Supply Chain Intelligence

Deposco's platform sits at the center of its customers' operations — orders, inventory, shipping, and labor all flow through it. That depth produces an enormous amount of data, but data alone doesn't move a business forward. Customers needed a way to see what was happening across their supply chain and, more importantly, to know what to do about it.

Supply Chain Intelligence was the answer: a command center that turns operational signals into action. I joined as the lead product designer responsible for the screens behind the offering — defining how complex supply-chain data becomes something an executive or operator can act on in seconds.

As lead product designer, I owned the end-to-end design of the Shipping, Inventory, and Labor Intelligence experiences — from first research through developer handoff. I also designed Deposco's Executive Intelligence mobile app, the executive-facing companion to this offering, which I cover in depth in its own case study.

Across all of it, my goal was consistency: a single visual and interaction language, so that learning one Intelligence module means knowing how to use them all.

I started wide. Working with product and engineering, I ran whiteboarding sessions to map the problem space, then built journey maps for the distinct roles this serves — executives, operations managers, and the analysts living in the data day to day. I grounded every direction in both sides of the evidence: qualitative insight from UX research with real users, and the quantitative signals already flowing through the platform. Understanding that operational context — and where decisions were getting stuck — shaped everything that followed.

From there I moved fast and iteratively — sketching concepts, building wireframes, mapping user flows, and pressure-testing every round against real user needs. By the time a screen reached high fidelity it had already earned its layout, and I carried that fidelity into a close partnership with engineering through handoff.

The most important decision wasn't any single screen — it was the framework underneath them. I designed the Intelligence UI as a scalable system: a consistent, accessible structure for navigation, data visualization, and the all-important “what to do next” — built from shared, WCAG-minded patterns that any new module can adopt out of the box.

That consistency is exactly what the three modules below show: Shipping, Inventory, and Labor are different data, but the same language. It's also what lets the offering grow — today the framework powers three Intelligence categories plus a mobile app, each a deep five-to-eight-page experience rather than a single screen, and it's the foundation for six more categories on the way.

Here's the balance I had to strike. Supply Chain Intelligence had to feel like one product — a single, coherent system a customer could learn once and trust everywhere. That's the systems-thinking half: shared navigation, a shared visual language, and shared patterns for turning data into a decision.

But Shipping, Inventory, and Labor are not the same problem, and they're not used by the same person. So I treated each as its own project — its own user, its own research, its own hardest question — and let the framework be the thing that quietly held them together. Three deep investigations; one cohesive experience.

For shipping and logistics managers, cost is a black box — money disappears into carriers, zones, and surcharges with little visibility into why. I researched how these teams actually make shipping decisions, then designed views that turn that spend into clear, comparable signals: cost per order over time, the most expensive ship-to states, and how a customer's own contracts stack up against the wider network.

Inventory managers — and the finance partners beside them — care less about counts than about capital: what's trapped, what's at risk, and what's quietly unprofitable. Researching that mindset shifted the design from “how much do we have” to “what is it costing us”: true revenue and stock health at a glance, and SKU-level rationalization that separates real profit from false positives.

Warehouse operations managers live shift to shift, so Labor Intelligence had to be immediate. Separate research with that audience focused the design on real-time cost-to-serve and throughput against benchmarks — putting a dollar value on an efficiency gap and breaking performance down to the individual, so a manager can act before the shift is over instead of reading about it the next day.

“Opportunities” is a newer feature within the offering that surfaces the highest-value actions a customer should take next. For this one I leaned heavily on AI as a design partner — using Figma Make, Figma MCP, and Claude Code to explore multiple directions at once, see crucial interactive states in action, and refine them on the fly, standing up ultra-high-fidelity, interactive prototypes far faster than traditional methods allow.

That speed came with tradeoffs I had to manage deliberately. Left unchecked, the tools would produce plausible-but-wrong data, off-system components, and inconsistent patterns — so I treated every output as a draft, not an answer. I kept a designer in the loop to vet each screen against real data shapes, our existing component library, and accessibility standards before it reached a user. The skill wasn't prompting; it was knowing where AI accelerated the work and where it still needed human judgment to stay trustworthy.

The payoff showed up in research. Because I could put a near-real, interactive experience in front of users — and run usability testing before a developer wrote a single line of code — the feedback was concrete: users responded most to the plain-language insights, and more than one said it was the first time the data told them what to do, not just what happened. Testing also surfaced a clear ask to see the “why” behind each recommendation, which directly shaped how much supporting evidence each Opportunity now reveals on demand.

That sequence is the real win: we entered development with real user data and sentiment behind a validated direction, instead of paying to build something we'd later rip out and replace.

I partnered closely with engineering through handoff and build, staying in the loop to protect the experience as it became real. But launch was the starting line, not the finish. I continue to add pages for specific user needs and refine the experience as I watch real customers use it — research-driven iteration on a product that's genuinely alive.

Supply Chain Intelligence became a flagship part of Deposco's offering and an active driver in winning new deals and expansions today — a measurable contributor to expansion and new-deal revenue, and counting. Just as importantly, the framework means that value compounds: every new module and subpage ships faster because the foundation is already there.

The response from customers is the part I care about most: they tell us they feel far more empowered to make decisions, and that they finally trust what their data is telling them.

It's the work I'm most proud of — not because it's finished, but because it isn't. It's a living product I get to keep sharpening with every round of research.