Build a Logistics Control Tower for High-Stakes Assets with Amazon Quick Automate
Discover how Amazon Quick Automate transforms logistics tracking for utility-scale energy projects automatically navigating carrier portals, extracting live shipment data, and consolidating it into a single operations dashboard so project managers can act before delays become costly crises.
Overview
Logistics visibility for high-stakes assets like 100-meter wind blades or house-sized generators is one of the hardest problems in global supply chains. Tracking data exists across dozens of carrier websites, each with its own login and layout, and there moght not be a single view of where things are. By the time you discover a weather delay or port congestion, you've already accumulated demurrage fines.
In this post, you learn how to build a logistics control tower using Amazon Quick Automate to track high-stakes shipments across multiple carrier websites. You walk through the end-to-end workflow: downloading a shipment manifest from Amazon S3, using UI agent to extract live tracking data, and combining enriched results into a single operational dataset.
Instead of spending hours on manual carrier lookups, you get unified visibility with a single automated workflow.
The Solution: Quick Automate as a Logistics Control Tower
Navigate carrier websites automatically with Quick Automate to extract live shipment intelligence and aggregate it into a single operations dataset you act on immediately.
Prerequisites
- Amazon Quick Suite subscription with Enterprise subscription plan which includes Quick Automate.
- An Amazon S3 bucket containing logistics-master.xlsx (referred to as the input file throughout this post) at the path shipping-tracking. This input file with at minimum a Carrier column and a Tracking Number column
- Access to a test carrier website to validate this workflow. This post uses a sample carrier environment (Not provided in this blog). You can also point the workflow at your own carrier websites with no changes to the automation logic.
- An AWS account with an AWS Identity and Access Management (IAM) role that has read and write permissions on your S3 bucket.
Input
For simplicity, this demo routes all login pages to a single sample website. In a real-world deployment, each carrier has its own distinct website. Make the solution fully dynamic by adding a Portal URL column to the input spreadsheet, allowing each shipment row to target a different carrier website without any changes to the workflow logic.
logistics-master.xlsx
Workflow flow:
S3 Manifest Download → Read Shipment Data → Add Output Columns → Login to Carrier Portal → [For Each Shipment] Scrape Live Status → Write Enriched Data → Upload to S3 → Operations Dashboard 
High-Level Steps
Step 1 - Download Tracking Info spreadsheet from S3
Step 2 – Lookup tracking info from Websites
Step 3 – Updating data to S3
Workflow Summary (Step-by-Step Breakdown)
The workflow runs in three high level steps. The table below talks about a few individual steps.
| Step | Action | Purpose | Input | Output |
|---|---|---|---|---|
| 1 | S3 Download | Retrieve logistics manifest | S3 bucket path | Excel file |
| 2 | Excel Read | Load shipment rows into data table | Excel file | tracking_info table |
| 3 | Add Columns | Prepare fields for enrichment | tracking_info | Table with 5 new columns |
| 4 | Browser Login | Authenticate to carrier portal | Stored credentials | Active browser session |
| 5 | For Each Row → UI Agent | Extract live tracking data per shipment | Carrier + Tracking Number | Status, Location, ETA, Risk Level, Last Check Time |
| 6 | Excel Write + S3 Upload | Persist enriched dataset | tracking_info | logistics-master-out.xlsx in S3 |
Under the Hood
The workflow relies on two key capabilities that work together to deliver accurate, structured shipment data: the UI Agent (an AI-powered agent that extracts carrier data) and a risk-classification layer.
UI Agent: Dynamic Carrier Information extraction
This is the core intelligence of the control tower. Rather than hardcoding navigation logic for each carrier, a single AI-powered agent called the UI Agent which adapts dynamically to each carrier's website layout using a natural language instruction built at runtime:
"Click on the tile with name [Carrier] Shipping. Enter tracking number: [Tracking Number]. Click Track. Extract: status, last known location, ETA, risk level, and last check time. Risk level is derived from shipment status." The UI Agent captures output as structured JSON, making it immediately usable for downstream processing and dashboard feeds.
Why this matters: When carrier websites update their layouts, the UI agent adapts automatically without requiring workflow changes. This flexibility is important for a supply chain spanning dozens of carriers.
Risk Level Classification
Understanding risk at a glance saves you from parsing raw carrier statuses across dozens of shipments. The UI Agent infers it from the shipment status.
This embedded logic means you receive an actionable signal rather. For example, a status of "Delayed" or "Customs Hold" maps to High risk, "In Transit" maps to Medium, and "Delivered" maps to Low.
UI Agent node
What You'll Need to Configure
S3 Action Connectors — One S3 connector is required to read and write data to an Amazon S3 bucket. You can follow the instructions in the AWS service action connectors to learn more about how to configure the S3 connector.
Credential Store — You store carrier website credentials in Quick Automate's credential store and reference them by attribute name (username, password). You never hardcode credentials in workflow logic. You can add credentials by going to the Automation Group page and clicking on the Add button as shown below.
Enter Credentials
Edit Credentials
To inject credentials at runtime, use the Enter Credential action. Specify the credential by name and choose whether to fill the username or password field.
Set Credentials
Output Schema — To verify that every carrier lookup returns the same fields regardless of website layout, the UI Agent uses a typed structured output:
{ "latest_status": "string", "last_location": "string", "eta": "string", "risk_level": "string", "last_check_time": "string" } Each field maps to a specific piece of carrier data: latest_status captures the current shipment state, last_location records where it was last scanned, eta shows the estimated arrival, risk_level provides the triaged priority, and last_check_time timestamps the extraction. In the Quick Automate console, click the Define button to create this structured output.
Structured output
Execution Walkthrough
- Quick Automate downloads the input file (logistics-master.xlsx) from S3 and reads all active shipment rows into a data table
- Quick Automate adds five empty columns to the table to hold enriched data
- The UI Agent navigates to the carrier website
- For each shipment row, the UI Agent locates the carrier tile, enters the tracking number, and extracts live status data
- Quick Automate maps extracted fields back to the corresponding row in the data table
- The UI Agent then navigates to the next website and the loop continues for the next shipment
- Once all rows are processed, Quick Automate writes the enriched table to a new Excel file and uploads it to S3 as logistics-master-out.xlsx (the enriched output)
Successful execution of the workflow
Output spreadsheet - logistics-master-out.xlsx
Next Steps
Connect to an operations dashboard — Connect your operations dashboard by feeding the S3 output into Amazon QuickSight to visualize all active shipments, flagged by risk level. For instructions on setting up QuickSight, see the Amazon Quick Sight
Add conditional alerting — Set up conditional alerting by using Quick Automate's conditional actions to trigger a Slack or email notification when a shipment's risk level changes to "High," so you can re-route crews before delays compound.
Expand carrier coverage — Add conditional logic on the Carrier column to support additional portals, building a truly carrier-agnostic control tower.
For complete guidance, see the Amazon Quick Suite User Guide .
Cleanup
To remove all resources: delete the workflow from the Quick Automate canvas, remove the S3 Action Connectors, and delete the carrier credential configurations.
Conclusion
Gain complete visibility into your utility-scale energy logistics. With Quick Automate as your digital control tower, you can navigate carrier websites, extract real-time shipment intelligence, and unify it into a single operational view you can act on before delays become crises.
Get started at the Amazon Quick Automate documentation or join the conversation at the Amazon Quick Suite Community .
Enjoyed reading this content? Let the author know!
Your likes, comments, shares, and saves help creators reach more builders.
Loading recommendations
Loading article