Making a connected fleet legible at a glance.

Turning system state into confidence.

Operations teams rely on connected field machines with strict uptime requirements, yet users lacked clear visibility into machine health, telemetry, and spare-parts readiness. I led the design of a machine-centered experience that unified telemetry, parts, and service context, enabling teams to anticipate issues, reduce downtime, and act before problems escalated.

Machine downtime is operational failure.

For a field operation, a machine that cannot run stops the work entirely. Users needed to answer time-critical questions quickly, but the answers were scattered and hard to interpret.

Constraints

• Safety-critical field operations environment
• Limited ability to expose raw telemetry data
• Hardware-driven system states and dependencies
• Global variability in site workflows
• Mixed audiences, both technical and non-technical users
• Contractual requirements around data accuracy and traceability

Owning UX for machine-focused experiences.

I owned UX across machine-focused experiences, including:

• Machine overview and navigation
• Telemetry visibility and status modeling
• Parts and spares tracking
• Service agreement and maintenance context
• Cross-functional collaboration with Engineering, Service, and Product
• Iterative validation through usability testing

The through line: turning complex system signals into actionable understanding.

What operations teams actually needed.

Key insights

Clustering the real signals.

To move beyond surface-level usability issues, I synthesized qualitative interview data and contextual observations into patterns that explained why users hesitated, over-ordered, or escalated prematurely. The goal was not to catalog pain points, but to identify the signals users relied on, or lacked, when making time-sensitive decisions.

Raw user statements captured during moderated usability sessions and interviews, clustered across operations managers, field techs, dispatch, and suppliers. Confusion stemmed less from missing data and more from unclear severity, timing, and context.

From user signals to operational design decisions.

To ensure the telemetry and parts experiences addressed real operational risk, not just system completeness, I mapped recurring user signals to the downstream behaviors and consequences they triggered. This framework helped prioritize which signals required clarity, guidance, or escalation versus those that could remain informational.

Decision framework: common user signals mapped to operational risk and corresponding design responses to reduce downtime, alert fatigue, and unnecessary dispatch escalation across machine and parts workflows.

Telemetry as signals, not data.

Principles

Below are the interface decisions that addressed these core operational needs.

Clarity for a complex machine.

Measured in downtime avoided, not speed.

Because this work focused on preventative visibility rather than workflow efficiency, results were measured in reduced downtime and avoided escalations rather than raw speed.

• Unplanned machine downtime decreased by approximately 12 to 18% through earlier visibility into machine health and spare-parts risk.
• Emergency parts orders and rush shipments declined by approximately 14 to 22%, as users could anticipate stock needs before hitting critical thresholds.
• Successful early identification of potential system issues increased by approximately 25 to 30%, driven by clearer prioritization of telemetry signals.
• User confidence in understanding machine health improved by approximately 18 to 22% in usability testing and follow-up surveys.
• Dispatches triggered by uncertainty or misinterpretation of telemetry decreased by approximately 10 to 15% following rollout.
• Proactive alerting and parts forecasting cut unplanned intervention costs by roughly 12 to 18% as operators spent less time in reactive troubleshooting.

Representative, anonymized outcomes based on usability testing, platform usage trends, and internal service data.

Reliability is non-negotiable.

In field operations, a machine that cannot be trusted stops real work. By translating complex telemetry and parts data into clear, preventative signals, this work helped teams maintain operational continuity, reduce stress, and operate more predictably, without exposing sensitive system internals or compromising contractual requirements.

From reactive to predictive.

• Predictive failure indicators based on telemetry trends
• Cross-machine health dashboards
• Automated parts-order suggestions tied to run volume
• Deeper integration with service scheduling
• Validating predictive indicators against real usage data to further reduce reactive workloads