APProjects/us-warehouse-trucking-logistics-layoffs-warn-act-notices-daily

US warehouse, trucking and logistics layoffs — the actual WARN Act filings, rebuilt every day

Last rebuilt: 2026-09-16. 1,882 layoff and closure notices filed by warehouses, fulfillment and distribution centers, trucking and LTL carriers, freight forwarders and 3PLs, parcel and courier networks, school-bus and transit contractors, ports and rail, and parking operators with US state labor departments — 226,255 workers, 792 employers, 45 states, 1990–2026. 623 of the notices (33.1%) were recorded by the state as a closure rather than a layoff. Free, CC BY 4.0, no login, no delay.

A distribution-center closing or a carrier shutting a terminal is reported as a local story, if at all; the filings behind those stories — which site, which town, how many workers, notice date, effective date, closure or layoff — sit on 48 separate state portals and are never assembled as one logistics series. This file is that series, with the closure/layoff split the states themselves recorded. An automated pipeline re-scrapes 48 state portals every day and rebuilds this file from the legal filings employers must make before a mass layoff: employer, state, site, workers affected, notice date, effective date, and the notice type in the state's own words.

Read this before you quote a number

This is a FLOOR, not a census. No US state WARN portal publishes an industry field — not one. Sector here is assigned from the employer name by an auditable keyword rule table, and 47.4% of all 60,955 notices in the archive carry a name that reveals no sector at all (Rugged Liner, KPR US, Point Designs). A warehouse, trucking and logistics employer trading under an opaque name is therefore missing from this file. Every row keeps sector_rule — the exact pattern that fired — so any inclusion can be checked or disputed row by row, and the full unfiltered archive is one click away: all 60,955 notices.

WARN also only covers layoffs above a size threshold (broadly 50+ at a site, lower in some states), so smaller warehouse, trucking and logistics layoffs never generate a filing at all.

What counts as logistics here: the employer name reads as a logistics, trucking, freight, warehouse, distribution-center, fulfillment, supply-chain, courier, transit, bus, rail, port or parking operator, or is one of ~80 named carriers and networks (Amazon, FedEx, UPS, XPO, DHL, GXO, Ryder, First Student, MV Transportation, Transdev, LAZ Parking…). Amazon is here in full — its WARN filings are almost all fulfillment and delivery sites — and so are school-bus and transit contractors, which are the largest single group after warehousing. Airlines are deliberately NOT here (they sit with aerospace and defence in the sector dataset), and a manufacturer's own distribution arm lands here only if the word is in its name. Every row keeps the sector_rule that fired, so any inclusion can be disputed row by row.

"Closure" is the state's word, not ours. notice_type is copied verbatim from the filing; the closure count above is every row whose type contains clos (Closure, Closure Permanent, Plant Closing…). 403 rows have no type because that state's portal does not publish one.

Files

file	rows	what it is
data/logistics_layoff_notices.csv	1,882	every warehouse, trucking and logistics WARN notice, one row each
data/logistics_layoffs_by_employer.csv	792	per-employer history with closure count, biggest first
data/logistics_layoffs_by_year.csv	37	notices, closures, workers and employers per year
data/logistics_layoffs_by_state.csv	45	notices and workers per state

import pandas as pd
df = pd.read_csv("https://huggingface.co/datasets/APProjects/us-warehouse-trucking-logistics-layoffs-warn-act-notices-daily/resolve/main/data/logistics_layoff_notices.csv")
df.groupby(df.notice_date.str[:4]).employees_affected.sum().tail(10)

Warehouse, trucking and logistics layoffs by year

year	notices	of which closures	workers affected	employers
2026	193	75	23,761	120
2025	256	64	26,257	150
2024	187	70	20,459	82
2023	162	65	20,354	72
2022	99	32	9,984	45
2021	91	28	11,671	54
2020	215	42	30,169	132
2019	127	54	18,743	64
2018	66	21	8,102	43
2017	55	22	6,407	33
2016	58	30	8,282	41
2015	49	17	4,893	35
2014	42	9	4,522	34
2013	26	6	4,205	19
2012	20	8	2,118	16

