The Manual Load Creation Process (The Reality for Most Teams)

Step 1: The Inbound Order Email

Orders often arrive via email containing:

• • Pickup and delivery locations
    
  • Dates and times
  • Commodity and weight
  • Rates and reference numbers
  • Special instructions

There’s no structured data feed—just text.

A typical inbound load request received via email when EDI or API is not available.

Step 2: A Blank TMS Load Screen

Once the email is received, an operations rep opens the TMS to create a new load. Every field is empty and waiting to be filled.

A standard TMS load creation screen—every field must be manually populated.

Step 3: Manual Data Entry (One Field at a Time)

Now comes the most time-consuming part.

The rep manually:

• • Reads each line of the email
  • Switches between email and TMS
  • Types every data point
  • Double-checks accuracy
  • Saves and publishes the load

This process can take 5–10 minutes per load—and much longer during peak volume.

Manual load creation: copying data from email and typing it into the TMS field by field

The Real Cost of Manual Load Creation

While each load may only take a few minutes, the impact adds up quickly:

• • High labor cost for repetitive work
  • Increased risk of data entry errors
  • Slower turnaround times
  • Limited scalability during volume spikes
  • Operations team burnout

This isn’t a technology failure—it’s a process gap.

Where RPA Fits Perfectly in Logistics Operations

What RPA Does in This Use Case

RPA is designed for structured, repeatable workflows like this one.

In this scenario, RPA:

• • Reads inbound order emails
  • Extracts structured data (addresses, dates, rates, references)
  • Maps each field to the correct TMS input
  • Automatically builds the load in the TMS
  • Applies validation rules before saving

No copy-paste. No retyping. No manual touch.

Why This Is Ideal When EDI or API Isn’t Available

Not every shipper supports EDI. Not every system integrates cleanly.

RPA fills the gap by automating what humans currently do—without requiring system-to-system integration.

Where AI and Humans Still Matter

Not every email is clean. Not every load is straightforward.

This is where AI and trained logistics professionals play a critical role:

• • AI helps classify emails, identify anomalies, or flag missing data
  • Human teams handle exceptions, judgment calls, and edge cases
  • Complex logic and decision-making remain human-led

At Valoroo, automation is not about replacing people—it’s about removing low-value work so teams can focus on what matters.

RPA vs. AI: Understanding the Difference

RPA

• • Automates structured, repetitive tasks
  • Follows defined rules
  • Ideal for data entry, load builds, billing updates

AI

• • Supports decision-based workflows
  • Handles variability and classification
  • Works best alongside human oversight

The most effective logistics automation strategies combine RPA + AI + people.

The Valoroo Approach: Blended Automation That Actually Works

Valoroo delivers automation the way logistics teams actually operate:

• • RPA for structured, high-volume workflows
  • AI to support decision systems and exceptions
  • Logistics-trained offshore teams to manage edge cases and oversight

The result:

• • Faster operations
  • Fewer errors
  • Lower cost per load
  • Scalable support without adding headcount

Conclusion: Start Where the Manual Work Is

Automation doesn’t have to start with massive system integrations.

For many logistics teams, the fastest ROI comes from eliminating manual work that already exists—like building loads from emails when EDI isn’t available.

Related Posts