Publix Technology · Summer 2025 Internship
Vendor Label Validation
A mobile GS1 barcode scanning workflow that transformed seafood vendor label validation from a slow manual process into a real-time, mobile-first tool.
Mobile Workflow Tool
Built for real warehouse and quality-assurance use on enterprise Android devices.
GS1 Parsing + Validation
Turned complex barcode data into structured fields with real-time feedback and checks.
Operational Impact
Reduced validation time dramatically while lowering manual entry errors and compliance risk.
Overview
What the project does
Vendor Label Validation is a mobile workflow tool built during my internship at Publix Technology to improve how seafood labels are checked before approval. The tool scans GS1 barcodes, parses structured field data, and validates key information in real time so associates can quickly determine whether a label is compliant.
Instead of manually comparing printed label information against internal requirements, users can scan once and immediately see relevant data, validation states, and warnings. The result is a faster, more reliable, and more operationally practical workflow.
Problem
Why the old workflow was inefficient
Before this tool, seafood vendor labels were validated manually. Associates had to compare GTINs, vendor information, weights, pack dates, and other details by hand, often across multiple systems. This process was slow, repetitive, and prone to transcription mistakes.
- Each label could take up to 15 minutes to validate manually
- Manual comparison increased the risk of input errors and missed issues
- Important compliance checks were harder to perform consistently
- The workflow was especially inefficient in fast-paced operational environments
The opportunity was not just scanning a barcode — it was designing a tool that made speed, clarity, and compliance work together in one experience.
Goals
What success looked like
The goal was to automate label validation through barcode scanning in a way that would save time, improve accuracy, and make the experience easier to use on the warehouse floor.
< 20s
Target validation time per label.
Real-Time Checks
Immediate feedback as structured barcode data populated the interface.
Fewer Errors
Reduce manual entry and improve consistency across validations.
The core hypothesis was that automating GS1 parsing and validation could reduce processing time by over 90% while improving trust in the workflow.
Research
What informed the design
I grounded the work in conversations with internal developers and seafood QA stakeholders, research into GS1 barcode requirements, and observation of the workflow context where the tool would actually be used.
- Interviews with internal devs and seafood QA specialists
- Review of GS1 Application Identifier formats and parsing requirements
- Workflow analysis to identify bottlenecks and repeated manual steps
- Consideration of field conditions like gloves, lighting, and quick scanning interactions
A key insight was that the most common label issues involved missing or incorrect data fields, so the interface needed to surface structured feedback immediately, not after the fact.
User Context
Who this was designed for
The primary user was a Publix QA associate responsible for checking vendor labels quickly and accurately before approving seafood loads. This meant the tool had to support fast scanning, immediate interpretation, and confidence in the result without adding unnecessary friction.
“When a seafood shipment arrives, I want to validate vendor labels quickly and accurately so I can approve loads without manual entry errors, ensuring quality and compliance.”
UX Decisions
How the workflow was designed
A major design challenge was making complex parsed data feel easy to understand on a mobile device. The interface needed to prioritize speed and clarity while still surfacing enough detail for confident validation.
Grouped Field Layout
Used a structured mobile form layout so related data could be scanned quickly and read with less effort.
Validation States
Introduced clear visual indicators for valid, warning, and error states.
High Contrast + Large Targets
Designed for floor conditions where speed, visibility, and touch accuracy matter.
Graceful Fallbacks
Supported incomplete or invalid scan data without breaking the user flow.
Scan History
Provided a way to review detailed records and support audit-related use cases.
Mobile Readability
Focused on hierarchy and field grouping so users could interpret results at a glance.
Technical Implementation
How it was built
Core Stack
C#, .NET, Xamarin.Forms, SQL, and Telerik RadDataForm for enterprise mobile UI.
GS1 Parser
Implemented parsing for key GS1 identifiers such as GTIN, lot number, weight, and dates.
Data Binding
Connected parsed values to product fields, validation rules, and user-facing feedback states.
Validation Logic
Added custom checks for required fields, date rules, weight handling, and compliance-related constraints.
A key part of implementation was making parser output meaningful inside the UI. It was not enough to extract values — the tool also needed to convert them into readable, validated field states that users could trust immediately.
- GTIN parsing and product identification
- Lot number and batch tracking support
- Weight conversion and readable display formatting
- Date parsing and freshness-related rules
- Fallback handling for invalid or missing values
Results
Outcome and impact
The final tool reduced validation time from roughly 15 minutes to about 15 seconds per label, while improving reliability and making compliance checks easier to perform consistently.
~60× Faster
Reduced time from ~15 minutes to ~15 seconds per label.
Improved Accuracy
Automated parsing removed many opportunities for manual transcription mistakes.
Mobile-First Workflow
Made validation practical in the operational environment where it actually happens.
Stronger Compliance
Surfaced validation issues earlier so corrections could happen before approval.
Takeaways
What this project taught me
Vendor Label Validation reinforced how powerful UX and engineering can be when they are shaped around a real operational workflow. The strongest outcome came from translating a technical parsing problem into a product that felt faster, clearer, and more trustworthy for the people using it.
- Designing for imperfect, real-world operational data
- Building enterprise tools that balance speed and reliability
- Translating parsing logic into clear user-facing feedback
- Designing mobile workflows for warehouse and QA contexts
- Working with stakeholders to align product behavior with compliance needs