TL;DR

The layoff headlines this week are retail, not just tech. Nike reportedly announced cuts of roughly 1,400 roles (week of July 20, 2026), with the majority landing in technology positions across its global operations. Separately, Amazon is reportedly planning to temporarily close a warehouse in Homestead, Florida, eliminating more than 600 jobs, with cuts starting this month and continuing through September 2026. Layoff-tracking sites reportedly put the 2026 aggregate at 322 events and roughly 205,832 workers as of July 23 — an average of about 1,009 job losses a day industry-wide (treat that as a rough tracker estimate, not an audited figure).

Tech-sector layoffs get most of the press. Retail and warehouse cuts — the kind that gutted a distribution center in a mid-size metro, or quietly closed a regional facility — often get a single local news brief, if that, even though they hit local economies just as hard. The public paper trail for these is the same one recruiters use for tech: state WARN Act notices. This post walks through using ScrapeMaster to build a running tracker specifically for the retail and warehouse/logistics vertical — a different data source and a different audience than the tech-company trackers we've covered before.

  • Different vertical, same tool. State WARN pages, not tech-company press releases.
  • Free, local, no-code. ScrapeMaster is on the Chrome Web Store.
  • Public filings only. No login, no paywall, no bypass — WARN notices are published for public awareness.

Why retail and warehouse layoffs fly under the radar

Open any tech-layoff tracker and you'll find granular, near-real-time coverage: headcount by division, severance terms, effective dates down to the day. That coverage exists because tech layoffs get covered by a dense press corps that lives on Twitter/X and LinkedIn, and because tech companies issue polished press releases when they cut staff.

Retail and warehouse layoffs don't get that treatment, for a few structural reasons:

  • One event, one region. A 600-job warehouse closure in a single Florida city is a huge story locally — for the workers, the surrounding suppliers, the town's tax base — but it barely registers nationally next to an 8,000-person tech layoff spread across the news cycle.
  • Fewer dedicated reporters. There's no equivalent of the tech-layoff Twitter beat for regional logistics and retail-store closures. Coverage, when it happens, comes from local business journalists or trade press, not national outlets.
  • The filings are scattered by design. WARN Act notices are filed with state labor departments, not a single federal database. A retail chain closing distribution centers in three states means three separate filings on three separate state websites, none of which link to each other.
  • The workforce is less online in the aggregate sense trackers rely on. Tech-layoff trackers partly work because affected employees update LinkedIn within days, which aggregators can then count. Warehouse and retail-floor layoffs don't generate the same LinkedIn signal, so crowd-sourced trackers built for tech undercount this vertical almost by construction.

None of that makes retail and warehouse layoffs less real or less consequential — arguably the opposite, since a single facility closure can be the largest layoff event a small metro sees all year. It just means the data lives in a different place, and needs a different collection habit than "check the usual tech-layoff tracker sites."

What happened this week, in plain terms

To be clear about what is and isn't confirmed here: these are two separate, reportedly-announced events, cited from layoff-tracking coverage this week. We're not adding detail beyond what's been reported.

Nike: reportedly ~1,400 roles cut

Nike reportedly announced layoffs affecting approximately 1,400 roles during the week of July 20, 2026, with the majority described as technology positions across its global operations. Nike is a useful example precisely because it sits outside the standard tech-layoff narrative — it's a retail and apparel company, and even where the specific roles cut are technology roles, the event sits inside a retail company's broader workforce, not a pure-play software business. If you're only watching tech-company trackers, an event like this can slip past you.

Amazon: reportedly closing a Homestead, Florida warehouse

Amazon is reportedly planning to temporarily close a warehouse in Homestead, Florida, eliminating more than 600 jobs, with the cuts reportedly starting in July 2026 and continuing through September 2026. This is the textbook case for why WARN tracking matters for this vertical: a single-facility closure, in a specific city, affecting a defined headcount over a defined window — exactly the kind of filing that shows up in a state WARN notice long before (or instead of) a national news story.

The aggregate picture

Layoff-tracking sites reportedly show 322 layoff events in 2026 affecting roughly 205,832 workers as of July 23, 2026 — an average of about 1,009 job losses per day for the year so far. Treat this the way you'd treat any aggregator number: as a rough industry-tracker estimate assembled from press releases, WARN filings, and news reports, not a precisely audited census. It's directionally useful for "how big is this year's wave," not a number to cite to the decimal.

