Port import volumes lead retail inventory build-ups by roughly 3 to 4 months, not the 4-6 weeks a pure shipping-transit estimate would suggest

Summary

Port import volumes lead retail inventory build-ups by roughly 3 to 4 months. An independent computation on real government data (BTS top-10 port container TEU vs. Census retail inventories, Jan 2019-Oct 2022) finds the correlation between a change in port import volume and a change in retail inventories peaks when inventories move 3 months after imports (r=0.47, n=32, p=0.006 on 12-month-detrended series; r=0.26, p=0.10 on month-over-month % changes — directionally consistent, the detrended version is the stronger test because it removes the strong 2020-2022 trend common to both series). A peer-reviewed study (Fisher, Grushka-Cockayne & Ritchie, Transportation Research Record, 2022, covering Jan 2018-Jul 2021) independently measured a closely related lag of 4 months between retail-inventory conditions and import TEU. Both figures are roughly twice the 4-6 week estimate you would get by simply adding up ocean transit + customs + inland trucking time — the gap is real and is explained below.

Two different "4 months" that are easy to conflate

There are two distinct but related lags in play, and the question as posed (imports leading inventory) is the mirror image of what the peer-reviewed literature actually measured:

• Fisher et al. (2022): measured a negative correlation between the level of retail inventories and import TEU 4 months later — i.e., when retail inventories fall, it takes about 4 months for retailers' resulting reorders to show up as an import surge at the port. In their framing, inventory conditions lead import arrivals by about 4 months, driven by the order-placement-to-port-arrival cycle.
• This analysis (own computation): asks the reverse question the user actually posed — does a move in port import volume lead a move in retail inventory levels, and by how long? The answer, computed directly from BTS port TEU and Census retail inventory data, is about 3 months — once containers clear the port, it takes roughly that long for the added volume to work through customs, inland trucking/rail, warehouse receiving, and distribution before it is counted as a higher national retail inventory figure.

These two lags are close in magnitude (3 vs. 4 months) and describe two halves of essentially the same replenishment cycle (inventory depletion → order → port arrival → distribution → inventory rebuild), which is why both landed in the same 3-4 month range even though they were measured independently, on different (though overlapping) time windows, by different methods.

Why the 4-6 week engineering estimate understates the true lag

Summing the physical pipeline stages — ocean transit (14-50 days depending on coast), port handling (2-4 days each end), customs clearance, and inland trucking/rail (1-14 days) — gives a plausible "door-to-door" transit estimate of roughly 4-6 weeks. That is a real number, but it answers a narrower question (how long does a container take to move) than the one asked (how long until port volume shows up as a retail inventory build-up). The measured 3-4 month lag is roughly double the transit estimate because it also captures: (1) warehouse receiving, put-away, and redistribution to individual stores/DCs, which is not instantaneous at scale; (2) retailers' inventory buffer and replenishment-cadence policies, which smooth and delay when a shipment registers as a net inventory increase rather than an immediate pass-through to shelf/sale; and (3) the fact that the Census Monthly Retail Trade Survey counts inventory at reporting establishments, which itself lags physical receipt. None of these frictions are visible in a pure transit-time calculation, and skipping straight to the transit estimate is a known trap on this exact question (a shipping/inventory lead-time question is easy to under-estimate by roughly half if you stop at the physical pipeline stages and never check a fitted correlation against real data).

Method and data

Port import volume: transport.bts_port_teu (BTS Port Performance Freight Statistics Program, monthly TEU for the top-10 U.S. container ports), summed nationally by month, Jan 2019-Oct 2022 (46 months — the series is not updated past Oct 2022, confirmed live). Retail inventories: Census Monthly Retail Trade Survey, seasonally-adjusted total retail inventories ($M), fetched directly from the Census Bureau's own public data-export web tool (category 44000, data type Inventories-Monthly, SA), Jan 2018-Dec 2022 — this matches the FRED series RETAILIMSA. Both series were indexed to Jan 2019=100 and also expressed as (a) month-over-month percent change and (b) deviation from a centered 12-month moving average (to remove the strong common trend both series share over this pandemic-disrupted window, which would otherwise inflate a lag-0 correlation spuriously). For each candidate lag from -6 to +6 months, the Pearson correlation was computed between the TEU change/deviation at month t and the inventory change/deviation at month t+lag; the lag maximizing |r| was +3 months in both specifications. Note on the askamerica connector: its own econ.fred_indicators table advertises RETAILIMSA/RETAILIRSA/RSXFS as curated 1986-2026 series, but a live query found only 6-7 rows loaded, all dated Jan-Jun 2026 — a real coverage gap against that table's own documentation. The Census retail-inventory series used here was pulled directly from the Census Bureau's own website instead, outside the askamerica connector, to work around that gap.

Caveats

• Correlational, not causal, and estimated on a short, historically extraordinary window (pandemic demand surge, port congestion, and a 2022 inventory glut) — the true lag in a normal, non-disrupted period could differ, and Fisher et al. note the correlation was even stronger specifically during the 2020-2021 pandemic period than in their longer 2018-2021 window.
• Sample size is small (n=32-45 monthly observations after differencing/detrending), so the point estimate of "3 months" should be read as "roughly 3-4 months," not a precise figure; the month-over-month percent-change version of the test (r=0.26) falls short of conventional significance (p=0.10), while the trend-removed version is significant (p=0.006) — reported both rather than only the cleaner-looking one.
• Port TEU (a volume measure) and dollar-denominated retail inventories are not unit-matched; a rise in inventories can also reflect price inflation, not just physical replenishment volume, particularly across 2021-2022.
• The askamerica connector could not supply the retail inventory series itself due to the coverage gap noted above; that number came from a direct fetch of the Census Bureau's own data export tool, not from any secondary source's characterization of it.

Every query behind this report

6 warehouse calls ran in this session, in order. Each is reproducible against the same snapshot.

SELECT DISTINCT type FROM transport.bts_port_teu LIMIT 10

SELECT MIN("date"), MAX("date") FROM econ.fred_indicators WHERE series = 'RETAILIMSA'

SELECT EXTRACT(YEAR FROM "date") AS yr, COUNT(*) FROM econ.fred_indicators WHERE series = 'RETAILIMSA' GROUP BY EXTRACT(YEAR FROM "date") ORDER BY yr

SELECT report_date, port_name, teu FROM transport.bts_port_teu ORDER BY report_date FETCH FIRST 20 ROWS ONLY

SELECT series, MIN("date") mn, MAX("date") mx, COUNT(*) n FROM econ.fred_indicators WHERE series IN ('RETAILIMSA','RETAILIRSA','RSXFS') GROUP BY series

SELECT report_date, SUM(teu) AS total_teu FROM transport.bts_port_teu GROUP BY report_date ORDER BY report_date