of-delivery — and the revenue — into the next period.
Annualized slip$152k
Orders crossing close / yr4.1

Output B · Service & cost

1.6OTIF points lost
Pulls you below your customer's threshold — penalties apply.
Late orders / yr (this cause)33
Penalties / yr$39k
Expedite spend / yr$39k
Annualized cost$78k

The prize

Close the update lag

Everything above is attributable to the days a changed date sits un-updated. Take the lag to same-day and this modeled impact goes to zero — revenue lands in-quarter and the cost disappears.

$231krecoverable / year
How this is calculated
Typical slip scales with lead time: slip = volatility × lead time — week-long lead times breed week-long slips.
Corrupted promises rise with the lag: f_bad = gated% × slip-rate × min(1, d / window)
A corrupted promise misses OTIF when the slip beats the buffer: P(miss) = e^(−buffer / slip) → for week-scale slips this approaches 1.
OTIF drop from this cause: loss = f_bad × P(miss), applied on top of your baseline.
Revenue slips the quarter when a failing order's delivery crosses close: cross ≈ (slip / 90) × 1.5 (end-of-quarter loading).
Annualized cost = penalties on late orders + expedite spend on the ones you rush to recover.
Defaults are illustrative — overwrite them with your own OTIF, slip rate, and order economics. Impact only touches orders actually gated by a slipping material.

Nordoon models internal response time as the one lever a business actually controls — external market and price signals are excluded by design. This simulator isolates a single mechanism (procurement date-update latency); the full model chains all five operating functions.