Designing for Movement: The Intelligent Future of Digital Space Orchestration

Overview

In 2021, Verizon Location Technology (VLT) expanded its location analytics capabilities through the acquisition of Senion, a leader in indoor positioning and geofencing. This partnership enabled us to create Digital Space Orchestration (DSO), a solution leveraging machine-learning sensor fusion and sub-meter location accuracy. One of its most impactful applications was in warehouse optimization, where motion analytics could enhance asset tracking, streamline workflows, and integrate autonomous robotics.

My team was tasked with designing the interface for Senion Location Portal, a SaaS platform generating vast amounts of motion data. Our challenge was twofold: first, to determine the most valuable insights for executives, directors, and floor managers; and second, to design a system that presented these insights in a clear, actionable manner.

Research & Data Collection

We conducted four rounds of contextual research at a 520,000+ square-foot warehouse designated as our proof-of-concept site. To gain a deep understanding of operations, we shadowed workers fulfilling picklists and managers overseeing logistics. Through this immersion, we identified key areas where motion analytics could drive efficiency:

• Worker & Equipment Movement: Identifying bottlenecks and inefficiencies in worker and forklift routes.

• Asset Utilization: Optimizing deployment of high-value equipment and minimizing dwell time.

• Environmental Context: Refining warehouse layouts to improve throughput.

• Safety Insights: Analyzing high-risk zones to enhance worker safety.

These insights guided our technology stack decisions, ensuring we captured the right data to generate meaningful analytics.

Designing for Meaningful Insights

To test and refine our approach, we converted a 20,000-square-foot section of the warehouse into a living lab. This controlled environment allowed us to experiment with different tracking technologies, including millimeter-wave Wi-Fi beacons and AI-powered cameras. One early hypothesis was that LiDAR-generated digital twins could provide an intuitive spatial representation of warehouse operations. However, through testing, we discovered that the sheer volume of real-time data required made LiDAR impractical for live decision-making. This pivot allowed us to focus on more scalable, real-time solutions.

Even without LiDAR, the challenge remained: how do we make vast amounts of motion data useful? Our research revealed that different user groups had distinct needs:

• Executives needed high-level operational overviews.

• Managers required the ability to drill into granular trends.

• Floor supervisors benefited from real-time alerts and historical comparisons.

To bridge these needs, we developed a definition bar component—a persistent, customizable tool that surfaced relevant insights for each user type. This allowed for at-a-glance monitoring while enabling deeper analysis when needed.

Pivoting from Reports to Visualizations

An early request from stakeholders was to mirror Excel-style reports, reflecting their existing decision-making processes. However, through observation, we saw that shift managers spent significant time interpreting these reports manually—an opportunity for optimization. Rather than replicate static tables, we explored interactive visualizations that could make insights more immediate and actionable.

We presented design comps showcasing alternative visual formats, ranging from heatmaps to trend graphs. Initial feedback was cautious, as users were accustomed to traditional reports. However, interactive prototypes changed the conversation—once users experienced dynamic filtering and drill-down capabilities, enthusiasm grew. One manager noted that the ability to pinpoint inefficiencies visually saved them hours of manual data crunching each week.

This shift validated our approach, reinforcing the idea that users don’t always know what they need upfront. By focusing on solving pain points rather than replicating existing tools, we created a platform that fundamentally improved decision-making.

Takeaways & Lessons Learned

Design for the need, not just the request.

A common aphorism in UX is, "Had Henry Ford asked customers what they wanted, they would have said 'faster horses.'” This project exemplified that principle: stakeholders initially requested Excel-like reports, but our research showed they needed a way to understand data faster. By pushing beyond surface requests and iterating through prototyping, we delivered a data visualization platform that fundamentally changed how warehouse operators interacted with motion analytics.

Additionally, this experience reinforced:

• The importance of real-world testing—our living lab allowed us to test ideas before full deployment.

• The value of iterative prototyping—interactive demos helped shift stakeholder mindsets.

• The necessity of adaptability—abandoning LiDAR allowed us to focus on more scalable solutions.

Through thoughtful research, iterative design, and strategic pivots, we transformed a complex dataset into a powerful decision-making tool, proving that great UX isn’t just about what users say they want, but about uncovering what they truly need.