What WARN Act notices are, and why they're the right public source

The Worker Adjustment and Retraining Notification (WARN) Act requires many US employers to give advance notice — generally 60 days — before a mass layoff or plant closing above a certain size threshold. Those notices are filed with state labor or workforce agencies, and most states publish them as public web pages, typically a table listing:

  • Employer / company name
  • Facility location (often down to the city or specific address)
  • Number of employees affected
  • Notice date and effective date

For retail and warehouse layoffs specifically, WARN notices are often the best public source available, for a simple reason: a warehouse closure or a round of store-level cuts frequently doesn't get a dedicated press release the way a tech layoff does. Nobody at company HQ is issuing a polished announcement about closing one distribution center. The WARN filing may be the only structured, public record that the event happened at all — which company, which facility, how many people, and when.

This is also where the retail/warehouse vertical diverges from tech-layoff tracking in practice. Tech trackers lean heavily on press releases and aggregator sites (Skillsyncer, TrueUp, layoffs.fyi) because tech companies self-report loudly. For retail and logistics, you get more signal by going straight to the primary source: your state's Department of Labor WARN notice page. Most US states publish one; the exact interface varies by state, so start with whichever state's page you actually need and treat the table structure as something ScrapeMaster figures out per-site, not something you need to memorize in advance.

Step-by-step: building a retail & warehouse layoff tracker with ScrapeMaster

This is the same core workflow we've described for tech-focused WARN tracking, adapted for a vertical where state WARN pages — not company press releases — are the primary source.

Step 1 — Open your state's WARN notice page

Navigate to your state's Department of Labor (or equivalent workforce agency) WARN notice page — the public listing of filed notices. These are government pages: no login, no paywall, nothing to bypass. If you're tracking multiple states (useful if a retail chain or logistics company operates facilities across a region), open each state's page in its own tab.

Step 2 — Let ScrapeMaster auto-detect the table

Click the ScrapeMaster icon to open it in Chrome's side panel. It analyzes the page in a couple of seconds and auto-detects the repeating rows — no CSS selectors, no code required. For a typical WARN table you'll get columns like company/employer, facility location, number of employees affected, notice date, and effective date. Rename any column that comes through awkwardly named; ScrapeMaster's AI-detected columns are a starting point, not a final schema.

Because ScrapeMaster runs inside your actual browser tab, it works whether the state's page renders its table as static HTML or loads it client-side with JavaScript — many state portals do the latter, and it doesn't matter to the extension either way.

Step 3 — Paginate through the full filing history

State WARN pages are often long lists, sometimes paginated, sometimes an infinite-scroll feed. ScrapeMaster handles next-page buttons, load-more buttons, numbered pagination, and infinite scroll automatically, walking the full list rather than just what's visible on the first screen. Set a reasonable extraction delay — you're a guest on a public-sector site, and there's no reason to hammer it.

Step 4 — Filter for retail and logistics facility names

Once exported, filter your spreadsheet for the company names and facility types you care about — retail chains, distribution centers, fulfillment centers, warehouses, grocery and big-box store closures. This is manual spreadsheet work, not something ScrapeMaster does for you, but it's fast once the raw data is in one table.

Step 5 — Combine multiple states into one running sheet

Export each state's filtered results to CSV or XLSX (or copy directly to your clipboard for a paste into Google Sheets), then stack them into one master tracker with a state column. Add a date_pulled column too — this matters more for a running tracker than a one-time pull, since you're building a dataset you'll revisit.

Step 6 — Re-run periodically and diff against your last pull

WARN pages update as new filings come in; there's no push alert built into any state's page. Re-run the same extraction on a schedule — weekly is reasonable for most states — and compare the new export against your last saved pull. New rows are new filings. Over a few months, this turns a static snapshot into an actual trend line: which regions are seeing repeated retail closures, which quarters concentrate warehouse layoffs, and whether a single company (Nike, Amazon, or anyone else) shows a pattern across multiple states rather than one isolated event.

Who this tracker is actually useful for

The retail and warehouse vertical has a different reader than the tech-layoff trackers, and it's worth being explicit about who benefits:

Local journalists. A regional business reporter covering a single metro area often has no efficient way to know a distribution center five miles from their newsroom is filing a WARN notice this week. A running state-level tracker turns "did anything happen locally" into a five-minute spreadsheet check instead of a manual search of a government site nobody thinks to visit proactively.

Staffing agencies. Warehouse and retail staffing firms live and die by knowing where labor is about to become available — and where a client's own facility might be affected. A WARN-based tracker gives lead time that's genuinely useful for planning, not just for outreach.

Union representatives. For unions organizing or already representing retail and warehouse workers, knowing about a facility closure through official channels — rather than after the fact from members — matters for everything from grievance timing to coordinating with affected locals in other states.

Affected workers researching severance norms. If you're facing a layoff yourself, a tracker built from multiple companies' filings over time gives you something to compare against — which facility sizes tend to get what kind of notice, how far in advance filings tend to land relative to the announced effective date, and how your own situation compares to others in the same sector and region. It won't tell you severance dollar amounts (WARN filings generally don't include that), but the timeline and scale context is genuinely useful when you're trying to gauge whether your own employer's notice is standard or unusual.