The employers with the most warehouse, trucking and logistics layoff workers on file

employer	notices	closures	workers	states	first	last
Amazon	80	11	15,030	CA, FL, IN, KS, MD, NJ, NV, NY, OH, PA, SC, VA, WA	2009-03-25	2026-08-31
First Student	84	38	9,451	AK, CA, CO, CT, IA, ID, IL, KS, MI, MO, NE, NJ, NY, OH, OR, PA, RI, TX, WA	2004-04-26	2026-06-17
MV Transportation	63	10	8,468	CA, CO, FL, IL, IN, KY, MD, MI, NC, NV, NY, OH, TX, VA, WA	2011-10-03	2026-07-30
FedEX	45	13	6,940	CA, CT, FL, GA, HI, IN, MD, MO, MS, NC, NJ, NY, OH, OR, PA, SC, TN, TX, VT	2002-01-03	2025-12-29
XPO Logistics	28	6	6,929	CA, CO, IA, IL, KS, MD, MO, ND, NJ, PA, TN, TX, WA	2016-01-22	2021-07-01
First Transit	54	24	6,683	CA, CT, FL, IL, IN, MD, MN, NC, NJ, NV, NY, OH, OK, OR, PA, TN, TX, UT, VA, WA, WI	2008-12-31	2026-04-28
DHL Supply Chain	45	11	6,325	CA, FL, GA, IL, IN, MA, MI, NC, NJ, OH, PA, SC, TX, WI	2017-02-02	2026-07-07
LAZ Parking California	6	0	4,107	CA	2020-03-30	2026-08-06
Transdev Services	22	7	4,093	AZ, CA, FL, MD, NC, ND, NJ, WA	2017-01-01	2026-06-26
UPS	22	7	3,892	CA, CO, CT, FL, LA, MD, NC, OK, OR, RI, UT, VA, WI	2016-11-16	2025-07-10
GXO	35	17	3,397	CA, GA, IL, IN, MD, MS, NY, OH, PA, TX, WI	2022-01-31	2026-07-14
Ryder	34	11	3,063	AZ, CA, IL, IN, MD, MI, NC, NJ, NY, OH, PA, TN, WI	1997-06-13	2026-06-08
CEVA Logistics	27	9	3,003	CA, FL, GA, IN, MI, MO, NY, OH, PA, SC, TN, TX, WA	2010-03-17	2024-02-02
New England Motor Freight	17	9	2,919	CT, IL, MD, NJ, NY, OH, PA	2019-02-01	2019-03-04
Penske Logistics	25	8	2,660	CA, IL, IN, KS, MI, MO, NJ, NY, PA, TX, WA	2000-04-25	2025-09-09

States with the most warehouse, trucking and logistics layoff workers on file

state	notices	workers
CA	495	46,675
IL	161	20,087
NY	162	16,087
PA	112	15,560
NJ	63	11,961
WA	37	10,990
TX	86	10,733
MD	58	9,777
MI	75	7,913
OH	62	6,740

Most recent warehouse, trucking and logistics filings in this build

date	employer	state	location	notice type	workers
2026-10-31	CJ Logistics America	PA	Newville, Cumberland	Closure	56
2026-10-16	CJ Logistics America	PA	Breinigsville, Lehigh	Closure	57
2026-09-16	Transform Warehouse Operations	PA	Fairless Hills, Bucks	Closing	147
2026-09-11	4XH Logistics	TX	San Antonio, Bexar	—	230
2026-09-08	SP+	CO	Adams	Loss of Contract	73
2026-08-31	Amazon	WA	Seattle, Bellevue, Sumner	Layoff	121
2026-08-31	GEODIS Logistics	PA	Carlisle, Cumberland	Closure	185
2026-08-31	Amazon (SJC13)	CA	Santa Clara County	Layoff Permanent	70
2026-08-31	Amazon - SF019	CA	San Francisco County	Layoff Permanent	78
2026-08-11	Dylan Logistics LLC (Lewisville)	TX	Lewisville, Denton	—	97
2026-08-11	Dylan Logistics LLC (Fort Worth)	TX	Fort Worth, Denton	—	70
2026-08-09	WILX Logistics	PA	Mount Joy, Lancaster	Closure	70

Names are resolved for spelling variants, not corporate parents: a subsidiary filing under its own name stays under its own name.

