Freight invoice lag in corporate finance, by contract horizon. Includes the M1, M2 and M3 repricing windows, the asymmetry that holds annual contracts at peak rates after the market falls, what the lag means for budget forecasting, and how sirius maps pressure onto all three horizons, explained by the team behind the sirius freight cost index.
There is a paradox at the centre of freight cost management that most corporate finance teams have encountered without fully naming. Market intelligence arrives in near real time: rate indices publish weekly, broker reports arrive daily, and a chokepoint event generates commentary within hours of the first transit restriction. Yet the actual cost impact, the number that ends up in your accounts payable, lags all of that by weeks, sometimes by a full quarter.
The result is that budget variances arrive as surprises. A procurement team that was watching every signal correctly still faces a CFO asking why freight cost was 15% over plan in Q2, when the freight market events that caused it were visible in January. The answer is almost always the same: the signals were visible, but the contractual mechanism that translates signals into invoices introduced a delay that the budget cycle did not account for.
Understanding that delay is the first step toward closing it. The M1/M2/M3 invoice lag framework describes why it exists, how long it runs in each contract type, and what it means for forecasting.
Table of Contents
- What are the three freight contract horizons?
- Why does the invoice lag asymmetry matter?
- What does invoice lag mean for freight forecasting?
- How does sirius model the invoice lag?
What are the three freight contract horizons?
Freight contracts do not all reprice on the same schedule. The market operates across three distinct contract horizons simultaneously, each with its own repricing rhythm, and most companies hold exposure across all three at once, often without a consolidated view of which share of their freight volume sits in each tier.
The first horizon is spot and short-term contracts, referred to in the sirius model as M1. These are agreements with repricing windows of roughly 30 days or less. When a carrier declares a surcharge or the spot market moves, M1 exposure reprices on the next booking cycle. The freight cost impact of a market event reaches M1 invoices within a month. This tier offers the least protection against volatility but also the fastest feedback loop: a drop in pressure is reflected in invoices almost as quickly as a spike.
The second horizon is quarterly contracts, M2. These agreements fix rates or establish rate bands for a three-month period, with review clauses at each quarterly renewal. A market event in week one of a quarter may not reach M2 invoices until the following quarter's repricing cycle, a lag of 30 to 60 days from the market event to the invoice. Carriers frequently invoke force majeure or exceptional surcharge clauses during sustained disruptions, which can accelerate M2 repricing, but the base rate typically holds until the contractual review date.
The third horizon covers annual and long-term contracts, M3. These are the agreements most procurement teams prize for budget stability, and the ones that create the longest invoice lag. Annual contracts reprice once per year at renewal. A market event that occurs six months into a contract year may not reach the invoice until the following year. In practice, M3 contracts rarely provide complete protection during sustained disruptions, because most include clauses allowing carriers to pass through documented surcharges. But the base rate component, often the largest part of the invoice, does not move until renewal. That repricing lag can run to 90 days, and in some contract structures, longer than six months.
Why does the invoice lag asymmetry matter?
The three horizons do not behave symmetrically when market conditions change. This is the part of the lag model that causes the most damage to budgets.
When freight costs spike (a disruption event, a surge in demand, a fuel cost jump), the pressure builds in days. Spot rates move quickly. Emergency surcharges are declared within weeks. The market is pricing the new reality almost immediately. M1 invoices follow within a month.
But when that pressure resolves, the sequence does not reverse at the same speed. Annual contracts that repriced upward during a disruption continue to run at the elevated rate until the next renewal date. A company whose M3 contracts repriced at the peak of a disruption may still be paying peak-era rates a full quarter after spot prices have returned to their pre-crisis level. The market recovered. The invoice did not.
This is the asymmetry: pressure builds faster than it dissipates through the contract stack. A crisis that lasts eight weeks in the spot market can produce invoice effects that persist for six months in M3 portfolios. It also means that companies entering a period of falling rates with predominantly annual contracts will systematically overpay relative to the market for the remainder of their contract year, a variance that is entirely predictable if you are tracking the pressure model, but invisible if you are only watching the invoice.
Budget implication
Annual budget cycles that are built on forward rate projections often fail to account for which contract horizon those projections apply to. A forecast that rates will fall 10% next quarter is accurate for M1 exposure. It will not reach M3 exposure until the renewal cycle, which may fall in a completely different fiscal quarter.
What does invoice lag mean for freight forecasting?
The direct consequence of invoice lag is that using current freight rate indices as a proxy for your current freight cost is almost always wrong. Rate indices tell you where the market is today. Your invoice reflects where the market was 30 to 90 days ago, filtered through whichever contract horizon the shipment fell into.
Watching rate indices is watching the past with a delay attached. A logistics manager who tracks SCFI weekly has a precise picture of where spot rates are moving. What that does not tell them is what percentage of their freight volume is exposed to today's rates versus locked into a contract that will reprice in two months. Without that overlay, the rate signal is incomplete as a forecasting input.
Closing the gap requires two things. First, a contract horizon map: a clear view of what share of freight volume is in M1, M2, and M3 at any given time, and when the next repricing events fall. Second, a forward pressure signal: an estimate of where freight cost pressure is heading over the next 30 to 90 days, not just where it has been. Combining those two inputs allows a procurement or finance team to estimate not what the market is doing today, but what will appear on invoices in the next one to three billing cycles.
That is the forecasting window that matters for budget management. Not the current rate. The future invoice.
How does sirius model the invoice lag?
The M1/M2/M3 invoice lag model is one of the core analytical layers in sirius. For each of the 10 trade lanes in the index, sirius tracks estimated cost pressure not just at the current point in time but offset across the three contract horizons. The effect is three forward curves per lane: what M1-exposed freight will likely cost next month, what M2-exposed freight will cost next quarter, and where M3 renewals coming up in the next 90 days are likely to land relative to the current pressure environment.
This is not a rate prediction model. sirius does not attempt to forecast what the SCFI or WCI will print next Friday. What it models is the pressure trajectory (the directional signal from fuel costs, capacity tightness, surcharges, FX, chokepoint throughput, and environmental cycles) and maps that trajectory onto the three contract horizons. A company with 60% M2 and 40% M3 exposure can use that output to estimate which portion of their freight cost is insulated from current market conditions, which is exposed within the quarter, and what pressure is building toward the next set of renewal conversations.
For a full description of the lag model and how it is calculated, see the invoice lag methodology and the M1/M2/M3 framework documentation.
