S&OP response-time simulator v1.2

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
Days of demand your stock covers — absorbs slips before they hit the promise
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
$
Cost to save one at-risk order — expedite, substitute, reallocate
%
Share of at-risk orders you act on — substitute, reallocate, resequence, partial-ship
% sub
Substitution is fast and usually beats the promise; resequencing is slower and some misses the window. Higher = more substitution-led
resequencing-ledsubstitution-led

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

The prize

Close the update lag

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

$231krecoverable / year
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 — a full miss unless you recover it
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.
Net late orders / yr (after recovery)33
Orders recovered / yr49
Penalties / yr$39k
Recovery spend / yr$39k
Annualized cost$78k
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 your cover: P(miss) = e^(−(buffer + safety stock) / slip) → for week-scale slips this approaches 1.
OTIF drop before recovery: at-risk = f_bad × P(miss), on top of your baseline.
Operational recovery: you act on a share of at-risk orders (recovery rate) and only a fraction land in time, set by approach — effectiveness = 0.55…0.95 from resequencing-led to substitution-led. net miss = at-risk × (1 − rate × effectiveness). You pay for every attempt; only the ones that land avoid the miss, slip, and penalty.
Revenue slips the quarter when a net-failing order's delivery crosses close: cross ≈ (slip / 90) × 1.5 (end-of-quarter loading).
Annualized cost = penalties on net late orders + recovery spend on the saved orders.
Defaults are illustrative — overwrite them with your own OTIF, slip rate, and order economics. Impact only touches orders actually gated by a slipping material.
Recoverable revenue vs update lag annualized — the upside of closing the lag

The curve is the revenue you'd recover per year at each update-lag value; the dot is where you sit today, and the green drop to zero is what you'd gain by getting to same-day updates. Every other lever reshapes the curve.

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.

Disclaimer: This simulator is provided for illustrative and educational purposes only. All figures are model estimates derived from simplified assumptions and the inputs you supply, and may not reflect actual results. Nothing here constitutes business, financial, accounting, tax, legal, or investment advice, or any guarantee of outcomes. Any decisions you make are your own responsibility and should be taken in consultation with qualified professionals.