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Rail Planning Optimization Platform

*Confidential Project – Summary Only

Overview

RailMax is Optym’s next-generation rail planning solution, designed to simplify and optimize yard operations and switching activities. It combines AI-powered recommendations with a clean, human-centered interface to reduce manual workload and enable faster, smarter planning decisions.

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The goal: improve visibility, reduce repetitive work, and accelerate operational decision-making in highly complex rail yards.

Problem Space

Rail yard planners face significant challenges in managing real-time train builds and switch moves across highly complex yards. They must juggle:

  • Constantly changing constraints (crew shifts, car orderings, priority jobs).

  • High-pressure decisions with little margin for error.

  • Outdated, cluttered tools that don’t reflect operational realities.

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This leads to:

  • Inefficiencies due to repetitive manual work.

  • Limited visibility across multiple moving parts.

  • Reactive firefighting instead of predictive planning.

  • Communication gaps slowing down coordination.

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Opportunity: A next-gen solution leveraging AI recommendations + human override control to simplify planning, reduce stress, and accelerate operations.

My Role & Collaboration

I was the UI/UX Designer on the project, responsible for:

  • Conducting research with yard planners & supervisors.

  • Translating operational workflows into intuitive digital interactions.

  • Designing AI-assisted features (Copilot, quick suggestions, bulk edits, autofill tools).

  • Creating wireframes, prototypes, and usability tests.

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I collaborated closely with:

  • Rail yard planners (end users) → validating needs & prototypes.

  • Product managers → aligning design decisions with business priorities.

  • AI engineers → embedding explainable AI into workflows.

Research

Methods

  • Stakeholder interviews & workflow shadowing → observed yard planners in live operations.

  • Process mapping → documented bottlenecks and stress points.

  • Comparative analysis → benchmarked legacy rail software and planning tools.

  • Design workshops → explored concepts like “AI Copilot” and bulk editing.

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Findings

  • Planners juggle multiple competing constraints simultaneously.

  • Legacy tools are reactive and cluttered, forcing reliance on manual processes.

  • Users wanted speed + flexibility, not rigid automation.

  • Trust in AI was possible — but only if its logic was transparent and editable.

Synthesis → Insights

We translated findings into personas, workflows, and pain points:

Personas:

  • Yard Planner → focused on daily train builds, car assignments, switch moves.

  • Yard Supervisor → oversees multiple planners, ensures compliance and efficiency.

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Workflow Pain Points:

  • Heavy reliance on manual updates.

  • Poor visibility during disruptions.

  • No safe way to test changes without disrupting operations.

Ideation & Design Process

We explored AI + Human collaboration through iterative design:

  • Sketches & whiteboarding → mapped AI Copilot, bulk edit flows, and sandbox planning.

  • Wireframes → tested streamlined dashboards, drag-and-drop interactions, and conflict highlights.

  • Prototypes → validated AI recommendation flows and quick suggestions with end users.

Prototyping & Testing

Through iterative testing:

  • AI Copilot → refined with rationale text for transparency.

  • Bulk Edit tools → reduced repetitive workload, validated by users.

  • Conflict highlights → color-coded alerts fine-tuned for instant readability.

  • Sandbox mode → introduced a safe space to simulate yard moves before execution.

Final Solution

Core Design Features:

  • AI Copilot → Assisted task execution with transparent recommendations.

  • Quick Suggestions → Context-aware prompts for resolving conflicts.

  • Bulk Edit → Apply changes to multiple cars/trains in one step.

  • Autofill Tools → Reduce manual data entry during train build planning.

  • Scenario Planning → Sandbox mode for safe testing before committing.

  • Visual Dashboards → Clear visibility of crew shifts, car flows, and conflicts.

Impact

  • Reduced manual workload → significant time saved with AI-driven edits.

  • Faster decision-making → planners resolved conflicts quicker with quick suggestions.

  • Improved trust in AI → transparent recommendations encouraged adoption.

  • Positioned Optym for scale → RailMax became the foundation for future yard planning innovations.

Key Takeaways

  • AI must assist, not replace → explainability and user control build trust.

  • Bulk editing & autofill → big wins in repetitive, manual-heavy workflows.

  • Scenario planning → lowers stress by allowing safe experimentation.

  • Designing for critical, time-sensitive operations requires balancing automation with human judgment.

Get to know                  better

RailMax is a confidential Optym project with leading rail operators. What you see here is a high-level overview.

 

I’d love to walk you through the full case study, including design artifacts and prototypes, in a one-on-one conversation.

 

You can call or email me directly, or simply fill out the form below to get in touch.

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