OBS Motorpool — Olympic‑Scale Dispatch, Uber‑Style

From “Where’s my driver?” to “It’s here.” We turned Olympic‑scale chaos into Uber‑style calm with a two‑sided Motorpool that moves crews and gear on time every time.

I Designed a two-sided app and admin system to transport press crews and equipment between Olympic venues, borrowing familiar ride-share patterns to reliably dispatch time-critical operations without causing operational chaos.

We reframed the Motorpool as an Uber-style service and delivered a dual-sided, lean design system, that transformed ad hoc dispatch into predictable, map-precise operations under pressure.

Role

UX & Product Design Consultant @ Cloud District

Client / Market

Olympic Broadcasting Services (International Olympic Committee), ERP & Operations, Worldwide.

Timeline

2020–2021

Who might find this case interesting?

Teams building internal tools for transport, field‑service, or campus logistics tools who want consumer‑grade clarity for staff while keeping enterprise controls for safety, compliance, and scale.

Product leaders exploring pattern‑borrowing (ride‑hailing mental models) to accelerate adoption, cut training, and de‑risk complex launches without months of change‑management.

Service designers who need a dual‑sided system—simple on the surface, but able to hide map precision, concurrency, and dispatch rules behind a clean app and a decisive admin.

Jump to Project's Media and Screenshots ->

Overview

OBS must shuttle press teams, camera crews, and hardware between venues—fast, predictably, and at scale. The brief sounded simple (“get people and equipment from A→B”), but the stakes demanded ride‑share‑level reliability. I led the product/UX strategy for a User App and an Operations Admin, turning a complex dispatch problem into a service users already understand.

Strategic Discovery Focus

• Pattern match to reduce risk. Benchmarking Uber/Cabify flows (request → match → dispatch → track → complete) gave us trusted mental models and a shared language with stakeholders.
• Service blueprint as the spine. We mapped end‑to‑end jobs across two surfaces: staff requesting rides (now/scheduled, vehicle class, status) and ops assigning/supervising drivers, vehicles, and gear.
• Field notes over assumptions. Lightweight studies around venue constraints and time windows spotlighted where precision (maps, pickup points) and concurrency (overlapping requests) would break first.

Project Constraints

• Time‑critical logistics: event windows and broadcast slots leave no slack.
• Map precision: venue access points and geofences must be unambiguous.
• Concurrency & fairness: parallel requests, queueing, and assignment logic.
• Operational clarity: drivers, vehicles, equipment, requests, and users managed in one cockpit. These constraints shaped the IA, the states, and the guardrails we designed into both surfaces.

Design & Delivery

• Information Architecture & Flows: Clear ownership of every job‑to‑be‑done across app and admin.
• Interaction Patterns: Requests, schedules, confirmations, live status, exceptions.
• Operational Cockpit: Real‑time queue, assign/reassign, resource controls, and visibility.
• Lean Design System: Standardized components for speed and consistency under tight timelines.
• Tooling & Rhythm: Figma for benchmarking and design; flowcharting for processes; GSuite/Slack/Trello for delivery cadence.

NDA note: no screenshots included; artifacts remained internal.

What We Delivered

• Dual‑sided Information Architecture & UX documentation that made the system coherent end to end.
• High‑fidelity wireframes for app and admin, ready for stakeholder sign‑off.
• Operational guidance (benchmarks, field notes, interaction/IA decisions) to de‑risk delivery.

Outcome

• Shared mental model unlocked alignment. Framing as “Uber‑style” made the solution instantly legible to leaders and ops.
• From ad‑hoc to predictable. Centralized dispatch replaced fragmented coordination and clarified who does what, when.
• Design system enabled velocity. Patterns and states reduced debate and sped up implementation.

Learnings & Next Steps

• Start from the end‑problem. When problems rhyme with proven patterns, borrow—then adapt to context.
• Operational UX > Screens. Reliability lives in states, exceptions, and assignment rules.
• Scale with guardrails. Next iterations: SLA‑friendly dispatch rules, bulk scheduling for event peaks, richer exception handling.