How to scrape logistics and supply chain data from SupplyChainBrain using the Minexa.ai extension
- Minexa.ai
- 7 days ago
- 5 min read
Supply chain data is everywhere on the web, but getting it into a spreadsheet without copying it line by line is a different story. This walkthrough shows how to pull structured article data from SupplyChainBrain, one of the most referenced logistics and supply chain publications, using the Minexa.ai Chrome extension.
The starting URL for this tutorial is the logistics service providers directory: https://www.supplychainbrain.com/directories/98-supplier-directory/topic/1254-logistics-service-providers-3pls
Watch the full tutorial first
Before going through the steps, watch the video below. It covers the entire extraction from opening the extension to exporting the final dataset. The written walkthrough below mirrors the same flow with screenshots and field explanations.
The problem: article data that is visible but not usable
SupplyChainBrain publishes hundreds of articles across freight forwarding, 3PLs, customs brokerage, and intermodal logistics. The data is all there on the page: titles, authors, publish dates, teaser text, images. But reading it in a browser and working with it as structured data are two completely different things. Copying it manually is slow and error-prone, and writing a custom scraper from scratch takes time most teams do not have.
Minexa.ai solves this by letting you point at the HTML container holding all that content and automatically identifying every data point inside it, no selectors or code required.
Step 1: open the target page and launch the extension
Navigate to the SupplyChainBrain logistics service providers listing in your browser. Once the page has fully loaded, click the Minexa.ai icon in your Chrome toolbar to open the extension panel.
The extension detects the current page automatically. Click 'I'm on the right page' to confirm you want to extract data from this URL.
Step 2: the pagination problem, already handled
SupplyChainBrain article listings span multiple pages. Manually tracking which page you are on, appending the right query parameter, and re-running a script for each page is a common friction point in supply chain data collection. Minexa.ai detects the pagination structure automatically and shows you which navigation element it identified.
Review the detected pagination logic, then click Continue to move to the next step.
Step 3: choose what to scrape
You have two options here. Scraping the list only gives you everything visible on the article listing page: titles, authors, dates, teasers, and image URLs. Scraping the list plus linked detail pages goes one level deeper, opening each article and extracting its full content. For most logistics intelligence workflows, the list-level data is enough to get started.
Select your preferred mode, then choose between simple and advanced scraping. Simple mode covers the majority of cases. Advanced mode is available if you need custom interactions like clicking tabs or expanding sections before extraction.
Step 4: highlight the data container
This is the core step. Rather than clicking on individual fields one by one, you select the single HTML element that wraps all the article cards on the page. Minexa.ai then discovers every data point inside that container automatically.
Once you confirm the container, click Create Scraper. Minexa.ai analyzes the structure and generates a reusable scraper with all columns identified and named. This typically takes a couple of minutes.
Step 5: review the extracted data points
After the scraper is created, you can browse all extracted columns using the next and previous navigation inside the extension panel. Here is a sample of what the extracted data looks like for SupplyChainBrain article listings:
[
{
"article_title": "Freight Audit and Payment: The Critical Anchor in a Hurricane of Supply Chain Disruption",
"article_link": "https://www.supplychainbrain.com/articles/44104-freight-audit-and-payment-the-critical-anchor-in-a-hurricane-of-supply-chain-disruption",
"author_name": "Better Supply Chains",
"publish_dates": [
{ "value": "May 15, 2026", "tag": "div", "type": "text" },
{ "value": "Better Supply Chains", "tag": "a", "type": "text" },
{ "value": "/authors/10304-better-supply-chains", "tag": "a", "type": "attribute" }
],
"image_source_url": "https://www.supplychainbrain.com/ext/resources/2026/05/14/SCB_BSC-FAP-Market-Radar-Thumbnail.webp?height=400&width=300",
"section_title": "Freight Audit and Payment: The Critical Anchor in a Hurricane of Supply Chain Disruption",
"element_class": "record article-summary "
},
{
"article_title": "Freight in 2026: The Trilemma That No Shippers Can Avoid",
"article_link": "https://www.supplychainbrain.com/articles/43952-freight-in-2026-the-trilemma-that-no-shippers-can-avoid",
"author_name": "Timothy O'Connell, Senior Vice President of Sales Operations and Marketing, Odyssey Logistics",
"section_title": "ENVIRONMENTAL",
"element_class": "record article-summary "
}
]What the key fields actually contain
The publish_dates field is one of the more information-dense columns in this dataset. It returns a structured array that encodes three distinct values per article card: the publication date as a text string inside a div element, the author display name as anchor text, and the relative author profile path as an href attribute. All three come back in a single traversable list, so you can extract whichever component you need without additional page requests.
The article_content field captures the teaser paragraph text but also includes inline span elements and their associated style attributes as separate typed objects. This means you can reconstruct the full visible teaser text by filtering for objects where the tag is a paragraph or span and the type is text, while discarding the style attribute entries if you do not need them.
The author_links and author_links_2 fields both surface the relative author profile path, but they originate from different DOM traversal contexts within the article card. For articles where the author is credited in two separate locations on the card, both fields are populated. For editorial pieces published directly under the SupplyChainBrain brand, both fields return empty.
The element_class field captures the CSS class string assigned to each article card wrapper. On SupplyChainBrain, cards that include a thumbnail image carry a different class value than cards without one, making this field useful for filtering your dataset by card type downstream.
The section_title field behaves differently depending on the card layout. For some articles it mirrors the article title, while for others it surfaces the editorial section label such as THINK TANK, SCB FEATURE, or ENVIRONMENTAL. Checking this field lets you categorize articles by content type without parsing the URL or making additional requests.
Step 6: complete the configuration and run
The job summary screen shows your configuration and gives you the option to connect a Google Sheet for direct export or set up a recurring schedule so the job re-runs automatically. Click Complete Configuration to save the scraper.
Your new scraping job appears at the top of the jobs list. Click Run to start extraction. Minexa.ai works through the paginated listing and collects all article records across every page.
Once the job finishes, the results appear in a table inside the Minexa.ai dashboard. Export to Excel or JSON depending on how you plan to use the data.
Reusing the scraper across topic pages
SupplyChainBrain organizes its content into dozens of topic categories beyond logistics service providers, covering areas like warehouse management, cold chain, last-mile delivery, and trade compliance. Because all topic listing pages share the same article card structure, the scraper you trained here works on any of them without modification. You just point it at a different topic URL and run the job again.
This is one of the practical advantages of training a scraper once on a page structure rather than writing per-URL extraction logic. The setup cost stays flat regardless of how many topic pages you need to cover.
If you want to go further and integrate this data into a programmatic pipeline, the Minexa.ai extension generates ready-to-run Python code directly from the scraper configuration. You can find that under the API Request button after the scraper is created.
For a related walkthrough on scraping news and article data at scale, see: How to scrape news articles for media monitoring.
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