Honest limits

  • It's a snapshot, not a live feed. You get data as of when you pull it. Catching new filings means re-running the extraction and diffing against your last pull — there's no automatic alert.
  • State formats vary widely. Fifty states means fifty different table layouts, and some publish PDFs instead of clean web tables. Auto-detect handles the common case well; a few states will need manual cleanup.
  • WARN coverage has thresholds. Not every layoff triggers a WARN filing — the Act covers mass layoffs and closings above specific size and notice-period thresholds, so smaller retail-store-level cuts may never show up here at all.
  • No bypass, no shortcuts. WARN notices are public filings with nothing to get around, which is exactly why this workflow stays clean — but it also means ScrapeMaster can't reach anything a state hasn't published in the first place.

Frequently asked questions

How is tracking retail and warehouse layoffs different from tracking tech layoffs?

Tech-layoff tracking leans on company press releases and crowd-sourced aggregator sites, because tech companies tend to announce cuts publicly and affected employees update LinkedIn quickly, which aggregators pick up. Retail and warehouse layoffs — especially single-facility closures — often skip the press release entirely and generate little LinkedIn signal, so state WARN notices become the primary, sometimes only, public record. The workflow is the same extraction tool, but the source you point it at is different: government filings first, rather than press coverage first.

Why did Nike's ~1,400-role layoff get less coverage than a similarly sized tech layoff would?

Retail companies making cuts inside internal functions — even technology roles — don't generate the same dedicated press-corps attention that a pure tech company's layoff does. There's no equivalent of the tech-layoff beat covering apparel and retail company staffing moves in the same granular, near-real-time way. That gap is exactly why building your own tracker from primary sources like WARN filings is worth the effort for anyone who actually needs this data.

Where do I find my state's WARN notice page?

Search for "[your state] Department of Labor WARN notice" or check your state's workforce development or labor agency website directly — most states publish a public page listing filed notices. The specific layout and navigation differs by state, so there isn't one universal link; treat it as a per-state lookup, and let ScrapeMaster's auto-detect handle whatever table format that state uses once you're on the page.

Can ScrapeMaster tell me why Amazon is closing the Homestead warehouse, or other reasons behind a filing?

No. ScrapeMaster extracts what's visibly published in the WARN table or on the page you're viewing — typically employer, facility location, headcount, and dates. WARN filings themselves don't usually include a detailed rationale, and the extension doesn't infer or add context beyond what's on the page. For the "why," you'd still need press coverage or a company statement, treated as a separate, hedged source.

How often should I re-run the extraction to catch new retail and warehouse filings?

Weekly is a reasonable cadence for most states — WARN filings don't arrive constantly, and a weekly diff against your last pull will catch new rows without hammering a public-sector site. If you're tracking a specific company you expect to file across multiple states over several months (a multi-facility retail chain, for example), weekly pulls also let you see the rollout pattern rather than just one snapshot.

Is this useful if I'm not a recruiter or journalist — say, I just got laid off from a warehouse or retail job myself?

Yes. A tracker built from multiple companies' WARN filings over time gives you comparison points: how far in advance similar-sized facilities typically filed relative to their effective date, how common closures are in your region and sector right now, and whether your own employer's notice looks standard or unusual next to others. It won't surface severance dollar amounts — WARN filings don't generally include those — but the scale and timing context is genuinely useful when you're trying to make sense of your own situation.

Does this only work for tracking Nike and Amazon, or can I point it at any retail or logistics company?

Any company that files a WARN notice in a state you're monitoring will show up in your extraction — Nike and Amazon are this week's examples, not a limitation of the tool. Once you've got a state's WARN page set up in ScrapeMaster, the same extraction captures every employer filing in that state, and you filter your spreadsheet down to the companies or facility types you actually care about.

Bottom line

Retail and warehouse layoffs — Nike's reported ~1,400 roles this week, Amazon's reported Homestead, Florida warehouse closure, and the roughly 322 events and 205,832 workers layoff trackers reportedly count for 2026 so far — get a fraction of the press attention that tech-sector cuts do, even though a single facility closure can be the biggest layoff event a local economy sees all year. State WARN Act notices are the public paper trail this vertical actually runs on, and ScrapeMaster turns those scattered, differently-formatted state pages into one running tracker — free, local to your browser, no coding required.

For the tech-sector side of this story, see our trackers on the May 2026 tech layoff wave, the Q1 2026 119K tech layoffs tracker, and the general WARN notice scraping workflow. If you're also watching which companies are hiring while others cut, pair this with monitoring careers pages for the hiring rebound.

Install ScrapeMaster free from the Chrome Web Store and start building your retail and warehouse layoff tracker this week.