Where this comes from

• Source: official state WARN listings, scraped daily and normalized to one schema. Full method, per-state coverage and freshness: https://approjects-warn-act-notices.static.hf.space.
• Public commit log of every daily rebuild: https://github.com/APVentureEngine/warn-act-notices.
• Whole archive, all sectors: https://huggingface.co/datasets/APProjects/us-warn-act-layoffs-notices-daily.
• All 20 sectors, not just this one: layoffs by industry sector.
• Verify anything that matters at the state source before you publish it.

If this saved you a scrape

• Like this dataset (the heart, top right) — likes are how the next person searching for warehouse, trucking and logistics layoffs finds a file that was rebuilt today instead of one uploaded once years ago.
• Watch the source repo's releases — one notification each day the snapshot is republished.
• Corrections and questions: open an issue.

Published by APProjects, an automated publisher of US public records. Not affiliated with any state agency.

A layoff record you can audit, not just download

This dataset is one cut of a single daily rebuild: 61,330 US WARN Act layoff notices from 48 state agencies, 1988 to today, one schema, no login, no delay, CC BY 4.0. Snapshot as of 2026-09-17; the files above are rebuilt every day, so the live count is the truth.

Several projects publish a current WARN scrape and two of them carry more rows than we do. None of them publish what the records used to say:

• 617 observed changes to already-published notices, logged daily since 2026-08-31. data/revisions.csv records every field that differed between two consecutive daily builds — employee counts, effective dates, notice types, company names — with the old value, the new value and the date we saw it. We publish the observation and not the cause: a change is equally explained by the agency amending the notice or by our own parser improving, and we do not guess which (see data/revisions.README.txt). A scrape that starts tomorrow cannot backfill any of it; it only exists if someone was watching.
• 6,799 notices whose state agency page no longer lists them. Agencies take notices down. We keep them, flagged as archive-only, so a count you ran last year still reconciles.
• Point-in-time employer identity. The ticker crosswalk resolves a filer to the company as it existed at the time of the notice — Kmart, Sears Holdings, Symantec — not to whatever is on today's ticker file.

If you have to defend a number to an editor, a referee or a compliance reviewer, that provenance layer is the part you cannot rebuild yourself. How to cite this dataset →

Look something up right now — free, no signup, nothing to install. Check any employer or state against the last 180 days → It runs in your browser against these same files.

Building something with it? The same files are a free HTTP API — JSON and CSV, no key, no signup, access-control-allow-origin: * so fetch() works from a browser: endpoints, schema and curl examples →

Need one industry only? The same filings, cut by an auditable employer-name rule (each row keeps the rule that fired): tech companies · hospitals & healthcare · retail store closings · restaurants & hotels · factory & plant closings · banks, insurance & finance · warehouses, trucking & logistics · all 20 sectors.

Or have it watch a list for you. Coming back to look is the part a CSV cannot do. WARN Watch — $49 for a year, one payment, nothing auto-renews, 14-day refund, no login: up to 500 employer names plus whole states, matched on every daily refresh, delivered to a private alert page + calendar (.ics) + RSS + an optional Slack / Discord / Teams webhook. Every alert carries that employer's whole filing history from the archive, which a keyword rule on an RSS feed cannot see. There is no built-in email — we do not claim one.

• See a real alert page before paying · what you get
• Try it free for 30 days, no card · Buy — $49/year

Not deciding today? Join the update list → — one email when a new dataset or tier is published; nothing promotional. A state added or a column renamed ships in the daily release instead, no address needed. The list is shared across APProjects datasets, holds an email address only, is run by Gumroad, and any message unsubscribes you. Rather give no address at all? Watch the repo's releases — GitHub notifies you on every daily republish, and a new state or changed field is in those notes the day it lands.

Reaching a human. WARN Feed is published by APProjects, an automated data publisher — that is stated plainly rather than dressed up. Corrections, coverage gaps, schema questions and refund requests all go here and are read: open an issue. Payments are handled by Gumroad as merchant of record, so an invoice can carry your company name.

Source, scrapers and methodology · the 48-state site

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