CN Rail Detector Technology Initiative

Building the foundation for smarter, safer, and more connected rail operations

ROLE

UX Lead

TOOLS

Mural, Figma, MS Teams

Six Months

DURATION

Project Leads, PM, Mechanical Engineers, Data Scientists, Rail Traffic Controllers, Tech Architects

TEAM

Modernizing Rail Through Data

CN Rail set out to overhaul how it tracks the health of its trains. With detectors lining thousands of miles of track, there was massive potential for real-time insight. However, siloed systems, delayed alerts, and inconsistent data matching made it hard to act confidently or quickly.

As the UX Lead, I led discovery—uncovering how data moved (or didn't), where workflows broke down, and what people needed to do their jobs better.

My Focus

Research Planning
Stakeholder Workshops
Journey Mapping
Insight Synthesis
Data Mapping
Early Wireframes

Discovery

Project Objective & Impact

The goal is to integrate data to improve Safety, Predictive Maintenance, and Efficiency, shaping discovery, guiding prioritization, and helping frame a phased roadmap that addresses user needs and business outcomes.

What Was Heard

Over 15 hours of interviews with rail traffic controllers, mechanical supervisors, data scientists, and tech leads revealed common threads, highlighting technical frustrations, operational risks, inefficiencies, and lost time.

Concept

Mapping the Data Journey

To turn insights into strategy, I worked with stakeholders to map the full detector data lifecycle—from AEI scans to field alerts, inspections, repairs, and billing.

Data Journey Map

Six key breakdowns:

1. Train Consist

AEI data wasn’t updated mid-route. Offline syncing caused lag, and load weights couldn’t be tied to assets.

2. Detectors

Axle counts and car-end IDs were often wrong. Some data still came from tape reviews.

3. Alert / Notify

Detector alerts (WHN, HBD) arrived late. Teams had no shared visibility.

4. Analyze / Decide

Matching train/car IDs took 15+ minutes. If it failed, the alert was discarded.

5. Inspect / Repair

Crews didn’t know the problem until the car showed up. Tablets failed in low-connectivity zones.

6. Billing

The SAP and CRB systems were out of sync. The handoff between repair and billing was broken.

Understanding the Data Landscape

We stepped back and asked:

What data do we need? Where does it live? Can we trust it?

We mapped key systems—AEI, WISE, SAP, Railinc—and looked deeper:

  • Which failures matter most?

  • How do seasonality, asset age, or repair history affect risk?

  • Where are data gaps hurting decisions?

We connected data points to decisions, pinpointing where misalignment led to missed opportunities.

In the end, CN Rail didn't need more dashboards; they needed an integrated platform to deliver verified, real-time insights when it mattered.

Refinement

Asking the Right Questions

Turning pain points into “How Might We” prompts to focus our workshops and align everyone around what matters and the right problems to solve.

From Data to Decisions: Real-World Use Cases

We mapped use cases to assist stakeholders in understanding how the system influences daily decision-making to help stakeholders see its shift from reactive to predictive.

Building Contextual Interfaces

We identified shared priorities and translated key concepts into actionable workflows and interfaces from three user perspectives.

Design Principles

Based on these views, establish key design pillars to guide the wireframing process.

Wireframe Highlights

Developed initial workflows to test how this logic would function in real-world scenarios. These wireframes assisted everyone, from rail traffic controllers to architects, in understanding what the system could achieve.

System-Level Alerts

Track alerts by location, severity, and team ownership

Yard Coordination

View staffing needs, part inventory, and open work orders

Train & Car Drilldowns

See alert history, recommended actions, and confidence scores

Predictive Insights

Forecast component risks before they escalate

Solution

A Roadmap to Make It Real

Once the vision was clear, we delivered a four-phase implementation roadmap to guide technical and design planning.

The Four Phases

Phase 1: Data Exploration

Root-cause analysis of matching issues, feasibility testing for predictive models, and early architecture planning.

Phase 2: Preventive Implementation

Data repository build-out, UI development, and integration fixes—running alongside legacy systems.

Phase 3: Predictive Intelligence

Model development for failure prediction, alert scoring, and detector health tracking.

Phase 4: Stabilization & Scalability

Device testing, Railinc integration, and long-term roadmap planning.

What We Delivered

  • A user-centered system strategy bridging operations, data science, and engineering

  • Lo-fi prototypes that aligned teams and informed testing

  • Clear data architecture recommendations

  • A phased roadmap for scalable implementation

Final Takeaway

This project wasn't just about designing dashboards; it was about helping teams use data more effectively to bridge gaps between people, tools, and decisions.

Focusing on real workflows and surfacing timely information, we created a foundation for predictive maintenance that aligns engineering, operations, and strategy, leading to smarter rail operations.

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